An Econometric Analysis of Regional Convergence and Market Integration in the Mexican Power

Industry  

Un Análisis Econométrico de Convergencia Regional e Integración en el Sector Eléctrico Mexicano

 

Dr. Alejandro Díaz-Bautista

Economics Seminar, June 2005 Professor of Economics and Researcher at the Department of Economic Studies, and Coordinator of the Master’s Program in Applied Economics at COLEF. Mexico Address: Blvd. Abelardo L. Rodríguez 2925, Zona del Río, BC, 22320, México. U.S. Address : P.O. Box “L”, Chula Vista, CA, 91912-1257, USA. Email: adiazbau@gmail.com adiazbau@yahoo.com

Dr. Díaz-Bautista received his Ph.D. in Economics from the University of California Irvine (UCI). He also earned his master's degree in economics at UCI. He was also educated at UCSD and ITAM in Mexico City where he earned his Bachelor’s degree in Economics. His career has involved academics, government service and consulting for private firms.

Introduction• This paper examines the law of one price in the

Mexican power market before and after its partial deregulation in 1993. The law of one price states that once the price between two goods is standardized, the good should sell at the same price across regions. The author examines tendencies toward equilibrium prices by examining prices between different regions in Mexico, between different sectors in Mexico, and between the United States and Mexico.

Introduction

• The results in the paper suggests that the failure of the law of one price in 73% of the Mexican Regional Power Markets in 1993 was due to arbitrage from the producer side. In addition, the author claims the law of one price doesn’t hold in the residential sector between the United States and Mexico between 1973 and 1998. The author shows how the deregulatory scheme, network integration and more flexible energy laws promotes price convergence over the different large network regions which had formerly been spatially separated. The empirical exercise of nodal power prices between regions in Mexico in 1998 shows that the regional power markets became more integrated after the partial economic reform in the power sector.

In 1960, the Mexican electricity industry was nationalized. In that year, only 44 percent of the population in Mexico had access to electricity. Since then, investments were made in order to provide electricity to more than 95 percent of the population.

During this period, the organization of the industry under public ownership and administration was appropriate for the integration of the industry and the expansion of the grid, due to the scale of the investments and the available technology. However, in recent years, the economic performance of the industry has been deteriorated. According to government estimations, in the next six years, the demand for energy will grow at a rate no less than 6 percent per year. The dependency of the funds to a limited public budget makes total public financing almost implausible, taking into account the urgent needs in other areas of the economy.

The Energy Ministry under the Salinas administration developed a visionary ten year plan for supplying Mexico's growing electric power needs. The idea was to invite private capital, national and foreign, to invest in electric power facilities.

Mexico changed its basic law on electricity during 1993 to permit the participation of the private sector in power generation. New regulations were issued in 1993 and modified in 1994. Private investors could construct, own, and operate generation facilities for the purposes of self supply, cogeneration , independent power generation, and small

production, up to 30 MW of capacity.

The reform of the Public Service of Electric Energy, expanded and defined the participation of private entities in the activities of electric energy generation, importing and exporting. In article 3, the law defines five activities that are not considered public service such as the generation of electric energy for self-supply purposes.

The activities include cogeneration or small production; generation of electric energy performed by the IPP's for sale to CFE; generation of electric energy to be exported, if such energy is the result of cogeneration, independent and small production, importing of electric energy for own uses, and generation of electric energy to be used in emergencies due to

interruptions in public service.

In February 1999, the Mexican government announced the "Structural Change of the Electricity Industry in Mexico" (Secretaría de Energía, 1999). With this reform, the government shifts its view of the industry from public ownership to one in which private investment is fundamental and the authorities serve only as regulators of the industry. It opens to competition the generation and supply of electricity, and maintains both the transmission and distribution sectors as regulated monopolies.

During the Fox administration a second structural reform was announced in the power sector. It established an open market for electricity in which the market price is determined by selecting, from the available generators, at the lowest cost until expected demand is met.

