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EuCham - European Chamber - 2015-09 Female employment rates and causality analysis of economic growth

     


     
     
     



2015-09 Female employment rates and causality analysis of economic growth

EuCham - European Chamber lists the female employment rates and analyzes the causal relationship between female employment and economic growth using a panel data of 32 countries in the period 2006-2014. The result of the econometric analysis shows that bidirectional or unidirectional causalities exist between female employment and economic growth. This means that female employment levels affect economic growth, and vice versa.

Statistics show that the three highest female employment rates in 2014 were in Iceland, Sweden and Switzerland with 80.5%, 77.6% and 77.4% respectively. Turkey lagged far behind Greece and was at the bottom of the list with 31.6%.

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EuCham Charts
September 2015

 

Female employment rates and causality analysis of economic growth 

 

 

Country

Female employment rate

1

Iceland

80.5%

2

Sweden

77.6%

3

Switzerland

77.4%

4

Norway

77.1%

5

Germany

73.1%

...

   

32

Turkey

31.6%

EuCham data based on Eurostat and the World Bank reports 32 European countries were considered for econometric analysis

 

 

EuCham_Charts_Logo.jpg


  • Iceland ranks at the top of the list with 80.5%. Sweden, Switzerland and Norway also show high female employment rates.  

  • Turkey is at the bottom of the list with 31.6% and has also the largest difference between employment rates by gender.

  • Female employment rates are in general lower than male rates across European countries.



          



                                            Source: eucham.eu/charts

 

Detailed Information

EuCham - European Chamber lists the female employment rates and analyzes the causal relationship between female employment and economic growth using a panel data of 32 countries in the period 2006-2014. The result of the econometric analysis shows that bidirectional or unidirectional causalities exist between female employment and economic growth. This means that female employment levels affect economic growth, and vice versa.

Statistics show that the three highest female employment rates in 2014 were in Iceland, Sweden and Switzerland with 80.5%, 77.6% and 77.4% respectively. Turkey lagged far behind Greece and was at the bottom of the list with 31.6%.

According to the OECD Better Life Index, Iceland is the best performing country and at the top of the list in many measures such as jobs and income, above the average in social connections, health status, environmental quality, personal security, education and skills. However, Turkey ranks below the average in all these measures, apart from civic engagement.The most important requirements for finding a job are good education and skills; all these indicators explain why Turkey is at the bottom of the list and Iceland is the top. 

Turkey also reveals the biggest difference between employment rates by gender. In contrast, there was almost no difference in employment rates by gender in Finland. In order to achieve a sustainable economy, policies should be developed to increase female employment.

The European Union, in a changing world, aims to maintaining a smart, sustainable and inclusive economy. To reach this purpose, female employment should be more effective in the overall employment rate as mentioned in the European Union’s 2020 strategies. In these strategies, it has been highlighted that the female employment rate has increased significantly around the world. However, female and male employment rates have not reached parity in any of the countries observed. Alongside this, equal pay, equality in decision-making, dignity, integrity and gender equality in external actions were also mentioned.

 

Figure 1: Female employment rate map

 

Table 1: Female employment rates in 2014 (%)

 

    Country

Female
employment rate

1

    Iceland

80.5

2

    Sweden

77.6

3

    Switzerland

77.4

4

    Norway

77.1

5

    Germany

73.1

6

    Denmark

72.2

7

    Finland

72.1

8

    United Kingdom

70.6

9

    Estonia

70.6

10

    Lithuania

70.6

11

    Austria

70.1

12

    Netherlands

69.7

13

    Latvia

68.5

14

    France

66.2

15

    Luxembourg

65.5

16

    Czech Republic

64.7

17

    Portugal

64.2

18

    Cyprus

63.9

19

    Slovenia

63.6

20

    Belgium

62.9

21

    Bulgaria

62.0

22

    Ireland

61.2

23

    Hungary

60.2

24

    Poland

59.4

25

    Slovakia

58.6

26

    Romania

57.3

27

    Spain

54.8

28

    Croatia

54.2

29

    Malta

51.9

30

    Italy

50.3

31

    Greece

44.3

32

    Turkey

31.6

 

Figure 2: Difference between female and male employment rates in 2014

 

Table 2: Difference between female and male employment rates in 2014

 

Country

Female (%)

