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.

Advertisement

PERU: Upbeat Poverty Stats Questioned

By Milagros Salazar

LIMA, May 29 (IPS) – The Peruvian government has announced that poverty fell by 5.2 percent in a year and forecasts that by 2015, less than 10 percent of the population will be below the poverty line. But experts and provincial governors cast doubt on these figures, given the unmet basic needs of peasant families.

“These poverty figures show that Peru’s economic model is working,” Finance Minister Luis Carranza said on Wednesday after announcing that according to the National Institute of Statistics and Informatics (INEI), the proportion of people living in poverty dropped from 44.5 percent of the population in 2006 to 39.3 percent in 2007.

This means that nearly 1.4 million Peruvians have escaped poverty, and is an improvement on the 42 percent poverty rate projected by the authorities. President Alan García celebrated the result, saying that he had not been over-optimistic when he promised that by the end of his term, in 2011, poverty would be reduced to 30 percent.

“I can tell the country that my aspirations go even further and that by 2015 we will have a poverty rate of less than 10 percent of the population, which means that Peru will no longer be a Third World country,” said the president, making a forecast that exceeds his five-year term of office.

Meanwhile, the proportion of people living in extreme poverty shrank from 16.1 percent to 13.7 percent of the population. The country’s Andean highlands, as opposed to the coastal and Amazon jungle regions of Peru, are home to 67.5 percent of the extreme poor.

The results of the 2007 census, to be released on Jun. 9, will give a more precise idea of how many people have been lifted out of poverty, and what extrapolations can be made, said the head of INEI, Renán Quispe.

President García did not make allowances in his calculations for population growth, which is occurring at a rate of 1.3 percent a year. According to the latest official statistics, 27.2 million people live in Peru.

But Farid Matuk, a former head of INEI, said that the figures given out by the authorities were not credible.

“In spite of the nine percent gross domestic product growth posted in 2007, the García administration could not possibly have managed to reduce poverty by nearly 10 percent in two years, when the previous government of President Alejandro Toledo only achieved a fall of six points in five years,” Matuk told IPS.

The expert also said the García administration has manipulated the figures by increasing the 2005 poverty rate by nearly four percentage points, from 44.5 to 48 percent of the population, by changing the method used to measure poverty.

“These results are completely illogical. I suspect that urban incomes have been inflated in order to show this reduction in poverty,” Matuk said.

That would explain that poverty was reported to have fallen most in urban areas, from 31.2 to 25.7 percent.

But the figures show apparent improvement in rural areas as well. Between 2005 and 2006 rural poverty fell by only 1.6 percent, but in 2007 it was reduced by 4.7 percent.

The highlands region showed the least progress in fighting rural poverty, with a total reduction of only 3.2 percent, while in coastal rural areas poverty dropped by up to 11 percent.

In Matuk’s view, the INEI experts may have overvalued the prices assigned to the food grown by rural families

Since many families, mainly in the rural areas, grow their own food or provide their own essential services, such as water, INEI assigns these goods a value which, in Matuk’s opinion, should be made public, in order to assess the reliability of their figures.

Based on this method, INEI set the poverty line at 229.4 soles (82 dollars) a month per person, and the extreme poverty line at 121.2 soles (43 dollars) a month. Persons consuming less than these amounts are considered poor, or extremely poor, respectively.

“It’s important to know what price was assigned to some foods like eggs and potatoes, and also, for example, what value was established for ‘self-rent’ in marginalised urban areas. So far none of this is known, so the poverty lines are a mystery,” he said.

In response to the criticism, INEI published this information on its web site on Tuesday, and experts are now analysing it. INEI emphasised that it had received advice from the World Bank and several research centres in drawing up its report.

“The results are in compliance with international guidelines, and most importantly, they are transparent,” said World Bank regional director Felipe Jaramillo.

Matuk said that one way of demonstrating that the economic growth achieved between 2006 and 2007 had no impact on the living conditions of the majority of the population is that hunger had only been reduced by just over one percent — “in other words, hardly at all” — over the same period.

For his part, Pedro Francke, an economist at the Pontificia Catholic University, concluded that the method used by INEI did not take into account higher food prices, and was only showing one side of poverty. He said the institute should use a much broader form of measurement that is not only monetary.

“The quality of health and education services that are provided to the population should be measured, as well as whether or not people have identity documents, and what access they have to democracy, for example,” said Francke.

Several provincial governors expressed doubts that poverty reduction in their area could have been as great as the statistics suggest, especially in provinces where historically over 70 percent of the people were considered poor.

“The statistics must have been manipulated, because people are still protesting in the streets due to the fact that they are not seeing the benefits of economic growth. INEI does not measure poverty in villages and towns in the rural areas, where the extreme poor are concentrated,” Hernán Fuentes, the governor of Puno, told IPS.

In his southern Andean region, poverty fell from 76.3 percent to 67.2 percent, according to the official figures.

The poverty rate also fell in Ayacucho, another southern Andean province, from 78.4 to 68.3 percent. “We were sure that poverty had fallen by three or four percent, but not to such an extent. I hope it’s true,” said Governor Ernesto Molina.

Loreto, in the northeast, is the province that apparently made the most progress, with a spectacular 11.7 percent drop in the poverty rate. Governor Iván Vásquez said that such a reduction was indeed possible, but mainly in large cities like Iquitos, the provincial capital, where over half of the population lives.

In Cuzco, however, the poverty rate rose from 49.9 to 57.4 percent. “The social programmes aren’t working, because out of every 10 soles the government allocates to fight malnutrition or poverty, six are swallowed up by bureaucracy,” said Governor Hugo González.

Huancavelica remains the poorest province, with 85.7 percent of the population below the poverty line, after a reduction of barely three percent. ( END/2008 )

http://www.ipsnews.net/news.asp?idnews=42586