Archive for inflation

How to lie twice to a President and succeed (13-VI-09)

Posted in 06 - Junio, Año 2009 with tags , , , , , , , , , , , , , , , , , , on June 13, 2009 by Farid Matuk

Near three months ago, it was analyzed how the Ministry of Economy and the Central Bank collude in order to show awesome GDP growth rates for Peruvian economy, in order to fulfill presidential fantasies of success, and now it is possible to witness how such collusion persists in order to show Peru as the country with the highest economic growth in Latin America, and one of the highest around the world.

Less than 2 weeks ago, in May 30th, the Ministry of Economy published the MMF (Multi-annual Macroeconomic Framework) for 2010-2012, which includes an economic forecast for 2009. Yesterday, the Central Bank published its quarterly report which includes also an economic forecast for 2009. As before, there is little difference on the GDP growth rate, 3.5% for one and 3.3% for the other, but this lucky coincidence is only good for providing presidential comfort, but behind those numbers two different worlds emerge.

The table below shows how the Ministry of Economy (ME) and the Central Bank (CB) evolve in its economic forecast, and also shows the gross differences on key indicators as elasticity between imports and GDP or export growth, which has impact on the ratio trade gap – GDP, as well on foreign currency reserve, and domestic currency convertibility.

 

Institution

Date of Issue GDP growth rate Import growth rate Export growth rate
ME May 28th ‘08 6.5% 12.6% 8.0%
CB Oct 24th ‘08 6.5% 9.0% 6.2%
CB Dec 17th ‘08 6.0% 7.3% 4.6%
ME Feb 2nd ‘09 5.0% 11.0% 5.4%
CB Feb 5th ‘09 5.0% 5.5% 3.4%
ME Feb 23rd ‘09 5.0% 13.5% 5.0%
CB Mar 21st ‘09 5.0% 2.1% 1.9%
ME May 30th 09 3.5% 1.3% -2.6%
CB Jun 12th 09 3.3% -4.7% -1.3%

  

Institution Date of Issue Elasticity Import – GDP Ratio Trade Gap – GDP
ME May 28th ‘08 1.9 4.8%
CB Oct 24th ‘08 1.4 4.4%
CB Dec 17th ‘08 1.2 4.3%
ME Feb 2nd ‘09 2.2 5.0%
CB Feb 5th ‘09 1.1 4.2%
ME Feb 23rd ‘09 2.7 5.7%
CB Mar 21st ‘09 0.4 3.7%
ME May 30th 09 0.4 2.4%
CB Jun 12th 09 -1.4 0.9%

 

As before, the elasticity between imports and GDP is a clear signal of two macroeconomic forecast structures forced to converge in a GDP growth of 3.3%-3.5%. While the Ministry of Economy finds a positive elasticity, the Central Bank finds a negative elasticity which is extremely uncommon in Peruvian economic history of the last 20 years; in particular to have a negative growth rate of imports.

Last circumstance Peru had a real reduction of imports was in 1999 due to a negative GDP growth in 1998. Previous similar experience was in 1988 and 1989, when Peru has the most disastrous economic experience becoming a member of the select group of countries with hyperinflation a la Cagan (more than 10,000% annual) with a GDP contraction of more than 20%.

While Ministry of Economy forecast could be seen as straightforward optimistic and unreal, at least it is possible to foresee a conventional macroeconomic model behind with coefficients making sense with experience. On the other side, the Central Bank model looks more as a two stage model, on the first stage realistic assumptions are made (as negative imports growth due to a recession) and in the second stage comfort outcome are imposed (positive GDP growth).

 In any case, Peruvian economic authorities had shown a clear behavior of common deception to the Presidency. Both of them show a positive GDP growth which goes against common sense but on synchrony with President Garcia flamboyancy. This deceptive behavior has been analyzed previously in “How to lie to a President and succeed”.

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20090324 – Hunger intensifies despite economic growth in Peru

Posted in 3 Cables with tags , , , , , , , , , , , , , , , , on March 28, 2009 by Farid Matuk

32 percent of Peruvians get inadequate food

Slower economic growth likely to push up hunger rates

By Dana Ford

LIMA, March 24 (Reuters) – More Peruvians went hungry last year despite blazing economic growth, a sign that President Alan Garcia is stumbling in efforts to direct benefits of an impressive expansion to the poor.

The percentage of people in Peru with inadequate nutrition rose by more than 11 percent in 2008, faster than the economy’s 9.8 percent surge, according to the national statistics agency.

Now, 32 percent of Peruvians do not get enough to eat.