The following figure shows the structure of the power industry in Mexico from 1960 to

2000.

Structure of the Power Industry. (1960-2000)

G en era tio n

N a tio n a ltra n sm iss io n

gridD istr ibu to rs U sers

9 0 % 9 8 .0 % 9 0 .4 %

C F E

2 .3 % 2 .0 % 9 .6 %

LF C

Pem ex

4 .4 %

3 .3 %

P r iv a te secto r

Evolution of Real Power Prices in M exico 1996-1999 at the National Level

Real Electricity Prices

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Domestic Agricultural Total Industrial

Regional Competition and the Law of one Price in the Power Sector  The law of one price states that a good must sell for the same price in all regions. This law applies in the regional and international markets and is a common sense notion. If the law were not true, unexploited profit opportunities would exist, allowing someone to earn riskless profits by purchasing low in one region and selling high in another.  In the study, a comparison is made for the equilibrium price tendencies in regional markets. In an arbitrage free economy with no transactions costs, any given time state claim will sell for the same price, no matter how obtained. This will also be true for any package of time-state claims in power. This property is known as the law of one price.

The law of one price implies that every commodity should have the same price worldwide when measured in the same currency. Rogoff's (1996) survey is the best single review and summary of research on law of one price. In a path breaking paper in the energy literature, De Vany and Walls (1993) argued that the recently developed cointegration techniques were the natural way to evaluate competition between the natural gas spot markets at dispersed points in the national transmission network. They found that more than 65% of the natural gas markets had become cointegrated in the U.S. The increased cointegration of prices showed evidence that open access had made gas markets more competitive.

De Vany and Walls (1999) developed a cointegration analysis of spot electricity prices and gave some insights on transmission efficiency in the western United States grid. I follow the work by De Vany and Walls (1999), and apply cointegration analysis for electricity prices in Mexican nodal regions.

Interaction between Macro and Micro Theory and Macro-Micro econometrics

• The Emergence of new statistical techniques for empirical analysis supports the evolution of economic theories and vice versa.

• Granger and Engle (Nobel Prize, 2003): Deepened our understanding of two central properties of many economic time series:– Nonstationarity: no clear tendency to

return to a constant value or a linear trend.

– Time-varying volatility.

Some Key Econometric Concepts

• Stationarity and non-stationarity.

• Unit roots.

• Dickey fuller test.

• Testing for cointegration.

Econometric Analysis

• Instable macro variables invalid statistical inference failed policy evaluation

• How to test for non-stationarity?• Use Unit Root tests

– Original idea: Paper by Dickey and Fuller (1976), if I(1) holds, AR(1) coeff is not traditionally distributed

– Refinements: ADF test; Phillips-Perron test (1988)– Hard to get consistent estimates of coeffs keep

challenging researchers to study it

What is Stationarity?

A stochastic process is said to be stationary if its mean and variance are constant over time and the value of the covariance between the two time periods depends only on the distance or gap between the two time periods and not the actual time at which the covariance is computed.

Stationarity

Stationarity may be strong or weak (covariance)

E(yt) is independent of time; Var(yt) is a finite, positive constant and independent

of time; Cov(yt, yk) is a finite function of t-k, but not of t or k

The whole distribution of the variable does not depend on time

       Autoregressive processes:

For an AR(1) process written as yt = yt-1 + t

we have autocorrelation coefficients k = k

Implications:1.   A shock in an AR(1) process affects all

future observations but with a decreasing effect

The condition for yt to be stationary is that ||<1 .

