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From Samples to Global Estimates: Extrapolation Methodology

In order to develop estimates of the global scale of organization-based and direct volunteering and its variation among countries at different levels of economic development, it is necessary to blow up our sample to the global population of countries using a common technique known as extrapolation. Because of the nature of the underlying data, we had to deploy two separate extrapolation techniques: one for the organization-based volunteering data generated on 43 countries through our CNP project; and the other for our direct volunteering data generated through our assembly of TUS results for 33 countries. The first data source is used to estimate volunteering through organizations, while the second to estimate direct volunteering.

Organizational volunteering.

Our methodology for estimating volunteering through organizations takes advantage of the data on the NPI workforce in 43 countries assembled by the Johns Hopkins Comparative Nonprofit Sector Project (CNP).[1] For the 43 countries in the CNP data set, we used the actual volunteer shares of EAP reported in Table 2.1 to estimate the actual number of FTE volunteers. These 43 countries on which we have actual observations account for nearly 59 % of the total estimated number of FTE volunteers globally. For the remaining countries, we used a two-step process that made use of a linear least square regression model derived from data on these 43 countries to estimate the aggregate size of the organizational volunteer workforce in the remaining 139 countries of the world not covered by the CNP.[2]

Table 2.2 Average minutes per person per day spent on direct volunteering , by country

Region/Country

Minutes

Developed Western

Australia

6

Austria

6

Belgium

5

Canada

13

Denmark

13

Finland

16

France

7

Germany

15

Ireland

8

Italy

8

Netherlands

20

New Zealand

13

Portugal

9

Spain

9

Sweden

8

United Kingdom

3

United States

17

Eastern Europe

Bulgaria

8

Estonia

14

Latvia

11

Lithuania

13

Poland

14

Slovenia

8

Other Countries

China

3

India

1

Japan

4

Korea, Republic of

1

Mexico

8

Mozambique

6

Pakistan

1

South Africa

2

Thailand

5

Turkey

19

Average

8.9

Standard deviation

5.2

Skewness

0.39

Source: OECD http://www.oecd.org/gender/ data/OECD_ 1564_TUSupdatePortal.xls; Harmonised European Time Use Survey, https://www.h5.scb.se/tus/tus/Statistics.htm ; Government of Pakistan, Statistics Division, Federal Bureau of Statistics, Islamabad

  • [1] For detailed country data and data assembly methodology see Salamon et al., 2004.
  • [2] We used a regression analysis to find our way to a good predictor of the aggregate size of theorganization-based volunteer workforce measured as a share of the economically active population(EAP) in each country. We examined different regression models trying to find one that explainsmost of the cross-country variance on the dependent variable observed in the 43-country dataset.
 
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