Why growth rates differ

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This cookie is set by the provider Media. I contacted the Development Data Group at the World Bank to enquire about the availability of metadata and the raw data underlying the calculation of the time series of national accounts. In response I was told by the Data Group that. Raw data provided by the National Statistics Agencies are not available for external users and only a handful of people at the World Bank have access to it.

The data series here go back to and there are better metadata available for some indicators. However, for national accounts, the sources and methods listed contain very little information. This meant that I had come full circle. On my third request for the underlying national account files and the sources and methods used, I got a similar response:.

At the Data Group, we do not have the information you've requested. National account data we receive are electronic files either from the Country offices or from the IMF.

May we suggest you contact the National Statistics Offices directly? We do not have statistical bulletins or any national source publications. Country authorities send data to us electronically in files that we do not share with the public. In a last attempt to break the circle I contacted the compilers of the Penn World Tables PWT for access to the underlying data series, but to no avail. Since the United Nations database only holds data from onwards, I asked which sources were used before this date and was told:.

For the data before , we applied the growth rate of the variable from our old national data to the new data of to extrapolate the missing data, e. Before PWT6. In the course of my Ph. D research on four countries Botswana, Kenya, Tanzania, and Zambia , I found that annual GDP growth rates from World Development Indicators, Penn World Tables, Maddison, and official national accounts data varied so much as to bring definitive statements of the comparative growth experience into doubt.

In order to deal with the uncertainty surrounding the evidence on growth, the thesis consulted the primary sources for growth data: national accounts for Botswana, Kenya, Tanzania, and Zambia Jerven d. As noted, one source reports the other as being its main source, and vice versa. The series are loosely based on national account data files, but it is not clear which sources are used and how these are assembled into continuous time series.

The primary evidence comes from the official national accounts data. The key difference between the official national accounts data and the data available from the other databases is that the national accounts do not provide a continuous series for the whole post-colonial period. The data available from the international series have largely been passed on from governments and statistical bureaus and may have then been modified, harmonised to fit the purpose of the data retailer and their customers.

The data should therefore be considered secondary sources. It is unsatisfactory to work with data where no proper sources are given and there is no immediate indication as to why different sources disagree.

The inaccuracy underlines the importance of consulting primary evidence in economic analysis. The growth observations in the databases bridge years in which no official data were available, and where different base years were used. The only way inconsistencies in the data and effects of revisions can be dealt with satisfactorily is by consulting the primary source.

The advantage of using national accounts is that they come with guidelines and commentaries. When the underlying methods or basic data involved in assembling the accounts are changed, these changes are reported. A major inconvenience with respect to national accounts data is that they are not readily downloadable. The publications have to be collected manually, and then the process of data entry and interpretation follow.

Thus my study in my thesis and in Poor Numbers was based on research visits to the statistical offices. In each country I read reports and handbooks on methodology and I supplemented information gained with consultations with the representatives of central statistical offices.

There is other research into the variation in data quality in the datasets. Johnson et al. Cicconi and Jarocinski studied the impact of the difference in the growth data in the PWT following the 6. They concluded that margins of error in international income estimates appeared too large for agnostic growth empirics to be deemed robust. Jerven did the same analysis for GDP level estimates for the African sub-sample and found that variation in ordinal ranking according to GDP level estimates was surprisingly large.

Why was there so much disagreement in the GDP level data? In part, the systematic variation in values reflects the fact that the income per capita measures are quoted in international dollars from different base years in Maddison, WDI, and PWT. Furthermore, the income estimates reported in the datasets differ because different formulae were used to calculate the purchasing power parity PPP values used in international price comparisons.

However, the methods applied to express the income estimates are quite similar and should not alone account for such differences in ranking. The international GDP per capita datasets all take the national account files as provided by the appropriate statistical agencies as a starting point, although they may use them differently.

They may, moreover, impute data in different ways in different cases. Of course, the datasets necessarily inherit all the data quality problems originating in the country. Thus, many of the numbers may be misleading to begin with. However, the way they are generated through secondary operations upon this data may create new difficulties. While all the databases have the same starting point, they make use of different national account series, before or after some revisions.

Maddison actually did primary research in some cases, and generated his own national account estimates from archival data. We would thus expect United Nations and World Bank data to be correlated, because both draw on national statistical sources. These expectations have been confirmed Jerven d. There are differences not only in methods but also in the actual primary data used to create the harmonised series. The differences can be quite substantial depending on when the database was last updated, and which GDP data series was used.

This paper contributes to the existing literature by analysing the three sources of dissonance in the data series: revisions to the benchmark years in GDP series; clerical errors, oversights, and lack of quality control when putting series together; and the role of negotiation and the political contestation of the numbers. Outright dissembling or wilful misrepresentation is less common. Most of the problems of getting the right numbers from countries stem from sheer lack of data and resources, and general ignorance, although sometimes politics matters too.

Overnight, the size of the economy was adjusted upward by over 60 per cent. While this change in GDP was exceptionally large, it turned out to be far from an isolated case. These well-publicised statistical events have increased the attention being paid to the quality of macroeconomic statistics in low-income countries, especially in Africa.