Non Stationary Data

Non stationary process is characterised by the presence of a unit root, a deterministic trend or both An AR(1) process yt = yt-1 + t is

Stationary if || < 1 Contains a unit root if || = 1 (ie. a random walk) Explosive if ||> 1

Implications of unit roots:

The shock has a persistent effect that lasts forever Ever increasing variance is added to the series

thereby violating stationarity

T estin g for U n it R oots

G raph ical A na lysis F orm al testing D ickey F ulle r (D F ) and

A ugm en ted D F (A D F ) tests In an A R (1 ) m odel app ly the D F test:

D F test reg ressing on one lag o f the se ries

H o : = 1 (un it roo t)

S tandard t s ta tistic is g iven by )ˆ(

1ˆˆ

se

B ut canno t u se the c ritica l values from the standard t tab les as y ou m ay re jec t a un it roo t too o ften

An Ar(1) process containing a unit root is written as

yt = yt-1 + t

Taking the first difference, it may be rewritten as

yt - yt-1 = t or yt = t

where t ~ IID(0,2)

yt is stationary (integrated of order one, I(1))

Unit root difference once if the series becomes stationary it is said to be I(1)

If the series does not become stationary it contains a second unit root difference it a second time if it becomes stationary now it is said to be I(2)

Gujarati mentions some Properties of Integrated Series

• 1. Linear Combinations of stationary and nonstationary series are nonstationary.

• 2. A linear combination of an I(d) series is also I(d).

• 3. A linear combination of an I(d1) and an I(d2) series is I (d2) where d1<d2.

• 4.A linear combination of two I(d) series is integrated of order d* where d* is less than or equal to d.

Models with non-stationary variables

Non-stationary processes may be characterised by the presence of a unit root (ie. A stochastic trend)

The solution is to difference the series If it becomes stationary, the process is referred

to asdifference stationary.

CointegrationIntuition:

While Yt and Xt are individually I(1), there may exist a linear combination of the series which is stationary

ie. the difference Yt - Xt might be stable

around a fixed mean

Implications: Cointegration the series are drifting upwards together at roughly the same rate

there is a long-run equilibrium relationship between Yt and Xt

Test for Cointegration

• Test both series to see if I(1)• Run OLS on the two variables with no lags

– If cointegrating relationship exists (Z) then OLS will find it

– If it doesn’t exist then “spurious regression”

• To see which it is, test Z to see if I(1)– Z will be the residual from the OLS regression

– use DF or ADF test

– critical values are different from standard DF!

– if Z is I(1) then variable are not cointegrated

Cointegration Test

• The Basic Cointegration Tests approach suffers from two main weaknesses:– it assumes there is only one cointegrating

relationship between variables• this is possibly incorrect in large regional systems

– it assumes one variable can be treated as the dependent variable, the others as exogenous.

• The Vector Error Correction Model (VECM) representation of the VAR is the following:

  • The test for cointegration between the y’s is calculated

by looking at the rank of the matrix .

• The rank of a matrix is equal to the number of its eigenvalues that are different from zero.

• The eigenvalues are denoted by i

tktktttt uyyyyy )1(122111 ...

Cointegration

• If the variables are not cointegrated, the rank of will not be significantly different from zero, – so i = 0 i.

– tests that follow will use ln(1-i)

– but if i = 0, ln(1-i) = 0 so essentially the same

Johansen’s Cointegration Tests

• The test statistics for cointegration are formulated as  

trace tests the null that the number of cointegrating vectors is less than or equal to r against an unspecified alternative. trace = 0 when all the i = 0, so it is a joint test.

max tests the null that the number of cointegrating vectors is r against an alternative of r+1.

g

riitrace Tr

1

)ˆ1ln()(

max ( , ) ln( )r r T r 1 1 1

Johansen’s Cointegration Tests

is defined as the product of two matrices:

= contains the cointegrating vectors gives the “loadings” of each vector in each equation.• For example, if g=4 and r=1, and will be 41, and

yt-k will be given by:

ktyyyy

414313212111

14

13

12

11

Johansen’s Cointegration Tests

• Critical values for the two test statistics are non-standard and depend on:1. the value of g-r, the number of non-stationary components2. whether a constant and /or trend are included in the

regressions.

•  If the test statistic is greater than the critical value, reject the null hypothesis that there are r cointegrating vectors in favour of the alternative that there are more than r.

• Beware, the two tests can suggest conflicting inference

Advantages of Johansen

• Simple cointegration tests did not allow us to do hypothesis tests on the cointegrating relationship itself, but the Johansen approach does.

