DESINVERSIONES EN COMPAÑÍAS
C) Principales tendencias de la industria
2. Tendencias del segmento de las líneas aéreas
The final component element of the SES of women is female labour force participation. As with maternal health and female education, I created a scaled index consisting of the following female labour force participation indicators:
• Female agricultural employment • Female industry employment • Female service employment
• Labour force participation females 15-24 • Labour force participation females 15-64 • Labour participation rate female 15+
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All indicators were already expressed in terms of percent, so no rescaling was required. Once the index was created as a single variable, I then integrated this variable into the various two- step models for analysis. Table 6.46, 6.47 and 6.48 illustrates the results of testing the impact of IMF programmes upon the labour force participation of women as expressed by
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Table 6.43: The effect of IMF participation on Female Labour Force Participation index
Dependent Variable Female Labour Force Participation Index Pooled Data Democracies Autocracies
Independent Variable Participation in IMF Programme (IMF Programme)
No of Obs: 1014 Wald chi² (157) = 5165.56 Prob > chi² = 0.000 Rho = 0.03538 No of Obs: 601 Wald chi² (100) = 2250.87 Prob > chi² = 0.000 Rho = 0.08295 No of Obs: 407 Wald chi² (71) = 2247.79 Prob > chi² = 0.000 Rho = -0.00882
Variable Description Coefficient
Standard Error Coefficient Standard Error Coefficient Standard Error
Participation on an IMF programme -0.21 -0.72 -0.51 -0.96 -0.08 -1.18
Participation on an IMF programme (t-1) 4.45* -2.404 3.82 -3.217 7.71 -6.885
GDP per capita (t-1) 0 0 0 0 0 0
Log of GDP per capita (t-1) -0.35*** -0.107 -0.37*** -0.139 -0.22 -0.271
GDP (t-1) 0 0 0 0 0 0
Log of GDP (t-1) 0.1 -0.069 0.06 -0.09 0.41* -0.24
GDP Growth (t-1) 0.05** -0.023 0.02 -0.066 0.05 -0.029
Log of GDP Growth (t-1) -0.40*** -0.129 -0.38* -0.202 -0.31 -0.234
Reserves as a percentage of GDP (t-1) 0 0 0 0 0 0
Participation in IMF programmes (t-1) * GDP Per capita (t-1) 0 0 0 0 0 0
Participation in IMF programmes (t-1) * Log of GDP Per capita 0.17 -0.202 -0.07 -0.279 0.61 -0.443
Participation in IMF programmes (t-1) * GDP 0 0 0 0 0 0
Participation IMF programmes (t-1) * Log of GDP (t-1) -0.17 -0.112 -0.09 -0.152 -0.43 -0.293 Participation IMF programmes (t-1) * GDP Growth (t-1) -0.04 -0.057 -0.02 -0.104 -0.01 -0.81 Participation in IMF programmes (t-1) * Log of GDP Growth (t-1) 0.39 -0.256 0.46 -0.384 0.14 -0.412
Participation IMF programmes (t-1) * Total Reserves (t-1) 0 0 0 0 0 0
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Table 6.44: The effect of IMF Programme Implementation on Female Labour Force Participation index
Dependent Variable Female Labour Force Participation Index Pooled Data Democracies Autocracies
Independent Variable Participation in IMF Programme (IMF Programme)
Insufficient data for model to return results
Insufficient data for model to return results
No of Obs: 144 Wald chi² (41) = 878.02
Prob > chi² = 0.000 Rho = -0.86776
Variable Description Coefficient
Standard Error Coefficient Standard Error Coefficient Standard Error
Participation on an IMF programme 6.29 -3.84
Participation on an IMF programme (t-1) -5341.11*** -4.346
GDP per capita (t-1) 4.44*** -0.089
Log of GDP per capita (t-1) -1170.30** -4.059
GDP (t-1) 0 0