Male (%)

Difference between  rates

1

     Finland

72.1

74.0

-1.9

2

     Lithuania

70.6

73.1

-2.5

3

     Latvia

68.5

73.1

-4.6

4

     Sweden

77.6

82.2

-4.6

5

     Norway

77.1

81.9

-4.8

6

     Iceland

80.5

86.5

-6.0

7

     Bulgaria

62.0

68.1

-6.1

8

     Portugal

64.2

71.3

-7.1

9

     Denmark

72.2

79.5

-7.3

10

     France

66.2

73.7

-7.5

11

     Cyprus

63.9

71.6

-7.7

12

     Estonia

70.6

78.3

-7.7

13

     Slovenia

63.6

71.6

-8.0

14

     Austria

70.1

78.3

-8.2

15

     Belgium

62.9

71.6

-8.7

16

     Germany

73.1

82.3

-9.2

17

     Switzerland

77.4

87.1

-9.7

18

     Croatia

54.2

64.2

-10.0

19

     Spain

54.8

65.0

-10.2

20

     United Kingdom

70.6

81.9

-11.3

21

     Netherlands  

69.7

81.1

-11.4

22

     Ireland

61.2

73.0

-11.8

23

     Luxembourg

65.5

78.4

-12.9

24

     Hungary

60.2

73.5

-13.3

25

     Poland

59.4

73.6

-14.2

26

     Slovakia

58.6

73.2

-14.6

27

     Romania

57.3

74.0

-16.7

28

     Czech Republic

64.7

82.2

-17.5

29

     Greece

44.3

62.6

-18.3

30

     Italy

50.3

69.7

-19.4

31

     Malta

51.9

80.3

-28.4

32

     Turkey

31.6

75.0

-43.4

 

Methodology

All data are derived from Eurostat and The World Bank. In this paper, the causal relationship between female employment and economic growth (Real GDP) were investigated in three steps using Eviews 7 and Stata 11.

Firstly, the Pesaran CDLM test for cross sectional dependence was used, secondly, the Pesaran CADF test was used and thirdly, the Granger causality test was applied after lag order was selected pursuant to information criteria.

There are various tests that analyze cross sectional dependence in panel data. Cross sectional dependence can be identified as a situation in which a shock happens in countries. Such a shock could be an economic crisis that also affects other countries. In this study, the Pesaran CDLM test was used for cross sectional dependence and the test could be used when N>T. Number of countries and period of time are defined as N and T. If cross sectional dependence exists between units, second generation tests should be used for successful forecasting. For this purpose, the Pesaran CADF test was used. This is a test that considers cross-sectional dependence and could be used when N>T.

 

Econometric Results

According to the results in the Table 3, cross- sectional dependency was found in both variables.

After the cross sectional dependency test, Pesaran CADF was used and results in Table 4 show that the first difference of variables rejects the null hypothesis of a unit root. A unit root can cause difficulties in econometric inference. To illustrate the effect of a unit root, first difference of variables can be considered. To test whether there was a causal relationship among the variables, a panel causality test was performed.

The first difference of variables in the Table 5 shows that bidirectional causalities exist between female employment and economic growth. In other words, female employment levels affect economic growth and economic growth also affects female employment levels.

 

Table 3: Pesaran CDLM Test

 

t statistics

Prob

GDP

49.206

0.0000

Female employment

9.988

             0.0000                                                                                                            

 

Table 4: Unit Root Test - Pesaran CADF

Variables

Critical Value

Test Statistics

%1

%5

%10           

t- bar

p-value

         gdp

-2.360

-2.220

-1.902

-1.003

0.158

         dgdp

-2.360

-2.160

-2.050

-2.579

0.005

         emp

-2.360

-2.160

-2.050

2.433

0.993

         demp

-2.360

-2.160

-2.050

-1.840

0.033

dgdp: first difference of gdp

demp: first difference of employment

 

Table 5: Panel Granger Causality Analysis - Period 2006-2014

Lag: 2     •     H0

          Test Statistics

Prob

Female employment
does not affect economic growth

9.49233

0.0001

Economic growth
does not affect female employment

16.0808

0.0000

Source: Eurostat and The World Bank
EuCham Research Department - Compiled by Gülşah Sedefoğlu 2015-08-20

 

 

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