• If there exist r cointegrating vectors, only these linear combinations will be stationary.

• You can test a hypothesis about one or more coefficients in the cointegrating relationship by viewing the hypothesis as a restriction on the matrix.

Empirical Methodology and the Economic Model of the Power Sector  This paper employs a time series cointegration method to study the validity of the law of one price. Linear cointegration techniques, such as unit root tests, examine the properties of power data by assuming the variables behave the same regardless of their proximity to the hypothesized equilibrium condition. I test the law of one price by examining the power prices between different regions of Mexico, between sectors of the economy in Mexico and finally between the US and Mexico. I anticipate the differential to be small, after the passage of the law of 1993 and behaving in a random, non deterministic way. When the deviations are large, arbitrage and substitution could restore the approximate equilibrium price. 

Price and Marginal Cost in the Power Sector

100MW

100MW

100MW

100MW

100MW

100MW

100MW

100MW

100MW

100MW

100MW

100MW

100MW

100MW

100MW

Cantidad (MW)

Costos de plantas públicas

Precio actual (PA)

Demanda

Precio ($/MW)

Generadoresdespachados

P

Costos marginales auditados de centrales de generación

CURVA DE DEMANDA ELÉCTRICA

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50

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1 3 5 7 9 11 13 15 17 19 21 23

Horas día

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CARGA INSTALADA DEMANDA

MÁXIMA

Curva de demanda

First I test hourly and daily nodal marginal costs by months for 25 regions in Mexico between 1992 and 1993 and between 1997 and 1998. The first sample is taken from Mexico's CFE statistics. I also test annual price data for the U.S. and Mexico from 1973 to 1999. The sample is residential, commercial, industrial and agricultural power prices from 1973 to 1999 in Mexico and the U.S. .The Ministry of Energy, INEGI, Bank of Mexico, Comision Federal de Electricidad and Ministry of Finance supplied the Mexican data, and the U.S. data comes from the Energy Information Administration and the U.S. Department of Energy. The econometric methodology to test for cointegration across nodal power regions is the likelihood ratio cointegration test used by Johansen (1991).

Empirical Results in the Regional Power Model Given a group of non-stationary power price series, I am interested in determining whether the series are cointegrated, and if they are, in identifying the cointegrating (long-run equilibrium) relationships. To test the results of pairwise series cointegration, I used the Johansen Test. The accurate question is not the effect of deregulation, but was the deregulation of the power sector real? Tests were also performed for a unit root in the level, first difference, or second difference of the series. If the test failed to reject the test in levels but rejected the test in first differences, then the series contains one unit root integrated of order one I(1).

Unit root tests like the Dickey Fuller (DF), Augmented Dickey-Fuller (ADF) test and the Phillips-Perron (PP) test were also performed.

The next tables show the likelihood ratio cointegration test results across nodal power regions in Mexico for 1993 and 1998. The null hypothesis of a unit root is rejected against the one-sided alternative if the t statistic is less than (lies to the left of) the critical value. For example, the test fails to reject the null hypothesis of a unit root in the Central series at any of the reported significance levels. The unit root hypothesis is not rejected even at the 10 percent level for the central Power price

series.

Table 1. Likelihood Ratio1 Cointegration Test Results Across Nodal Power Regions in Mexico( 1993) *(**) denotes rejection of the hypothesis of 1 cointegrating equation at 5%(1%) significance level. If not rejected the Likelihood ratio test indicates 1 cointegrating equation at 5% (1%) significance level. The finding of at least one cointegrating relation is in accord with the theory.2

Baja Baja Sur Balsas Bravo Campeche Central Chetumal Chihua

Acapulco 3.54*

6.50* 2.63* 2.78* 6.86 2.59* 7.32 3.66*

Baja - 1.39* 1.94 1.33* 1.40* 1.37* 1.36* 1.37* Baja Sur - - 4.24* 2.70* 1.20* 4.52* 5.43* 2.70* Balsas - - - 2.91* 5.16 4.54* 5.21 3.10* Bravo - - - - 2.69* 2.88* 2.68* 2.77 Campeche

- - - 5.10 0.75* 3.73

Central - - - 5.16 2.91* Chetumal - 3.84*

1 Johansen (1991) showed that the likelihood ratio statistic has an asymptotic 2 distribution. 2 Ho: 1 Cointegrating equation. The alternative would be a nominal change or no change, after the start of the deregulation process.