The results suggest the poor did not make gains during Peru’s economic boom last year. They also explain in part why the government is so unpopular in rural areas, where hunger rates are highest and leftist politicians like Ollanta Humala, who plans to run for office in 2011, draw support.

“The benefits of the economic boom have not been distributed equally,” said Federico Arnillas, president of a network of civic groups that works on poverty issues with the health and finance ministries.

Garcia, who embraced mainstream economic policies after his first term in the 1980s ended in runaway inflation that made adequate food too costly for millions of people, has said he wants to reduce poverty to 30 percent by the time he leaves office.

When he was re-elected in 2006, Garcia fervently pushed investment and free trade and his recipe to lift incomes seemed to work. Prices for Peru’s metal exports surged and domestic demand rose, contributing to rapid economic growth.

The national poverty rate fell 5 percent in 2007 to 39 percent, a year when inflation was low and public spending on food programs was relatively high.

But in 2008, hunger crept up, as inflation spiked on a global run up in food prices and aid spending fell. Peru’s poverty rate for last year is not yet available, but experts say the government may have lost ground. That could hurt Garcia’s approval rating, now at 34 percent.

“The numbers tell us there is a percentage of the population that is, quite literally, dying of hunger,” said Farid Matuk, a former director of the national statistics agency and a government critic.

In rural areas, where Garcia’s support is weak, the number of people not eating enough rose to 42.5 percent in 2008.

Arnillas said the increase stems from political decisions and pointed to cuts in social spending.

“It’s not a simple resource problem. It’s a political one,” he said of hunger in Peru.

LOOKING AHEAD

Advocates say slower economic growth this year will likely push hunger rates higher and are urging the government to adopt policies that prioritize food security.

Peru’s government is rolling out a $3 billion stimulus package meant to maintain investment and employment levels and increase public work projects. The plan, which aims for economic growth of at least 5 percent, also includes agricultural incentives to boost local food production.

Matuk, the former statistics agency head, said the government is too focused on high macroeconomic growth figures and should have paid more attention to the poor before the global economy entered a crisis.

Arnillas said Peru needs a bigger safety net as private economists forecast growth of less than 1 percent this year.

“We are worried the poor will wind up paying the cost of the crisis,” he said. “This is what happened in the past and we are working to make sure it does not happen again.” (Editing by Terry Wade and Vicki Allen)

http://www.reuters.com/article/idUSN24355472

Growth, Inflation & Poverty (24-III-09)

Posted in 03 - Marzo, Año 2009 with tags , , , , , , , , , , , , , , , , , , , , , , , on March 24, 2009 by Farid Matuk

The most common approach to lowering poverty rates is to have high economic growth, but recent evidence from Peru disputes this result, pointing out that high growth rates could be canceled out by high inflation rates, with as a consequence high growth rates and higher poverty rates.

Measuring poverty has many tools from the most elemental to the most sophisticated; data available speaks of the degree of statistical development of each county, as well the willingness of each government to finance surveys that expose the painful reality of the poor.

The simplest method consists of a poverty line in US dollars, being US$ 2 per day a popular threshold. The conventional problem with this value is different purchasing power of US$ 2 in different countries, the World Bank has tried to solve this problem with a successful world-wide effort to measure PPP (purchasing power parity) for each country and therefore now it is possible to have US$ 2 PPP for each country. But a large limitation of this approach is that rural areas – where the poor live- have been excluded because the PPP was build with domestic CPI (consumer price index), which by definition excludes rural areas.

A second approach, also pioneered by the World Bank, is a poverty survey, well known as Living Standard Measurement Survey (LSMS). A medium size survey (around 5,000 households) is applied in urban and rural areas of any country, with a strong emphasis in food and beverage consumption (as source of calories), plus expenses in other major items of any consumption basket. The main result of this approach is to obtain a poverty line in domestic currency for any given country.

The main pitfall of this approach is the lack of transparency of the assumptions taken for producing a poverty line. Almost every country produces data tabulates; most of them data base access; and almost none computer code applied. The computer code written is essential to identify is a systematic bias have been applied for the published poverty line, as well to learn all arbitrary decision taken on the steps described below.

Step 1: How to define an average poor household? Usually the average is in the half poor of the sample, but there is no international standard to identify it. If the mean poor household is closer to the median household, the final poverty line will be high compare to a mean poor household who is far from the median household.

Step 2: How to define a vicinity of the average poor? After a mean poor household was identified, vicinity must be defined. This could be one tenth, one fifth, one fourth or one third of the sample, and again there is no international standard. For a larger vicinity, a lower poverty line is found, and vice versa.