Log of GDP (t-1) 0.01 -0.174
GDP Growth (t-1) .003 -0.072
Log of GDP Growth (t-1) -0.18 0.337
Reserves as a percentage of GDP (t-1) 0 0
Participation in IMF programmes (t-1) * GDP Per capita (t-1) -4.44*** -0.089
Participation in IMF programmes (t-1) * Log of GDP Per capita -1170.68*** -4.076
Participation in IMF programmes (t-1) * GDP 0 0
Participation IMF programmes (t-1) * Log of GDP (t-1) 0 0
Participation IMF programmes (t-1) * GDP Growth (t-1) 0 0
Participation in IMF programmes (t-1) * Log of GDP Growth (t-1) 0 0
Participation IMF programmes (t-1) * Total Reserves (t-1) 0 0
Implementation Levels (t-1) 0 -0.006
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Table 6.45: The effect of IMF Programme Design on Female Labour Force Participation index
Dependent Variable Female Labour Force Participation Index Pooled Data Democracies Autocracies
Independent Variable Participation in IMF Programme (IMF Programme)
Insufficient data for model to return results
Insufficient data for model to return results
No of Obs: 143 Wald chi² (41) = 875.39
Prob > chi² = 0.000 Rho = -0.70029
Variable Description Coefficient
Standard Error Coefficient Standard Error Coefficient Standard Error
Participation on an IMF programme 4.67 -3.42
Participation on an IMF programme (t-1) -5,402.02*** -4.345
GDP per capita (t-1) 4.5*** 0
Log of GDP per capita (t-1) -1,184.20*** -4.065
GDP (t-1) 0 0
Log of GDP (t-1) 0.01 -0.171
GDP Growth (t-1) 0.04 -0.071
Log of GDP Growth (t-1) -0.25 -0.339
Reserves as a percentage of GDP (t-1) 0 0
Participation in IMF programmes (t-1) * GDP Per capita (t-1) -4.5*** -0.089
Participation in IMF programmes (t-1) * Log of GDP Per capita 1,184.50*** -4.081
Participation in IMF programmes (t-1) * GDP 0 0
Participation IMF programmes (t-1) * Log of GDP (t-1) 0 0
Participation IMF programmes (t-1) * GDP Growth (t-1) 0 0
Participation in IMF programmes (t-1) * Log of GDP Growth (t-1) 0 0
Participation IMF programmes (t-1) * Total Reserves (t-1) 0 0
Ratio of QPC Conditions to SPC Conditions 0.01** 0
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The results displayed in Table 6.43, 6.44 and 6.45 where enough data is present, point towards IMF programmes having no statistically significant impact on female labour force participation as represented by the labour force index. Table 6.43 considers participation in an IMF programme, while Table 6.44 incorporates an additional control for implementation levels and Table 6.45 controls for programme design.
To understand whether there are regional differences, I have also used a model to test for any regional variations. Below I have presented a selection of notable results in Tables 6.46, 6.47 and 6.48. These results illustrate that there are indeed regional variations present in how IMF programmes impact the female labour force participation index. The only notable result is from the Arab States where implementation of an IMF has a statistically significant negative impact on female labour force participation as represented by the female labour force index. However, it is important to point out the very low number of observations.
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Table 6.46: The effect of IMF Programme participation on female labour force index, Regional View Dependent
Variable
Femal Labour force index
Asia Pac Europe / Nth America Africa Latin America CIS Arab States