Table 1. Continue

Coahuila Colima Huasteca Juarez Laguna Mazatlan Mazat201 Monter

Acapulco 2.77* 2.60* 3.00 3.64* 2.72* 1.26* 5.21 2.77* Baja 1.33* 1.37* 1.90 1.37* 1.33* 1.88* 2.87 1.33* Baja Sur 2.70* 3.93* 4.13* 2.67 2.65* 0.07 5.97 2.70* Balsas 2.90* 2.28* 3.43* 3.08* 2.87* 1.55* 5.69 2.90* Bravo 3.70* 2.97* 2.94 2.76* 2.50* 0.25* 2.96* 3.36* Campeche

2.68* 4.96* 4.32* 3.70 2.64* 1.09 5.91 2.68*

Central 2.87* 3.99* 3.65 2.89* 2.84* 1.64* 6.31 2.87* Chetumal 2.64* 4.73 4.73 4.26* 2.62* 0.99 5.96 2.67* Chihuahua

2.77* 3.16* 2.60* 2.25* 2.71 1.45* 4.10* 2.77

Coahuila - 2.97* 2.93* 2.77 2.73* 0.27* 2.96* 3.21* Colima - 3.30* 3.14* 2.93* 1.42* 5.44 2.96* Huasteca - 2.59* 2.90* 2.06* 6.23 2.93* Juárez - 2.70* 1.45* 4.09* 2.77* Laguna - 0.34* 2.94* 2.70* Mazatlan 101

- 1.98* 0.27*

Mazatlan 201

- 2.96*

Table 1. Continue

Occid Oriente Petacalc Sinaloa Sonora Sur Tepic Veracruz Yucatan

Acapulco 2.74*

2.34* 2.66* 1.60* 1.23* 5.29*

2.65*

2.98* 7.34

Baja 1.68 1.85 1.97 1.86* 1.88* 1.58*

1.68 1.62 1.67

Baja Sur 3.95*

4.68 4.21* 0.10 0.02 17.25

3.97 5.44 5.45

Balsas 2.36*

2.53* 3.45* 1.92* 1.61* 2.74*

2.01*

2.25* 5.21

Bravo 2.98*

2.76* 2.91* 0.58* 0.36* 2.75*

2.98*

2.78* 2.68*

Campeche

5.16 5.27 5.07 1.24 0.73 0.86 5.12*

6.35* 0.69*

Central 3.98*

6.43 4.25* 1.97* 1.68* 4.76 3.92*

4.79* 5.16*

Chetumal 4.85*

5.33 5.13 1.14 0.64 0.77 4.83 6.41 2.23*

Chihua. 3.23*

2.87* 3.11* 1.96* 1.50* 3.31*

3.20*

3.10* 3.84*

Table 2. Likelihood Ratio Cointegration Test Results Across Nodal Power Regions in Mexico( 1998) *(**) denotes rejection of the rejection of the hypothesis of 1 cointegrating equation at 5%(1%) significance level. If not rejected the Likelihood ratio test indicates 1 cointegrating equation at 5% (1%) significance level. The finding of at least one cointegrating relation is in accord with the theory.