Step 3: How to define a basket of food and beverages for the extreme poverty line? After steps 1 and 2 are done, the researcher must choose which goods will be taken in account for valorizing the extreme poverty line, which is made setting a price for each product chosen. The exclusion criterion is arbitrary and may produce biases in any direction, according the price of the excluded products.

Step 4: How to deflate prices spatially? Since the survey is applied nation-wide, there are areas were food and beverages are non-market items, because the households in rural areas have an economy of subsistence, where they are producers and consumers at the same time. Theoretically there is many options to imputed prices, but since computer code is not public, a source of bias could be easily masked.

Step 5: How to measure an Engel coefficient for the extreme poverty line? After the extreme poverty line is obtained, it is necessary to produce a total poverty line, which must include non-calorie goods and services. While again, there is a large literature on this subject, without the computer code is impossible to analyze if the poverty line has a bias that overestimates or underestimates real poverty.

Besides these limitations, a poverty line is measured with a survey, and a poverty basket is designed to monitor poverty evolution. All problems of a conventional Laspeyres index are valid, but certainly a national poverty line is better than a US$ 2 PPP line because this measurement includes rural areas, where most of the poor used to live.

The UN MDG (United Nations Millennium Development Goals) has several goals, where Goal 1 is “Eradicate extreme poverty and hunger”, Target 1.C is “Halve, between 1990 and 2015, the proportion of people who suffer from hunger”, Indicator 1.8 is “Prevalence of underweight children under five years of age” and Indicator 1.9 is “Proportion of population below minimum level of dietary energy consumption”.

While Target 1.A and Target 1.B for Goal 1 are related to economic conditions of the poor, Target 1.C is related to biological and anthropometric characteristics of the poor. The main advantage of indicators for Target 1.C is that fewer assumptions are required for its measurement, therefore less built-in steps for biases.

For Indicator 1.8, the most common statistical device is the Demographic and Health Survey (DHS) which is funded by United States International Development Agency (USAID) around the world. The traditional design is a large scale sample around 20,000, which allows to measure demographic and heath variables, applied in intervals of 5 years. A new approach, which has Peru as pilot country started in 2004, sampling every year 6,000 households in a five year plan; this new design is able to produce statistical results for key variables with low variance, and for other variables in 5-year average.

For Indicator 1.9, a World Bank’s LSMS could be used in order to measure caloric intake and caloric needs for each household and from both figures the percentage of population below minimum intake could be obtained.

The graphs below are for Peru where Indicator 1.9 is plotted with inflation in the first and with growth in the second.

Poverty & Inflation (2004 - 2008)

Poverty & Inflation (2004 - 2008)

Poverty & Growth (2004 - 2008)

Poverty & Growth (2004 - 2008)

Starting May 2003, Peru was applying a LSMS in monthly basis with an annual target of 20,000 households. This new approach has sampling difficulties that were solve through a technical cooperation program with Statistics Canada, who did the sampling for the first year, and subsequent years were done by Peru’s statistical agency.

For monetary poverty results, an annual sample is cumulated and then a poverty line in domestic currency is obtained as described above. The first result was measured for May 2003 – April 2004, and subsequent results have been published for calendar years.

But the main advantage of this design is to obtain quarterly results for caloric poverty as defined by UN MDG Indicator 1.9. In the graphs above, the bars are annual moving average for caloric poverty, as well economic growth and consumer inflation. The spreadsheet to redo the graphs is available here and was obtained from official sources as described below.

The quarterly results are available in PDF on the web site of Peru’s Statistical Agency, first step is to click in “Boletines” on the left side panel, then to click on “Condiciones de Vida” also on the left side panel, and download the PDF files for each calendar quarter since 2007, and for each moving quarter since 2003.

The other two variables, economic growth and consumer inflation are taken from the web site of Peru’s Central Bank, economic growth is obtained from real quarterly national accounts, with the rate of growth of the moving average of four quarters compared with previous fourth quarters of Gross Domestic Product (GDP). Consumer Inflation have been built from the implicit deflator for private consumption; in order to obtain this index, the private consumption in the nominal quarterly national accounts, is divided by the private consumption in the real quarterly national accounts. The rate of growth follows same procedure for economic growth.

An examination of both graphs offers a clear example that GDP growth by itself will not reduce poverty, and that the inflation level is a more critical tool for fighting poverty. Therefore low growth with low inflation reduces poverty at a steady rate, while high growth with high inflation increases poverty at a steady rate.

