Archive for growth

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.



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”.


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.


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)

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.


Date of Issue

GDP growth rate

Import growth rate

Export growth rate


May 28th ‘08





Oct 24th ‘08





Dec 17th ‘08





Feb 2nd ‘09





Feb 5th ‘09





Feb 23rd ‘09





Mar 21st ‘09





Date of Issue

Elasticity Import – GDP

Ratio Trade Gap – GDP


May 28th ‘08




Oct 24th ‘08




Dec 17th ‘08




Feb 2nd ‘09




Feb 5th ‘09




Feb 23rd ‘09




Mar 21st ‘09



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“).

Mon Jun 9, 2003 7:02 am

Posted in 2003-06 Junio with tags , , , , , , , , , , , , on February 1, 2009 by Farid Matuk

Ley de Okun

Hace varios dias que no aparece la teoría del 6%-7% de
crecimiento para emplear el crecimiento vegetativo de la PEA. Pero
si se publicó recientemente en un diario local un estimado de la Ley
de Okun para Perú de 2.34%

Esto quiere decir que para mantener la tasa de desempleo natural,
en caso este exista, basta crecer al 2.34% hasta donde recuerdo la
Ley de Okun. Y los desvios sobre esta tasa incrementan o reducen la
tasa de desempleo.

Si esta medición es cierta, tenemos que no es necesario el 6%-7%
de crecimiento para mantener el status quo, sino bastaría un poco
mas de 2%.

Creo que junto a la discusión previa sobre el ICOR, aquí tenemos
una veta nueva para complementar una suerte de modelo macro basado
en pocas reglas genéricas pero que permitirían hacer simulaciones
menos sensibles a parametros estimados, y menos discrecionales que
las délficas.

Farid Matuk

Timoratos versus Timberos – Parte Dos (La República 18-IV-07)

Posted in Año 2007 with tags , , , , , , , , , , , , , , , , , , , , on January 31, 2009 by Farid Matuk


Timorato: Tímido, indeciso, encogido.


Timbero: Apostador en juegos de azar.

En el gráfico adjunto tenemos once años de PBI mensual en el Perú, pero nuestros ojos son engañados por el mal hábito de creer que en el Perú se mide el PBI mes a mes. Este mal hábito empezó en 1987 cuando la Presidencia de la República quería “buenas noticias” mensuales, y el INEI tuvo la cortesía  de empezar a medir mensualmente, lo que el resto del planeta mide trimestralmente.



Como referencia, la medición del PBI mensual es tan compleja y difícil, que de las potencias que conforman el G-7 (Alemania, Canadá, Estados Unidos, Francia, Inglaterra, Italia, y Japón) sólo uno de ellos –Canadá- puede hacerlo. Y a nivel de la toda la Comunidad Europea, tan sólo un país –Finlandia- puede hacerlo. Por ello linda entre lo iluso y lo idiota creer que en Perú tenemos un PBI mensual.


Lo que si tenemos como sólido, es la información de campo que mensualmente las oficinas de estadística de los ministerios respectivos acopian sobre la agricultura, la pesquería, la minería, la manufactura, y la producción de agua y electricidad. Esta información consolidada por el INEI, produce una medición del 30% de nuestra actividad económica y esta la graficada como “PBI de Campo”.


El otro 70% del PBI no se mide en el campo, sino que se calcula en el gabinete para satisfacer un capricho presidencial que data de 1987. Este cálculo se hace con un conjunto variado de artificios matemáticos, que ni las potencias del G-7 ni los países europeos arriesgan usar, y este cálculo es grave porque induce a pensar que lo graficado como “PBI de Gabinete” es sólido, cuando en realidad es extremadamente dúctil.


El gráfico en cuestión parte de Agosto 1995 y culmina en Febrero 2007, y tenemos que en las gestiones Fujimori, Paniagua, y Toledo, ambos PBI están entrelazados, y por ello la medición total se puede considerar relativamente exacta. Pero en Marzo 2006 esta coherencia se pierde, porque el PBI de Gabinete que tiene una metodología dúctil, crece sin cesar y con ello el PBI Total también crece sin cesar.


Este cambio de patrón tiene como única explicación un cambio metodológico en el cálculo del PBI de Gabinete, que provoca una sobre-estimación sistemática de la medición del PBI Total. Esta medición errónea puede ser interpretada benévolamente como una “falla” a corregir de inmediato, o puede ser interpretada malévolamente como un “arreglo favorable”.