Baja Baja Sur

Balsas Bravo Campeche Central Chetumal Chihuahua

Acapulco 3.54*

3.71*

18.94 3.064 4.488 2.37 4.53 1.22

Baja - 3.25 *

2.53* 1.76* 2.93* 2.54* 2.90* 4.1*

Baja Sur - - 2.64* 2.78* 2.72* 2.67* 2.76* 1.77 Balsas - - - 3.03 4.44 4.78 4.49 1.20* Bravo - - - - 2.74 3.09 2.79 1.20* Campeche - - - 4.43 3.00 1.69* Central - - - 4.16 2.82 Chetumal - 3.34

Table 2. Continue

Coahuila Colima Huasteca Juarez Laguna Mazatlan 101

Mazatlan 201

Monterrey

Acapulco 3.04 4.86 6.72 1.23 1.43 1.55 1.43 3.04 Baja 1.77* 2.53* 2.53* 4.06* 2.21* 3.38 2.47* 1.78* Baja Sur 1.53* 2.61* 2.65* 1.77 1.86* 2.68 2.00* 1.54* Balsas 3.01 4.48 3.10* 1.20* 1.43 1.48 1.43 3.01 Bravo 2.49 2.96 2.98 1.21* 2.15 1.49 2.00 2.08 Campeche 2.74 4.46 4.48 1.68* 1.59* 3.29 1.62* 2.74 Central 2.81 3.49 3.23 2.87 2.82 1.60 6.31 2.83 Chetumal 2.61 4.71 4.70 4.24 2.61 0.91 5.90 2.61 Chihuahua 2.71* 3.15* 2.61 2.28 2.73 1.43 4.11 2.71 Coahuila - 2.91 2.95 2.76 2.79 0.21 2.95 3.25 Colima - 3.32* 1.18* 1.43 1.44 1.43 2.95 Huasteca - 1.21* 1.42 1.53 1.43 2.96 Juarez - 2.49* 1.68 2.51* 1.24* Laguna - 1.21 1.43 2.20 Mazatlan - 1.18 1.54 Mazatlan201

- 2.05

Table 2. Continue

Occidente

Oriente Petacalco Sinaloa Sonora Sur Tepic Veracruz

Yucatan

Acapulco 4.93 2.62 4.83 1.55 1.56 2.75 5.23*

2.16 4.51

Baja 2.53*

2.54* 2.53* 3.38 3.39 2.55*

2.53*

2.55* 2.91*

Baja Sur 2.61*

2.67* 2.64* 2.68 2.67 2.68*

2.59*

2.68* 2.74*

Balsas 4.72 4.69 4.90 1.48 1.49 4.84*

4.93*

5.21 4.46

Bravo 2.96 3.09 3.02 1.49 1.50 3.11 2.98 3.11 2.77 Campeche 4.46 4.41 4.45 3.29 3.29 4.46 4.40 4.42 2.28* Central 3.91 6.34 4.13 1.98 1.73 4.34 3.90

* 4.54* 5.34*

Chetumal 4.34 5.12 5.34 1.34 0.23 1.45 4.67 6.35 2.67 Chihuahua 3.56 2.58 3.63* 2.34 1.59 3.34 3.25 3.45 3.45 Coahuila 2.91 2.78 2.45* 1.78 0.39 2.75 2.67 2.87 2.34 Colima 3.18

* 2.67* 4.44 1.44 1.45 4.79 4.24

* 4.89 4.48

Huasteca 3.35*

7.48 3.03* 1.53 1.53 6.97 4.01*

7.40 4.51

Juarez 1.18*

1.22* 1.20* 1.68 1.68 1.68 1.16*

1.22* 1.68*

Laguna 1.43 1.43 1.43 1.21 1.22 1.43 1.42 1.43 1.55 Mazatlan 1.44 1.48 1.48 2.70 2.83 1.53 1.42 1.53 3.22 Mazatlan201

1.43 1.43 1.43 1.18 1.18 1.43 1.42 1.43 1.61

Monterrey 2.94 3.07 3.00 1.54 1.54 3.08 2.96 3.09 2.77 Occidente - 4.71 4.70 1.44 1.45 4.88 4.22