  • Econometric Analysis

Besides this graphical analysis, an econometric one is feasible and some results are presented below. In first place, the sampling period for the analysis is the whole period for available quarterly measurement of caloric poverty; this is from 2003 Q3 to 2008 Q4, with a total of 22 observations. Data for economic growth and consumer inflation is available quarterly since 1980.

Caloric poverty (CALP) is the percentage of population who lacks the minimum calorie intake; economic growth (GDP) is the difference of logarithm of real GDP in any quarter to similar in previous year; and consumer inflation has same mathematical transformation with the deflator of private consumption (DPC).

The model to be estimated is quite simple, with a perturbation component that fulfill classical assumption for error term of normal distribution, serial independence, and homocedasticty:

CALP{t} = BETA0 + BETA1*DPC{t} + BETA2*GDP{t-1} + U{t}

The data could be found here, the RATS source code could be found here, and the RATS output file could be found here.

An initial regression was tried with GDP impact in same period, but GDP lagged one period showed a better result. Therefore changes in inflation have a faster impact on poverty than growth rate, which is not surprising that a nominal variable has faster impact than a real variable.

The best result provides BETA1 and BETA2 coefficients with null hypothesis of zero value rejected at 99% confidence, when the first observation of the sample is excluded, having as result a total of 20 observations for the analysis.

Another important result also showed in the output file is that null hypothesis of BETA1 and BETA2 having similar value with opposite sign is always accepted, with several sampling periods. This allows conclude that lowing inflation has the same impact that increasing growth rate.

Finally, the econometric evidence reaffirms what was intuitive on the graphs. Not only economic growth matters for fighting poverty, also matters low inflation in equal degree.

How to lie to a President and succeed (16-III-09)

Posted in 03 - Marzo, Año 2009 with tags , , , , , , , , , , , , , , , , , , on March 16, 2009 by Farid Matuk

Peruvian economic authorities are two as in many countries, one in charge of fiscal policy and other in charge of monetary policy. As in many countries too, fiscal policy is in charge of a Minister, who is an appointee of the President; and monetary policy is in charge of the head of the Central Bank, who in Peru is choose by the Congress.

Until May 2008, both authorities had a point of agreement in economic policy and macroeconomic forecast, in a document called Multi-annual Macroeconomic Framework (Marco Macroeconómico Multianual), which is prepared by the Ministry of Economy and subscribed by the Central Bank, through an attached letter of acceptance.

Actual Peruvian president, hold the dubious record of producing the most lasting hyperinflation (Cagan definition) in Latin America, in his first tenure between 1985 and 1990, and this legacy hunts him.  At the beginning of the present economic crisis in 2008 he declared “Peru is insulated”, now he declares that Peru will be in top 5 world wide highest GDP growth for 2009, in an optimism that exudes maniac behavior.

But the world economic crisis has produced a hidden divorce in Peru economic authorities, in the process of trying to satisfy a President who believes that economic panic could be cured by hypnosis, the Ministry of Economy and the Central Bank have build macroeconomic forecast based on presidential needs and not on technical criteria.

In the table below, is possible to see how the public documents from both institutions, available through Internet, document two different ways to fulfill a GDP growth rate out of reality. ME stands for Ministry of Economy and CB stands for Central Bank.

Institution

Date of Issue

GDP growth rate

Import growth rate

Export growth rate

ME

May 28th ‘08

6.5%

12.6%

8.0%

CB

Oct 24th ‘08

6.5%

9.0%

6.2%

CB

Dec 17th ‘08

6.0%

7.3%

4.6%

ME

Feb 2nd ‘09

5.0%

11.0%

5.4%

CB

Feb 5th ‘09

5.0%

5.5%

3.4%

ME

Feb 23rd ‘09

5.0%

13.5%

5.0%

CB

Mar 21st ‘09

5.0%

2.1%

1.9%

Institution

Date of Issue

Elasticity Import – GDP

Ratio Trade Gap – GDP

ME

May 28th ‘08

1.9

4.8%

CB

Oct 24th ‘08

1.4

4.4%

CB

Dec 17th ‘08

1.2

4.3%

ME

Feb 2nd ‘09

2.2

5.0%

CB

Feb 5th ‘09

1.1

4.2%

ME

Feb 23rd ‘09

2.7

5.7%

CB

Mar 21st ‘09

0.4

3.7%

Even today is March 16th, Central Bank web site link to its March document is dated March 21st, who know why. Going to substance, the column for GDP growth rate shows a reduction from 6.5% to 5.0% from May ’08 to Mar ’09; which looks as a shy reduction to many analysts but maybe Peru has a secret formula for growth.