In a survey of the status of GDP statistics in Sub-Saharan Africa Jerven , it was found that, of 37 countries studied, only six followed the advice of the IMF to use a base year that was no more than five years old i.

This survey of 45 countries produced similar results, with only four countries meeting the five-year rule. Making matters worse was that, while 28 countries had base years more than 10 years old, 13 countries were still using base years more than 20 years old. The African Development Bank conducted a survey in published in April and, intriguingly, their report had different findings on the closeness of the base years, even though the surveys were both conducted in the spring of Jerven a.

Depending on when and how these changes are implemented, these revisions in GDP estimates cause changes in the different databases at different times.

The changes in growth rates can be observed by comparing old and new versions of the same database. Since , the WDI has archived older versions of their own database, allowing one to observe this difference more easily.

Figure 1 plots the growth series in Nigeria as published in April and as published in April The graphs make clear that most of the upward revision in GDP resulted in an apparent watershed in economic growth in a wholly implausible 34 per cent , one that did not exist previously when growth was recorded as an already robust 10 per cent. Altogether, the average picture of growth in Nigeria has not changed much on average since 4.

It is the distribution of growth over time that has changed. The new series reports an 8. It is difficult, based on the sparse information that is available about how this data series was updated, to determine why these changes happened so far in the past. Meanwhile, the GDP series at fixed prices as published by the NBS in indicates growth of 3 per cent in , which agrees with the WDI series, but is 12 percentage points greater than is reported in the WDI series.

Similarly, it reports zero growth in , which agrees with the WDI series but is 11 percentage points greater than reported in the WDI series. It is difficult to tell what the source of these differences is from the data provided by the World Bank. It is hard to believe that assigning different sectoral weights the Nigerian economy was less petroleum-dependent and more tertiary-sector-dependent in than in would only have had an impact in these years.

The changes we see in the series appear in order to reconcile past levels deriving from an old series with present levels deriving from a new one. For investors, growth rates typically represent the compounded annualized rate of growth of a company's revenues, earnings, dividends, or even macro concepts, such as gross domestic product GDP and retail sales.

Expected forward-looking or trailing growth rates are two common kinds of growth rates used for analysis. At their most basic level, growth rates are used to express the annual change in a variable as a percentage. An economy's growth rate, for example, is derived as the annual rate of change at which a country's GDP increases or decreases.

This rate of growth is used to measure an economy's recession or expansion. If the income within a country declines for two consecutive quarters, it is considered to be in a recession. Conversely, if the country has grown its income for two consecutive quarters, it is considered to be expanding.

Growth rates can be calculated in several ways, depending on what the figure is intended to convey. The economic growth rate for a country's GDP can thus be computed as:. This approach, however, may be overly simplistic. A common modification is the compound annual growth rate CAGR, which is not a true return rate, but rather a representation that describes the rate at which an investment would have grown if it had grown the same rate every year and the profits were reinvested at the end of each year.

The formula for calculating CAGR is:. The CAGR calculation assumes that growth is steady over a specified period of time. CAGR is a widely used metric due to its simplicity and flexibility, and many firms will use it to report and forecast earnings growth.

Financial theory suggests that a company's shares can be fairly valued using a dividend discount model DDM , based on the hypothesis that present-day price is worth the sum of all of its future dividend payments when discounted back to their present value.

As a result, dividend growth rates are important for valuing stocks. The Gordon Growth Model GGM is a popular approach used to determine the intrinsic value of a stock based on a future series of dividends that grow at a constant rate. This dividend growth rate is assumed to be positive as mature companies seek to increase the dividends paid to their investors on a regular basis. Knowing the dividend growth rate is thus a key input for stock valuation.

Growth rates are utilized by analysts, investors, and a company's management to assess a firm's growth periodically and make predictions about future performance. Most often, growth rates are calculated for a firm's earnings, sales, or cash flows, but investors also look at growth rates for other metrics, such as price-to-earnings ratios or book value, among others.

When public companies report quarterly earnings, the headline figures are typically earnings and revenue, along with the growth rates—quarter over quarter or year over year—for each. The internal growth rate IGR is a specific type of growth rate used to measure an investment's or project's return or a company's performance.

It is the highest level of growth achievable for a business without obtaining outside financing, and a firm's maximum internal growth rate is the level of business operations that can continue to fund and grow the company. Because stock prices are thought to reflect the discounted value of a firm's future cash flows, a rising stock market implies improving forecasted growth rates for the company.

Specific industries also have growth rates. Each industry has a unique benchmark number for rates of growth against which its performance is measured. For instance, companies on the cutting edge of technology are more likely to have higher annual rates of growth compared to a mature industry such as retail. Industry growth rates can be used as a point of comparison for firms seeking to gauge their performance relative to their peers.

The use of historical growth rates is one of the simplest methods of estimating the future growth of an industry. However, historically high growth rates do not always indicate a high rate of growth looking into the future as industrial and economic conditions change constantly and are often cyclical.