* 5.01 4.48

Oriente - 4.72 1.53 1.53 2.41*

5.29 4.72* 4.44

Petacalco - 1.48 1.48 1.48 4.84*

5.19 4.47

Sinaloa - 1.42 1.53 1.42 1.53 3.22 Sonrante - 1.53 1.43 1.53 3.22 Sureste - 1.53 1.43 3.22 Tepic - 5.54 4.42 Veracruz - 4.44

The empirical results are very interesting. In 1993, 73% of the nodal power regions were not cointegrated. Before deregulation and the removal of legal restrictions and integration of markets by law, and not by interconnection, we have a large percentage of nodal regions showing lack of cointegration. After the passage of several laws permitting more competitive markets, Mexico has a more integrated power network.

In 1998, 75% of the regional nodal power markets had cointegrated prices. The result suggests that almost all power regions were interconnected via transmission lines. This means that the Mexican power network between regions is competitive

and has the capability for complete open access.

Consider the Baja California Region, since it remains isolated from the rest of the Mexican National Interconnected system in terms of interconnection via transmission lines. In 1998, the Baja California Region showed lack of cointegration with other regions. We can rule out this region since it has no

possibility of integration after deregulation.

Cointegration in the power series by Sectors between the United States and Mexico  The law of one price does not to hold between the United States and Mexico for several reasons. Tariffs, product differentiation and quality, and institutional entry barriers are a few of the impediments in the history of the Mexican power markets. Other impediments include restrictive agreements between manufacturers and retailers, exchange rate volatility, tariffs and transportation costs. The next table shows that the hypothesis of non-cointegration is not rejected for the residential prices between the US and Mexico at 1% and 5% critical levels. This is not surprising since only large

users can choose foreign distributors in Mexico.

For the commercial sector, the first row tests the hypothesis of no cointegration. The

Johansen Test rejects the hypothesis of no cointegration. And doesn't reject the

hypothesis of one cointegrating relation, and two cointegrating relations, against the

hypothesis of full rank or all the series are stationary. Surprisingly for the commercial

sector, the law one price holds for power prices between Mexico and the U.S.

Table 4. Cointegration of Commercial Power Series between Mexico and the U.S. Series PESODOLLAREXCHAN ELECPRICECOMERUSA ELECPRICECOMERMEXICO Lags interval: 1 to 1 **denotes rejection of hypothesis at 5% significance level.

Likelihood

5 Percent 1 Percent Hypothesized

Eigenvalue Ratio Critical Value

Critical Value

No. of CE(s)

0.908108 71.77333

42.44 48.45 None **

0.256767 12.09493

25.32 30.45 At most 1

0.170599 4.676298

12.25 16.26 At most 2

Series: ELECPRICEINDUSA INDUSTPRICEMEX Lags interval: 1 to 1

Likelihood

5 Percent 1 Percent Hypothesized

Eigenvalue Ratio Critical Value

Critical Value

No. of CE(s)

0.446900 20.41396

25.32 30.45 None

0.200958 5.608532

12.25 16.26 At most 1

For the industrial and agricultural sectors we cannot reject the hypothesis of no cointegration.

Conclusions  The results show the apparent failure of the law of one price in 73% of the Mexican Regional Power Markets in 1993 due to the presence of substantial barriers to producer arbitrage. High barriers mean that only large price differentials can induce convergence in prices.  The results also suggests that the law of one price doesn’t hold in the residential sector between the U.S. and Mexico between 1973 and 1998, since the price differentials behave randomly and linear tests are unable to

find evidence of the price convergence condition.

Conclusions

The empirical examination of nodal power prices between regions in Mexico in 1998 leads me to conclude that regional power markets became more integrated after the economic reform in the power sector. By 1998, 75% of the nodal price market pairs between regions were cointegrated, showing a higher degree of integration in the Mexican National Power Transmission Network. Partial Open access in the Mexican transmision system has provided the basis for integrating separate and distant markets into one market.

Cointegration in the heavy industry power series between Mexico and the US, and increasing power exchanges between Mexico and the U.S. shows also a more integrated North American than the one thought by energy analysts.