Independent Variable Participation in IMF Programme (IMF Programme) No of Obs: 164 Wald chi² (27) = 741.12 Prob > chi² = 0.000 Rho = 0.06353 No of Obs: 243 Wald chi² (42) = 1388.84 Prob > chi² = 0.000 Rho = 0.17025 No of Obs: 329 Wald chi² (50) = 1752.76 Prob > chi² = 0.000 Rho = -0.27227 No of Obs: 152 Wald chi² (28) = 472.06 Prob > chi² = 0.000 Rho = 0.20885 No of Obs: 50 Wald chi² (11) = 75.64 Prob > chi² = 0.000 Rho = -0.02875 No of Obs: 76 Wald chi² (15) = 159.39 Prob > chi² = 0.000 Rho = 1.000
Variable Description Coefficient
Standard Error Coefficient Standard Error Coefficient Standard Error Coefficient Standard Error Coefficient Standard Error Coefficient Standard Error
Participation on IMF programme -1.34 -1.637 -0.66 -1.818 1.73 -1.157 -1.57 -1.85 -3.02 -2.83 0.23 -4.154 Participation on IMF programme (t-
1) 3,219.41*** -22.185 19.69 -20.002 -3.6 -6.596 21.8 -15.36 310.18 0 1,476.07 0
GDP per capita (t-1) -0.33 -0.768 0 0 0 0 0 -0.001 -0.01 -0.258 -0.03 -8.129
Log of GDP per capita (t-1) 383.55 -290.306 -1.15** -0.474 -0.37 -0.387 0.99 -1.771 81.04 0 -70.36 0
GDP (t-1) 0 0 0 0 0 0 0 0 0 0 0 0
Log of GDP (t-1) 44.15 -64.191 0.32 -0.281 0.18 -0.17 0.52 -0.33 -23.77 -21.715 93.93 -402.336 GDP Growth (t-1) 3.89 -29.411 -0.11 -0.151 0.02 -0.072 -0.8 -0.492 13.58 -13.748 0.05 -750.918 Log of GDP Growth (t-1) -16.51 -50.855 -0.11 -0.29 -0.21 -0.413 1.24 -1.205 -59.54 -59.735 3.56 0
Reserves as percentage of GDP (t-1) 0 0 0 0 0 0 0 0 0 0 0 0
Participation in IMF programmes (t-
1) * GDP Per capita (t-1) 0.35 -0.768 0 -0.001 0 0 0 -0.001 -0.02 -0.261 -0.08 0
Participation in IMF programmes (t-
1) * Log of GDP Per capita -386.35 -290.344 -0.79 -2.099 0.65 -0.509 -1.76 -1.973 -51.54* -30.158 145.19 0 Participation in IMF programmes (t-
1) * GDP 0 0 0 0 0 0 0 0 0 0 0 0
Participation IMF programmes (t-1)
* Log of GDP (t-1) -43.23 -64.194 -0.64 -0.514 0.09 -0.295 -0.42 -0.384 5 -17.683 -99.7 0 Participation IMF programmes (t-1)
* GDP Growth (t-1) -2.93 -29.421 0.26 -0.356 -0.03 -0.096 0.87* -0.521 -11.99 -12.014 0.03 0 Participation in IMF programmes (t-
1) * Log of GDP Growth (t-1) 8.7 -51.159 0 -1.048 0.34 -0.51 -1.33 -1.326 43.25 0 -3.73 0 Participation IMF programmes (t-1)
* Total Reserves (t-1) 0 0 0 0 0 0 0 0 0 0 0 0
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Table 6.47: The effect of IMF Programme Implementation on female labour force index, Regional View Dependent
Variable
Female Labour Force Index
Asia Pac Europe / Nth America Africa Latin America CIS Arab States
Independent Variable
Participation in IMF Programme (IMF Programme)
Insufficient data for model to return results
Insufficient data for model to return results
No of Obs: 187 Wald chi² (37) = 792.54 Prob > chi² = 0.000 Rho = -0.65299 No of Obs: 60 Wald chi² (21) = 212.32 Prob > chi² = 0.000 Rho = -0.85304
Insufficient data for model to return results
Insufficient data for model to return results
Variable Description Coefficient
Standard Error Coefficient Standard Error Coefficient Standard Error Coefficient Standard Error Coefficient Standard Error Coefficient Standard Error
Participation on IMF programme 4.94 -3.628 8.46 -6.585
Participation on IMF programme (t-1) 3,979.23*** -5.351 -366.54 0
GDP per capita (t-1) -4.67*** -0.063 0.03 -0.125
Log of GDP per capita (t-1) 927.40*** -2.652 -57.25 -39.581
GDP (t-1) 0.00*** 0 0 0
Log of GDP (t-1) 0.29 -0.241 0.14 -0.224
GDP Growth (t-1) 0 -0.066 0.13 -0.19
Log of GDP Growth (t-1) 0.03 -0.315 -0.2 -0.608
Reserves as percentage of GDP (t-1) 0 0 0 0
Participation in IMF programmes (t-
