What relationship exists between a real GDP, a nominal GDP, and a GDP deflator? priced at a standard amount so as to arrive at the relative productive power. Measuring nominal. GDP. 3. Measuring real GDP. 6. Measuring labour input. 7 Growth and productivity are on the policy agenda in most OECD countries. Nominal and Real GDP - Measuring Real National Income MCQ revision money GDP and real GDP - revision video Productivity and Economic Growth.
Moving now to more substantive reasons why one should expect a significant amount of quarter to quarter volatility, one needs to recognize that GDP is estimated based on surveys and other such sources of statistical information. The estimates are not based on a full and complete census of all production each quarter.
Indeed, such an economic census is only undertaken once every five years, and is carried out by the US Census Bureau. One should also recognize that an estimate of real GDP depends on two measures, each of which is subject to sampling and other error. Rather, one estimates what nominal GDP has been based on estimates in current dollars of the value of all economic transactions that enter into GDPand then how much prices have changed.
Price indices are estimated based on the prices of surveyed samples, and the components of real GDP are then estimated from the nominal GDP of the component divided by the relevant price index. Real GDP is only obtained indirectly.
There will then be two sets of errors in the measurements: One for the nominal GDP flows and one for the price indices. And surveys, whether of income flows or of prices, are necessarily partial. Even if totally accurate for the firms and other entities sampled, one cannot say with certainty whether those sampled in that quarter are fully representative of everyone in the economy.
This is in particular a problem which the BEA recognizes in capturing what is happening to newly established firms. Such firms will not be included in the samples used as they did not exist when the samples were set up and the experiences of such newly established firms can be quite different from those of established firms. Indeed, sampling error the fact that two samples will come up with different results simply due to the randomness of who is chosen is probably the least concern.
Rather, systemic issues arise whenever one is trying to infer measures at the national level from the results found in some survey. The results will depend, for example, on whether all the components were captured well, and even on how the questions are phrased. This is an indication that there are systemic issues, and not simply something arising from sample randomness. A small quarterly difference looms large when looked at in terms of annualized rates.
But a relatively small error in the estimates of real GDP in any quarter could still lead to quite substantial volatility in the estimates of the quarter to quarter growth.
Economic activity varies over the course of the year, with predictable patterns. There is a seasonality to holidays, to when crops are grown, to when students graduate from school and enter the job market, and much much more. Thus the GDP data we normally focus on has been adjusted by various statistical methods to remove the seasonality factor, making use of past data to estimate what the patterns are. The importance of this can be seen if one compares what the seasonally adjusted levels of GDP look like compared to the levels before seasonal adjustment.
Note the level of GDP here is for one calendar quarter — it will be four times this at an annual rate: There is a regular pattern to GDP: It is relatively high in the last quarter of each year, relatively low in the first quarter, and somewhere in between in the second and third quarters.
The seasonally adjusted series takes account of this, and is far smoother. Calculating quarterly growth rates from a series which has not been adjusted for seasonality would be misleading in the extreme, and not of much use. But adjusting for seasonality is not easy to do. While the best statisticians around have tried to come up with good statistical routines to do this, it is inherently difficult. A fundamental problem is that one can only look for patterns based on what they have been in the past, but the number of observations one has will necessarily be limited.
If one went back to use 20 years of data, say, one would only have 20 observations to ascertain the statistical pattern. This is not much.
One could go back further, but then one has the problem that the economy as it existed 30 or 40 years ago and indeed even 20 years ago was quite different from what it is now, and the seasonal patterns could also now be significantly different. While there are sophisticated statistical routines that have been developed to try to make best use of the available data and the changes observed in the economy over timethis can only be imperfect.
Indeed, the GDP estimates released by the BEA on July 27 incorporated a number of methodological changes which we will discuss belowone of which was a major update to the statistical routines used for the seasonal adjustment calculations.
Many observers including at the BEA had noted in recent years that seasonally adjusted GDP growth in the first quarter of each year was unusually and consistently low.
It then recovered in the second quarter. This did not look right. One aim of the update to the seasonal adjustment statistical routines was to address this issue. Whether it was fully successful is not fully clear. As seen in the chart at the top of this post which reflects estimates that have been seasonally adjusted based on the new statistical routinesthere still appear to be significant dips in the seasonally adjusted first quarter figures in many of the years comparing the first quarter GDP figures to those just before and just after — i.
