Population estimates and projections

Click on Custom Country. A new box will open. Click on the desired countries listed in the country selection panel. Enter the group name in the Enter Group Title box and click on Add. The new country group will be added to the right panel.

To edit an existing country group, click on the Edit link in the current selection panel in right side. Now you can add new countries or remove the countries to an existing customized group.
1. Click on the additional countries listed in the country selection panel.
2. To remove the country from the group double click on the country or select the country and click Remove button.
3. Click on Add to save changes to your customized group.
Note: Editing the group name will create a new custom group.

You can remove the customized group by clicking on the Delete button in the current selection panel in right side

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All Countries Aggregates

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Aggregation Rule

Defines the methodologies to be used when deriving custom aggregates. These rules apply only to custom country groups you have created! Learn More »

Define Aggregation Rule

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Indicator	Rule	Weighted Indicator	%
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Indicator	Rule	Weighted Indicator	%

Custom Indicator

myDataBank allows you to derive your own Custom Indicators from existing series.

Click on Custom Indicators. Choose input indicators by clicking on the desired series in the panel and use the calculator functions to construct your custom indicator formula.

For example, for a series that shows the percentage of female population, double-click on the series Population, Female. Then create a formula by clicking*100/ from the key pad. Then double click on the series Population, Total.

After the formula is complete, you can verify its syntax by clicking the Validate button. Give a name to your custom indicator and click on Add.

Similarly you can create custom indicators such as GDP per Capita as GDP/Population, and annual growth rates for population as AGR(Population, Total), etc. To have "not available" values in the database treated as zero within your formula, use the NA function.

Later if you wish to see or change the formula for an indicator you have created, from the right side current selection panel click the Edit. Use the DEL key to delete the last entry and step backwards to edit the formula. Click the Clear button to erase the custom indicator formula.

Note: Validation will verify a formula for proper syntax only. Derived indicators may yield inappropriate results and caution should be observed.

You can remove the customized indicators by clicking on the Delete button in the current selection panel in right side

Aggregation Rule

The Aggregation Rules function defines the methodologies to be used when deriving custom aggregates. These rules apply only to custom country groups you have created. They do not apply to official groups presented in your selected database.

For each selected series, choose your Aggregation Rule and Weight Indicator (if needed) from the corresponding drop-down boxes. Check the Apply to all box if you wish to use the same methodology for all selected series.

Aggregation Rules include:
1. Max: Aggregates are set to the highest available value for each time period.
2. Mean: Aggregates are calculated as the average of available data for each time period.
3. Mean 66: Aggregates are calculated as the average of available data for each time period. Values are not shown if more than one third of the observations in the series are missing.
4. Median: Aggregates are calculated as the median of available data for each time period.
5. Median 66: Aggregates are calculated as the median of available data for each time period. Values are not computed if more than a third of the observations in the series are missing.
6. Min: Aggregates are set to the lowest available value for each time period.
7. Sum: Aggregates are calculated as the sum of available data for each time period.
8. Sum 66: Aggregates are calculated as the sum of available data for each time period. Sums are not shown if more than one third of the observations in the series are missing.
9. Weighted Mean: Aggregates are calculated as weighted averages of available data for each time period. Select an appropriate weight variable (GNI, population, GDP, exports, imports, labor force or land area) from the Weight Indicator box, as shown above.
10. Weighted Mean 66: Aggregates are calculated as weighted averages of available data for each time period. No aggregate is shown if missing data account for more than one third of the observations in the series. Select an appropriate weight variable (GNI, population, GDP, exports, imports, labor force or land area) from the Weight Indicator box, as shown above.
11. Weighted Mean 66POP: Aggregates are calculated as weighted averages of available data for each time period. No aggregate is shown if countries with missing data represent more than one third of the total population of your custom group. Select an appropriate weight variable (GNI, population, GDP, exports, imports, labor force or land area) from the Weight Indicator box, as shown above.

Note 1: In none of the above methodologies are missing values imputed. Therefore, aggregates for groups of economies should be treated as approximations of unknown totals or average values.

Note 2: Aggregation results apply only to your custom-defined groups and do not reflect official World Bank aggregates based on regional and income classification of economies. Results may be inappropriate (e.g., summing series expressed as a percentage) and caution should be observed when using this function.

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Functions

To perform mathematical functions on the data over a specified time period, choose the type of operation from the dropdown menu, and then choose your time period. Give a name to your custom function and click Add. Note: The operation will be performed on all selected series. You should be aware that some results may be inappropriate (e.g., growth rates on current price series).

Exponential growth rate: the growth rate, r, between two points in time calculated from the equation r = ln(pn/p0)/n, where pn and p0 are the last and first observations in the period, n is the number of years in the period range, and ln is the natural logarithm operator. This growth rate is based on a model of continuous, exponential growth between two points in time. It does not take into account the intermediate values of the series.
Least-squares growth rate: the growth rate estimated by fitting a linear regression trend line to the logarithmic annual values of the variable in the relevant period. No growth rate is calculated if more than half the observations in a period are missing. The calculated growth rate is an average rate that is representative of the available observations over the entire period. It does not necessarily match the actual growth rate between any two periods.
Geometric growth rate: the growth rate over n periods calculated as r = exp[ln(pn/p0)/n] - 1. It is applicable to compound growth over discrete periods. Like the exponential growth rate, it does not take into account intermediate values of the series.
Average (or mean): the sum of values in the period divided by the number of values. Observations that are not available are ignored, however zero values are included.
Median: the middle value in the period.
Maximum: the highest value in the period.
Minimum: the lowest value in the period.
Standard deviation: the square root of the variance. Standard deviation is a measure of how widely values are dispersed from the average value (the mean).
Sum: the sum of the values in the period.
Variance: the mean of all squared deviations from the mean.
Most recent value: The most recent value (MRV) function will display a value in the specified year range according to the selected order (observation sequence). The default order is "First" which will display the most recent value available in the period. Selecting a different order, such as "Second" for example, will display the second-most recent value in the period, etc. MRV options are also available to display the year of the data, either to the left or right of the data value.