1) * GDP Per capita (t-1) 4.67*** -0.063 -0.03 -0.125
Participation in IMF programmes (t-
1) * Log of GDP Per capita -927.08*** -2.668 55.65 -39.56
Participation in IMF programmes (t-
1) * GDP -0.00***
Participation IMF programmes (t-1) *
Log of GDP (t-1)
Participation IMF programmes (t-1) *
GDP Growth (t-1)
Participation in IMF programmes (t-
1) * Log of GDP Growth (t-1)
Participation IMF programmes (t-1) *
Total Reserves (t-1)
Implementation Levels (t-1) 0.01 -0.006 -0.01 -0.008
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Table 6.48: The effect of IMF Programme Design on female labour force index, Regional View Dependent
Variable
Female Labour Force Index
Asia Pac Europe / Nth America Africa Latin America CIS Arab States
Independent Variable
Participation in IMF Programme (IMF Programme)
Insufficient data for model to return results
No of Obs: 38 Wald chi²(15 ) = 253.89 Prob > chi² = 0.000 Rho = 0.54999 No of Obs: 187 Wald chi² (37) = 809.05 Prob > chi² = 0.000 Rho = -0.39748
Insufficient data for model to return results
No of Obs: 28 Wald chi² (10) = 43.76
Prob > chi² = 0.000 Rho = -0.22607
Insufficient data for model to return results
Variable Description Coefficient
Standard Error Coefficient Standard Error Coefficient Standard Error Coefficient Standard Error Coefficient Standard Error Coefficient Standard Error
Participation on IMF programme 2.92 -3.637 -6.25 -4.825
Participation on IMF programme (t-1) 4,471.78*** -5.375
GDP per capita (t-1) -5.25*** -0.069 -0.07 -0.138
Log of GDP per capita (t-1) 1,042.03*** -2.881 127.37** -62.886
GDP (t-1) 0.00*** 0 0 0 Log of GDP (t-1) 0.3 -0.244 - 129.02*** -20.288 GDP Growth (t-1) 0 -0.063 1.69 -35.013 Log of GDP Growth (t-1) 0.06 -0.305 -33.45 -281.155 Reserves as percentage of GDP (t-1) 0 0 0 0
Participation in IMF programmes (t-
1) * GDP Per capita (t-1) 5.25*** -0.069
Participation in IMF programmes (t- 1) * Log of GDP Per capita
-
1,041.77*** -2.893
Participation in IMF programmes (t-
1) * GDP -0.00*** 0
Participation IMF programmes (t-1) *
Log of GDP (t-1)
Participation IMF programmes (t-1) *
GDP Growth (t-1)
Participation in IMF programmes (t-
1) * Log of GDP Growth (t-1)
Participation IMF programmes (t-1) *
Total Reserves (t-1)
Programme Design (t-1) 0.01 -0.005 0.37* -0.204
166 2. Female labour force participation indicators
While the results do not point towards any notable impact of IMF programmes upon the labour force participation of women it is not sufficient to conclude here. These findings demand a greater investigation. Is female labour force participation in specific sectors impacted? How do these findings compare with the impact that IMF programmes have upon male labour force participation? It is relevant and important to explore whether the impact of women’s labour force participation differs from that of men’s. To gain a greater insight I also tested my model against a number of male labour force participation indicators.
Appendices 9.61 – 9.102 present the results from these tests. Below, in Table 6.49, I list each of the tables and the specific results they present. For a comparative perspective, I have also included results for male agricultural, industry and service employment as well as male employment of men over 15 and between the ages of 15-24 and 15–64 as there is value to be found in exploring whether there is variation in the impact that IMF programmes have across genders. The results presented include country fixed effects.