This would be more frequent than one would expect if the residual changes were now random over the period. However, this is an observation based just on a simple look at a limited sample. The BEA has looked at this far more carefullyand rigorously, and believes that the new seasonal adjustment routines it has developed have removed any residual seasonality in the series as estimated. The production of the goods and services that make up the flow of GDP will also differ on Saturdays, Sundays, and holidays.
But the number of Saturdays, Sundays, and certain holidays may differ from one year to the next. While there are normally 13 Saturdays and 13 Sundays in each calendar quarter, and most holidays will be in the same quarter each year, this will not always be the case.
What is Nominal GDP? | Calculation | Advantages & Disadvantages
For example, there were just 12 Sundays in the first quarter ofrather than the normal And there will be 14 Sundays in the third quarter ofrather than the normal This could have an impact. Assume, just for the sake of illustration, that production of what goes into GDP is only one-half as much on a Saturday, Sunday, or holiday, than it is on a regular Monday through Friday workday. It will not be zero, as many stores, as well as certain industrial plants, are still open, and I am just using the one-half for illustration.
Using this, and based on a simple check of the calendars for andone will find there were 62 regular, Monday through Friday, non-holiday workdays in the first quarter ofwhile there will be 61 such regular workdays in the first quarter of The number of Saturdays, Sundays, and holidays were 28 in the first quarter of equivalent to 14 regular workdays in terms of GDP produced, assuming the one-half figurewhile the number of Saturdays, Sundays, and holidays will be 29 in the first quarter of equivalent to Thus the total regular work-day equivalents will be 76 in equal to 62 plus 14falling to This will be a reduction of 0.
This is not small. The changes due to the timing of holidays could matter even more, especially for certain countries around the world. In Europe and Latin America, it is customary to take up to a week of vacation around the Easter holidays. The change in economic activity from year to year, with Easter celebrated in one quarter in one year but a different one in the next, will make a significant difference to economic activity as measured in the quarter.
Real GDP and nominal GDP
And in Muslim countries, Ramadan a month of fasting from sunrise to sunsetfollowed by the three-day celebration of Eid al-Fitr, will rotate through the full year in terms of the Western calendar as it is linked to the lunar cycle. Hence it would make sense to adjust the quarterly figures not only for the normal seasonal adjustment, but also for any changes in the number of weekends and holidays in some particular calendar quarter. Eurostat and most but not all European countries make such an adjustment for the number of working days in a quarter before they apply the seasonal adjustment factors.
But I have not been able to find how the US handles this. The adjustment might be buried somehow in the seasonal adjustment routines, but I have not seen a document saying this.
If no adjustment is made, then this might explain part of the quarterly fluctuations seen in the figures. Not only was there extensive work on the seasonal adjustment routines, but there were definitional and other changes.
In this case, if we compare the output of andwe will see that the productivity of is better since the output is more. Now, you may ask how come has become more productive. So, we can only compare as per the current market price. But in reality, the actual growth should be measured by the increase in the quantity produced a year to year.
That can only be done in real GDP.
All you need to understand is current market price and how much quantity the country has produced during a year. As the current market price is easy to know and the quantity produced during the year can be gathered easily, nominal GDP is easy to calculate.
If you look at the nominal gross domestic product of two consecutive years, you would be able to tell just by a glance which year is more productive for the country.
Real GDP and nominal GDP (video) | Khan Academy
And then I could multiply this times the price. So this is this quantity. And then I could multiply it times the price in year one at year one's price.
And this will give me-- so let me just get my calculator out. I should be able to do that one in my head.
But let's see 0. And I get 1, Obviously, I'll round it to 1, So this is equal to 1, And this is an interesting number. So this is-- you could view this as year two's GDP. In year-- or adjusted for-- I'll write it, adjusted for prices, or adjusted for price increases.
Or you could say in year one prices. And what's useful about this is, this says, look, if prices had remained constant, this is what our GDP would have gotten to. If prices did not increase, our GDP would have gotten to this 1, So this area right over here that I'm-- actually, let me do it in a color.
Let me do it in orange, maybe. It really measures the productivity. Now this gives us an interesting, I guess, set of ideas. One idea is to just measure your GDP in the current year's dollars. So this was GDP measured in year two's dollars. It was year two GDP measured in year two dollars, year two prices. So we could call that year two's nominal GDP.