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

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 & 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.

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20090317 – Peru economy slows, growing by 3.1 pct in January

Peru’s boom sputtering? Economic growth slows to 3.1 pct in January
By ANDREW WHALEN
Associated Press Writer

LIMA, Peru (AP) _ Peru posted its lowest economic growth rate in years on Monday, saying it expanded by just 3.1 percent year-to-year in January amid signs that a three-year economic boom fueled by soaring metals prices could be sputtering.

The Andean nation’s economy grew by 9.8 percent last year, faster than China’s, but has slowed as the global financial crisis drives down prices and demand for its main mineral exports.

Peru’s statistics institute reported Monday that the economy grew by 3.1 percent in January over the same period a year earlier, the lowest monthly rate of President Alan Garcia’s term, which began in 2006.

 The report said exports fell 38.6 percent in January over the same period the year before.

The government is projecting 5 percent growth in 2009, which would be one of the highest in the world amid the global downturn. But analysts are already questioning the figure.

The government is implementing a $3 billion anti-crisis package aimed at infrastructure and public works to combat the effects of falling export prices.

The former head of the statistics institute, Farid Matuk, says the formal growth figure only represents reality for Peru’s upper crust. Despite soaring commodity prices that prompted growth rates of 8 percent in 2006, 8.9 percent in 2007 and 9.8 percent in 2008, job growth in Peru as a whole was stagnant, Matuk said.

Monday’s report said that overall employment from December 2008 through February 2009 fell by 0.7 percent in metropolitan Lima compared to the same period the year before, although it said that formal sector employment grew by 5.1 percent.

Matuk said statistics institute figures show that the number of employed Peruvians in metropolitan Lima who lack a college education has actually fallen by 3 percent since February 2007.

“In the past two years there have not been substantial improvements in household living standards and employment is basically stagnant because growth has been based on raw materials” like mining and other non-labor intensive industries, Matuk told the Associated Press.

Sat Nov 30, 2002 2:05 pm

La dolce vita: Is Peru’s feelgood factor spreading?
By Jude Webber

PACHACUTEC, Peru, Nov 29 (Reuters) – President Alejandro Toledo
boasts that more Peruvians are going to the movies — a sure sign,
he says, that improving gross domestic product statistics mean la
dolce vita (the sweet life) is spreading.

Economists call it the trickle down effect — and trickle is the
word, say residents in Pachacutec, a sprawling shanty town of straw
and wood huts on the sandy hills sloping up from the Pacific Ocean
at the northern fringe of Peru’s capital.

“Things are gradually getting better. Two months ago I didn’t
have a job. Now I have some work … It’s not great but I can pay
for my daughter’s school,” said construction worker Emerson
Reategui, 42, on his way with a sheaf of documents to apply for
official property rights to his shanty town home.

“We’ve got to give Toledo more time to work. He’s made a lot of
promises. In Pachacutec we feel he’s keeping them slowly — but he
is keeping them in things like property rights, which he’s starting
to give, and job projects,” said Carlos Ricaldi, 28, in his small
but well-stocked store. “I see progress.”

Toledo, who took office in July 2001 promising more jobs and
prosperity, hails Latin America’s No. 7 economy as the region’s
darling this year, saying international markets made their feelings
plain by clamoring for a $500 million bond that Peru sold this week
to raise cash to plug its budget deficit.

Although the issue meant Peru beefing up its borrowing just as
Argentina’s multiple debt defaults and Brazil’s ability to manage
its $260 billion public debt have worried markets, economists were
cheered by the relatively cheap interest rates it won.

Peru expects its 2003 debt servicing costs to rise to $2.2
billion from around $2 billion now, but trumpets its nearly $10
billion in international reserves as a sign of solidity.

Indeed, the government is so delighted with the health of the
economy — illustrated by ever rosier performance reports, including
an official September growth figure of 7.3 percent — that it has
jacked up its 2002 GDP growth target to 4.2 percent from a previous
3.7 percent.

The acceleration comes after four years of economic woes and
political strife. GDP grew just 0.2 percent last year.
And the head of the government’s National Statistics Institute
said this week even those glowing figures were still too low. Farid
Matuk said methodology problems meant Peru had been “systematically
underestimating” its data for years.

Peru is hoping to parlay the good news into closer trade ties
when U.S. Commerce Secretary Don Evans visits next week.

MOVIES, CELLPHONES, SHOPPING
Toledo never tires of telling voters — many of whom are
underwhelmed by his progress in creating jobs in a nation where more
than half the people live on $1.25 a day — that he has sacrificed
his popularity for the sake of economic prudence.

The U.S.-trained business school professor, whose approval rating
has climbed nearly 10 points recently but is still only around the
mid-20s in polls, told reporters this week that rises in the numbers
of moviegoers, cellphone users and supermarket sales showed an
increasing feelgood factor.

“You may ask what (the economy) has got to do with the cinema?
Well, if you’ve got a job, you can go to the movies more,” he
said. “The economy is becoming more dynamic, people are buying more.
It’s slow but things are improving.”

At Peru’s top business forum this week, a partner at a
headhunting firm said trade was picking up, and executives said the
labor intensive construction sector was in full recovery.

But Peru is a country of big contrasts — only one in five people
in Lima do their shopping in supermarkets, as opposed to local
markets, and the gap between rich and poor still yawns.

“The levels of inequality have increased. There is more, but not
more for everyone,” Matuk said.
Elmer Cuba, economist at private consultancy Macroconsult, said
things were picking up slowly “but we still need stronger and more
sustained growth for real salaries to grow, and that process is
going to take years.”

Back in Pachacutec, one measure of quality of life is whether
residents still have plastic drums outside their homes and have to
wait for the water truck to trundle past or whether they can hook up
to new standpipes on their dirt streets.

Residents said European aid agencies or American evangelists, not
the government, had brought the water.
But they credit the government — which says it has created
160,000 jobs in state temporary work schemes — with giving them
jobs as street cleaners and park builders.

“I feel a bit better. Not that much, but I’m less afraid of where
I’m going to get food from than I was before,” said Angelica Azteca,
a 28-year-old housewife and mother-of-three, who spends 15 soles
($4.30) a day on food for her family.

But others were gloomier. “I feel just the same — it seems my
pockets are full of holes. Money goes in and out like water,” said
Luz Malaga, 54. “I think Toledo has good intentions, and that he’s
trying to do something. But don’t they say the road to hell is paved
with good intentions?”

(Additional reporting by Tania Mellado, Eduardo Orozco)
((Lima newsroom, tel: +511 221 2130, fax +511 221 2133, e-mail:
lima.newsroom@reuters.com))