The column for Import growth rate shows substantial differences between the Ministry of Economy and the Central Bank, which is more evident in the column Elasticity Import – GDP. The Ministry of Economy has elasticity value that varies between 1.9 and 2.7, which fits with my own econometric estimations for this coefficient. But the Central Bank has an elasticity value that varies between 1.4 and 0.4 which is unrealistic from an econometric point of view, as well with historical data. Unless the Central Bank is planning a drastic change in its actual monetary policy, therefore the Peruvian currency will have a free fall enough large to reduce imports.

Finally, the last column shows the ratio for foreign trade and GDP, if May 2008 is taken as base line with financial markets willing to lend to emerging economies, actual circumstances imply a smaller gap since financial availability is lower than before for emerging markets. Also if terms of trade are constant, the baseline gap remains constant; but if there is deterioration of term of trade for Peru, then we have large gap; and today all analysis assumes deterioration for Peruvian terms of trade.

Assuming the Ministry of Economy export – GDP elasticity and the Central Bank export growth forecast, it is possible to simulate several scenarios. In order to reach the ratio for foreign trade and GDP of the base line, which is 4.8%, the maximum GDP growth rate feasible is 2.5%.

If foreign influx of capital is less than baseline scenario May 2008, and terms of trade deteriorated relative of baseline scenario May 2008, then GDP growth rate will be below 2.5%. From other analysis (in Spanish) made with short-term and medium-term real-sector economic cycle, the outcome is more grim with a GDP growth rate negative in 2010-Q1 of -1% (see “Fundiendo Motor” and “Compulsion a la Repeticion“).

Sat Apr 26, 2003 8:19 am

Posted in 2003-04 Abril with tags , , , , , , , , , , , , , , , on January 28, 2009 by Farid Matuk

 
Bruto(a) y Neto(a) [1]

Hola Luis Alberto:

Coincido contigo en que los términos “bruto” y “neto” son
confusionales porque no se encuentra una “tara”. Pero asi se crearon
las palabras hasta nuevo aviso.

La diferencia central es debido al fraccionamiento lineal de la
serie en el tiempo. Una serie como tipo de cambio es altamente
fraccionable pudiendo tenerse una devaluación por segundo, mientras
que una serie como PBI para todos los paises del mundo excepto tres
(Canada, Finlandia, y Perú) el fraccionamiento sólo llega a
trimestre.

Quizás colectivamente podemos hallar una manera de llamar a la
variacion del PBI del cuarto trimestre de una año con el cuarto
trimestre del anterior (Crecimiento Anual Neto) y otra manera de
llamar a la variación del PBI de un año con el año anterior
(Crecimiento Anual Bruto).

No se me ocurren nuevas palabras, pero estoy seguro que alguien
ingenioso puede hallar alguna mejor, porque de acuerdo al párrafo
anterior la “tara” sería una suerte de corrección del ciclo
económico.

Un abrazo, Farid

Fri Apr 25, 2003 7:00 am

Posted in 2003-04 Abril with tags , , , , , , , , , , , , , , , , on January 28, 2009 by Farid Matuk

Bruto(a) y Neto(a)

El día de hoy un diario local denominó “estrafalario” al concepto
de inflación bruta e inflación neta en boga hace algunos años. Si
bien la aplicación específica estuvo errónea como veremos a
continuación, la ignorancia supina del editorialista del diario en
cuestion es mayúscula.

Para medir la inflación mes a mes, se necesita hacer una canasta
de precisión mensual, en ese sentido, en el Perú tenemos que en 1994
se encuestaron 40,000 hogares para tener el IPC, si quisieramos
tener inflación semanal necesitariamos una muestra de 170,000
hogares, y si quisieramos tener una inflación diaria necesitariamos
una muestra de 1’200,000 hogares.

El problema de la inflación neta, que comparaba el último día de
un mes con el correspondiente del mes anterior, era un problema de
muestra, no un problema de concepto. Creía poder medir la inflación
con una muestra 30 veces mas pequeña que la necesaria, lo cual es
obviamente un error.