Desde afuera, el timbero creerá que el PBI continuará a tasas de crecimiento mayores mes a mes y tomará sus decisiones de acuerdo a esta realidad errónea, mientras que el timorato creerá que el PBI continuará creciendo a tasas en torno al 6% y tomará sus decisiones de acuerdo a esta realidad parcial.

Timoratos versus Timberos – Parte Uno (La República 16-IV-07)

Posted in Año 2007 with tags , , , , , , , , , , , , , , , , , , , , on January 31, 2009 by Farid Matuk


Timorato: Tímido, indeciso, encogido.


Timbero: Apostador en juegos de azar. 

 En el gráfico adjunto tenemos 56 años de historia de la evolución de los precios de nuestras exportaciones e importaciones de acuerdo al Banco Central. Un primer elemento cierto es que desde 1980 en adelante los productos que exportábamos valían menos que los productos que importábamos, pero esta tendencia se revierte desde 2001. En otras palabras, durante esos 21 años con el paso del tiempo nos iba igual o peor.



Al presente, tenemos una situación favorable donde el poder de compra de lo que vendemos al exterior se incrementa respecto a lo que compramos del exterior, situación que nos conduce a la situación de bonanza que se vive al presente con el inédito temor que el precio del dólar se reduzca, y no como fue nuestra experiencia desde el auge del guano en el siglo XIX, que el precio del dólar siempre se incrementaba.


Al cierre del 2006, tenemos cinco años consecutivos de incremento de nuestros precios al exterior con un valor de 52%, pero nunca antes a excepción del periodo 1961-1966 tuvimos una situación similar de mejora permanente, pero en ese quinquenio fue de tan sólo 32%. Finalmente, hay que tener presente que en nuestra historia registrada, el record es de cinco años de mejora, y estos 5 años se cumplieron en 2006.


Pero esta mejora de los precios internacionales no es nueva sino más bien cíclica, y las dos más importantes correspondieron a los periodos 1972-1974 y 1978-1980 correspondientes a los gobiernos de Velasco Alvarado y Morales Bermúdez, respectivamente. En el primero los nuestros precios mejoraron 37%, y en el segundo mejoraron 24%., y en ambos casos para derrumbarse posteriormente.


Si analizamos un periodo similar con nuestro presente, este sería 2004-2006 donde nuestros precios mejoraron 33% en sólo dos años, que es una tasa inferior a la de Velasco pero mayor a la de Morales; con la legítima duda de cual será la trayectoria futura, y para ello tenemos dos visiones alternativas del futuro, la de timorato y la de timbero.


La alternativa timorata nos dirá que esta mejora de los precios externos tiene que terminar por el pasado se repite y porque en el 2006 ya vivimos una situación inédita tanto en la duración de la mejoría como en el nivel de la misma.


La alternativa timbera nos dirá que el pasado ya pasó y que el futuro será diferente; por ello lo vivido desde el 2001 al 2006 es sólo el inicio de una nueva ruta por el paraíso prometido, y que no debemos temer un regreso a las crisis habituales.


En la segunda parte de este artículo, se verá cuales serán las consecuencia de ser timorato o timbero.

Fri Apr 25, 2003 3:42 am

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

El Crecimiento Anual (2)

Hola Bruno:

Si entiendo la idea de los días, pero la utilidad sería para
tener una serie “limpia” de tendencia, ciclo, y estacionalidad.
Quizas aplicando alguna suerte de filtro sea útil, porque si el
concepto “Semana Santa” es válido, tambien lo serían “Fiestas
Patrias” y “Navidad” en el sentido que los días trabajados se
reducen; aunque estas fiestas no son móviles.

De otro lado sería interesante evaluar el efecto de los “puentes”
en los feriados, quizás países con mejor infraestructura estadística
han efectuado la evaluación del impacto total en la economía de
estos dias no trabajados.

Thu Apr 24, 2003 5:30 am

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

El Crecimiento Anual
Hola Bruno:

RPP hoy dia señala que el IPE aumentó su proyección anual de 3.7%
a 4.1%, lo cual parece ir a contracorriente de tu idea que “… (la
economía) parece haberse parado por el shock de oferta del I
trimestre del año …” ¿Podrías detallar en que consistió ese shock
de oferta?