Thank You! Muchas Gracias!

An Econometric Analysis of Regional Convergence and Market Integration in the Mexican Power Industry

  

An Econometric Analysis of Regional Convergence and Market Integration in the Mexican Power

Industry  

Un Análisis Econométrico de Convergencia Regional e Integración en el Sector Eléctrico Mexicano

 

Dr. Alejandro Díaz-Bautista

Economics Seminar, June 2005 Professor of Economics and Researcher at the Department of Economic Studies, and Coordinator of the Master’s Program in Applied Economics at COLEF. Mexico Address: Blvd. Abelardo L. Rodríguez 2925, Zona del Río, BC, 22320, México. U.S. Address : P.O. Box “L”, Chula Vista, CA, 91912-1257, USA. Email: adiazbau@gmail.com adiazbau@yahoo.com

Dr. Díaz-Bautista received his Ph.D. in Economics from the University of California Irvine (UCI). He also earned his master's degree in economics at UCI. He was also educated at UCSD and ITAM in Mexico City where he earned his Bachelor’s degree in Economics. His career has involved academics, government service and consulting for private firms.

Nota: Uso de Costos marginales

• Reconocen diferencias horarias, regionales y estacionales

• Costos Marginales de Capacidad y de Energía

• La tarificación de la energía eléctrica basada en los costos marginales de largo plazo del sistema eléctrico, da una señal económica a la clientela que favorece la eficiencia económica global

Tarifa Uso final Demanda / tensión

Regionalización Estructura

1, 1A, 1B, 1C, 1D, 1E, 1F

Doméstico No hay requisito / baja tensión

Por temperatura Cargo por energía en bloques de consumo

DAC Doméstica para alto consumo

Demanda superior a 250kW /mes* / baja tensión

Por temperatura Cargo fijo + cargo por energía en bloques de consumo

5, 5A, 6 Servicios públicos (alumbrado público, bomebo aguas negras)

No hay requisito / baja y media tensión

5:DF, Monterrey, Guadalajara. 5A: Resto del país6: No aplica

5, 5A: Cargo por energía plano6: Cargo fijo por servicio, cargo por energía plano

9, 9M, 9CU, 9N

Agrícola 9, 9M:No hay requisito de demanda/ Baja o media tensión9CU, 9N:No hay requisito de demanda/ No hay requisito de tensión

No aplica 9, 9M:Cargo por energía en bloques de consumo9CU, 9N:Cargo por energía plano

Tarifas específicas

Tarifas generales

Tarifa Clasificación Demanda / tensión

Regionalización Estructura

2, 3 Baja tensión 2:Menor a 25kW / baja tensión3:Mayor a 25kW / baja tensión

No aplica 2: Cargo por fijo mensual + Cargo por energía por bloques de consumo3: Cargo por demanda + cargo por energía plano

O-M, H-M

Media tensión O-M:Menor a 100kW / media tensiónH-M:Mayor a 100kW / media tensión

Baja California, Baja California Sur, Central, Noreste, Noroeste, Norte, Peninsular, Sur

O-M: Cargo por demanda + cargo por energía planoH-M:Cargo por demanda + cargo por energía en bloques horarios

H-S, H-SL, H-T, H-TL

Alta tensión (niveles de subtrasmisión y transmisión)

No hay requisitos / alta tensión

Baja California, Baja California Sur, Central, Noreste, Noroeste, Norte, Peninsular, Sur

Cargo por demanda + cargo por energía en bloques horarios

Actualización tarifaria

La SHCP determina los factores mensuales de ajuste para la tarifas del servicio público de energía eléctrica. El factor de ajuste para las tarifas específicas considera únicamente inflación relevante del sector, mientras que el factor de ajuste para las tarifas generales considera además variaciones en el precio de la canasta de combustibles

Los índices inflacionarios considerados son proporcionados por Banco de México

Los combustibles considerados para dicho ajuste son combusóleo nacional y de importación, carbón nacional y de importación, gas natural y diesel