Casos legítimos de bruto y neto, son la devaluación y la
variación de las exportaciones entre otros. Cuando se habla de
devaluación de tipo de cambio de fin de periodo se tiene devaluación
neta, y cuando se habla de devaluación de tipo de cambio promedio se
tiene devaluación bruta. Por ejemplo la devaluación entre el 30 de
abril y el 31 de marzo es devaluación neta, y la devaluación entre
abril y marzo es devaluación bruta. La variación de las
exportaciones entre Abril 2003 y Abril 2002 es neta, pero la
variación entre el 2003 (cuando termine el año) y el 2002 sera bruta.

Como se puede ver, hacer las variaciones bruta y neta en índices
sustentados en información de campo es sumamente difícil por los
tamaños de muestra necesarios para hacer este ejercicio. De otro
lado, los índices sustentados en información administrativa son muy
dúctiles para hallar variaciones bruta y neta.

Un caso interesante sería la recaudación tributaria, si tuvieramos
un reporte de la variación de la tributación entre el 24 de abril de
2003 y el 24 de marzo de 2003 sería un reporte neto, frente al
reporte bruto que sería la variación entre todo abril y todo marzo.
Como a su vez, la variación entre marzo 2003 y marzo 2002 es neta,
frente a la variación bruta que habrá entre el 2003 (cuando termine
el año) y el 2002.

Un caso realmente estrafalario fue cuando empresas privadas en
los tiempos de la hiperinflación reportaban inflación semanal y en
algunos casos diaria, sin ninguna base muestral. Un caso
estrafalario semejante es el denominado PBI mensual, cuando sin
ninguna base muestral se fraccionó la medición del PBI trimestral.

La medición estadística no es un ejercicio de contabilidad en
donde la divisibilidad no es problema del todo, para tener mayor
frecuencia espacial o temporal en cualquier variable se requiere
mayor muestra. Por ello, a modo de ejemplo, hablar de PBI
departamental implica un nivel de muestra que no es posible conocer
al presente, dada la naturaleza del Censo Económico de 1994.

http://groups.yahoo.com/group/MacroPeru/message/2803

Sat Apr 5, 2003 10:11 pm

Posted in 2003-04 Abril with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , on January 28, 2009 by Farid Matuk

Activos y Flujos

Hoy tenemos en La República “… ¿nuestras exportaciones no
tradicionales (que son las que importan) podrán seguir creciendo con
una moneda nacional que se revalúa día a día? …” y “Sea lo que
fuere, es difícil pensar en un crecimiento sostenido de las
exportaciones no tradicionales si se mantiene o profundiza la
apreciación de nuestra moneda (precio relativo desfavorable). ¿Qué
es lo que viene sucediendo?”

Primero quisiera recordar un libro de Fair, quien es uno de los
pocos con un algoritmo propio para máxima verosimilitud, donde tiene
dos modelos simples para explicar valor agregado. Uno basado en
flujos donde el PBI depende de un componente de demanda exógeno, que
sería clasificado popularmente como keynesiano, y otro basado en
activos donde el PBI depende de un activo cualquiera, que sería
clasificado popularmente como monetarista.

En el pico de la hiperinflación, se discutía sobre las causas de
ésta, y básicamente había la de exceso de demanda interna (flujos) y
la de escasez de divisas (activos). Creo que similar enfoque se
puede aplicar al presente. O necesitamos un tipo de cambio real que
permita crecer via exportaciones no tradicionales (flujos) o
necesitamos un tipo de cambio real que permita crecer via
acumulación (activos).

La impresión que tengo esta sustentada en un mundo de activos,
donde la brecha entre las tasas de interés de soles y las tasas de
interés de dólares reflejan la devaluación esperada, pero
parafraseando a Beckett en “Esperando a Godot” la espera desespera y
la devaluación nunca llega, mientras el deseo que llegue se torna
desesperación.

Con un poco de tiempo y con la información detallada de la SBS,
se podría hacer el cálculo de la desesperación en 2002, es decir
cuanto fue la pérdida implícita de aquellos que ahorraron en dolares
y los que prestaron en dólares, versus la minoria que hizo las
mismas operaciones de activos en soles.

Un tema final sobre el tipo de cambio real, está en la
complejidad de construir los deflatores del comercio exterior de
bienes, ya que de servicios es sumamente dificultoso. Para
exportaciones suele ser mas fácil ya que estan mas concentradas pero
para un país con un fuerte componente de exportaciones primarias,
deflatar las exportaciones no primarias es titánico. Pero
importaciones tambien suele ser un problema ya que se tienen dos
soluciones básicas, o se deflata con algún índice de precios del
socio comercial o se se enfrenta la díficil tarea de construir un
índice de volumen de las importaciones.

Farid Matuk 

http://groups.yahoo.com/group/MacroPeru/message/2747