Respecto a los índices mensuales de producción, la parte directa
viene de las Oficinas Sectoriales de Estadística y no tienen
información día por día. La parte indirecta son simplemente fórmulas
basadas en los datos mensuales de información de campo o de
información administrativa.

Respecto al efecto “Semana Santa”, no me queda clara su
dirección. Si la semana de trabajo es de lunes a viernes, el mes se
redujo de 22 días a 20 días, y si fuese de lunes a sábado, el mes se
redujo de 26 días a 23 días. Así que no me es claro cuan fuerte la
Semana Santa impacta en los niveles de actividad.

Mas bien, como le comentaba a Elmer, si tuviesemos mediciones
directas del sector servicios, la producción de servicios de
transporte, hoteles, y restaurantes debe observar un pico en Semana
Santa. Por ello, creo imposible deflatar Semana Santa de cualquier
índice agregado.

Un abrazo, Farid

Thu Apr 24, 2003 1:18 am

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

Falso Rezago

            En el archivo IMF_0902 se encuentra en la pestaña “Paridad” un cuadro comparado de las brechas entre Peru y Chile, estas son 79% en 1960, 66% en 1970, y 87% en 1996. En este caso se tiene la misma canasta de bienes y servicios en ambos servicios, y se halla que Perú luego de reducir la brecha en 1970, la brecha se amplia en 1996.

             En la pestaña “PBIpc US$” se hace otra comparación entre Perú y Chile usando dólares nominales per cápita, a fin de evitar las altas variaciones del tipo de cambio se ha establecido promedios móviles decenales. Aqui se observa como la brecha se estuvo reduciendo sistemáticamente hasta 1988, y desde esa fecha la brecha se ha venido ampliando. Este resultado es coincidente con los estudios de paridad del párrafo anterior.

             En la pestaña “PBIpc Real” se hace nuevamente la comparación entre Perú y Chile. En este caso es el PBI real de cada país, es decir, la canasta es la misma al interior de cada país. Forzando una base 100 para la década del ’60, el resultado para el Perú es 15% de crecimiento luego de 40 años, mientras que para Chile es un crecimiento de 70%. Lo cual esta en contradicción con las dos pestañas anteriores.

             La conclusión es que existe una sistemática subestimación del crecimiento del PBI real por el uso de deflatores explícitos para el sector servicios ante la carencia de índices de volumen para dicho sector.

            El problema tambien puede ser visto de otra manera. Para el año base 1994 tal como esta publicado el consumo final de hogares es 71.3 miles de millones de soles, mientras el consumo intermedio de las industrias es 73.2 miles de millones de soles. Pero mientras para el consumo final se muestrean 70,000 precios al mes, para el consumo intermedio se muestrean 5,000 precios al mes. Es decir 14 veces menos para dos variables de prácticamente similar tamaño.

             Este hecho hace que el Valor Bruto de Producción sea deflatado sobre una base muy frágil, y dada la metodología hallada en el Instituto se produzca una subestimación sistemática del PBI, aunque otra metodología igualmente frágil hubiera podido producir un resultado inverso.

             En el archivo PBI_Movil, se tienen series largas mensuales con la base 1994 de todos los sectores del denominado PBI Mensual. Se han construido dos índices sintéticos, uno con aquellos sectores que cuentan con información de campo, y otro con aquellos sectores con elaboración de gabinete.

             Me he abstenido de hacer ejercicios de cointegración porque no me encuentro familiarizado con las últimas técnicas, pero sería interesante hallar el resultado. El panorama mundial es que el sector servicios crece mas rápido que el sector bienes y por lo tanto la hipótesis nula de cointegración debiera rechazarse.

             En la ventana de datos con la que contamos se produce un efecto difícil de esclarecer, en el sentido que la llamada primarización de la economía en los años recientes, popularmente llamado “Efecto Antamina”, es pasible de ser medida. Mientras que el neologismo del “supermercadismo”, usado en el contexto de nuevos centros comerciales tales como el de Los Olivos o de nuevas tiendas de departamentos en provincia tales como la de Arequipa, no es pasible de ser medido.

La conclusión es que mientras tenemos información de campo de las áreas mas tradicionales de la economía, carecemos de información de campo para las áreas mas dinámicas de la economía.