Table 6.49 - List of tables presenting results of Impact of IMF programmes on education
9.61 The impact of IMF Programme participation on female labour force participation index 9.62 The impact of IMF Programme implementation on female labour force participation index 9.63 The impact of IMF Programme design on female labour force participation index
9.64 The impact of IMF Programme participation on male labour force participation index 9.65 The impact of IMF Programme implementation on male labour force participation index 9.66 The impact of IMF Programme design on male labour force participation index
9.67 The impact of IMF Programme participation on Female agricultural employment 9.68 The impact of IMF Programme implementation on Female agricultural employment 9.69 The impact of IMF Programme design on Female agricultural employment
9.70 The impact of IMF Programme participation on male agricultural employment 9.71 The impact of IMF Programme implementation on male agricultural employment 9.72 The impact of IMF Programme design on male agricultural employment
9.73 The impact of IMF Programme participation on Female industrial employment 9.74 The impact of IMF Programme implementation on Female industrial employment 9.75 The impact of IMF Programme design on Female industrial employment
9.76 The impact of IMF Programme participation on male industrial employment 9.77 The impact of IMF Programme implementation on male industrial employment 9.78 The impact of IMF Programme design on male industrial employment
9.79 The impact of IMF Programme participation on Female service employment 9.80 The impact of IMF Programme implementation on Female service employment 9.81 The impact of IMF Programme design on Female service employment
9.82 The impact of IMF Programme participation on male service employment 9.83 The impact of IMF Programme implementation on male service employment 9.84 The impact of IMF Programme design on male service employment
9.85 The impact of IMF Programme participation on Female employment age 15-24 9.86 The impact of IMF Programme implementation on Female employment age 15-24 9.87 The impact of IMF Programme design on Female employment age 15-24
9.88 The impact of IMF Programme participation on Female employment age 15-64
9.89 The impact of IMF Programme implementation on Female employment age 15-64
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9.91 The impact of IMF Programme participation on male employment age 15-24
9.92 The impact of IMF Programme implementation on male employment age 15-24
9.93 The impact of IMF Programme design on male employment age 15-24 9.94 The impact of IMF Programme participation on male employment age 15-64 9.95 The impact of IMF Programme implementation on male employment age 15-64 9.96 The impact of IMF Programme design on male employment age 15-64
9.97 The impact of IMF Programme participation on Female labour force participation aged 15 plus 9.98 The impact of IMF Programme implementation on Female labour force participation aged 15
plus
9.99 The impact of IMF Programme design on Female labour force participation aged 15 plus 9.100 The impact of IMF Programme participation on male labour force participation aged 15 plus 9.101 The impact of IMF Programme implementation on male labour force participation aged 15
plus
9.102 The impact of IMF Programme design on male labour force participation aged 15 plus
Firstly, these results suggest that IMF programmes have a statistically significant impact on several aspects of female labour force participation. The results of the sectoral analysis show that participation in IMF programmes has no impact on female labour force participation in the agricultural (Appendices 9.67, 9.68 and 9.69) or service sectors (Appendices 9.79, 9.80 and 9.81) however, participation in an IMF programme does have a statistically significant and negative impact on female labour force participation in the industrial sector (Appendix 9.73) reducing this by almost 1 percent.
Secondly, regime type does seem to drive variation. Participation in an IMF programme also has a statistically significant negative impact on female labour force participation of women over 15 (Appendix 9.99) and also between the ages of 15–24 (Appendix 9.85) and 15-64 (Appendix 9.88). For each of these categories female labour force participation is reduced by almost 1 percent as a result of participating in an IMF agreement, but only in autocracies. Participation in an IMF agreement has no statistically significant impact on these indicators in either democracies or the pooled data. It is notable to see that the biggest impact is among younger women in the 15–24 age group.
Thirdly, just as with the results from testing maternal health and education indicators, controlling for implementation and programme design matters. On controlling for implementation levels and programme design focus, all statistical significance is lost. This confirms that it is not sufficient to consider participation alone to understand the impact IMF programmes may have and that different forces are at play at each level of interaction with the IMF.
These results have a number of implications. Firstly, the years between the ages of 15 and 24 are a crucial time for women to develop their economic independence. This negative impact of IMF programmes on labour force participation during these years suggest that the reduction happens early on in the female life and that perhaps, fewer women are entering the workforce in the first place. This jeopardises women’s ability to ever attain economic
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independence. In countries where women’s role is seen primarily as a homemaker and mother, as it is in many autocracies, such an impact could copper fasten this role and the societal view that women are best placed working in the home. Secondly, such a negative impact could contribute to a greater difficulty for women to move towards to more senior decision-making roles in their employment. With fewer women entering the workforce and a greater emphasis on remaining at home, the place of men as senior decision makers in employment is strengthened. Thirdly, on comparing the impact that participation in an IMF programme has upon labour force participation between men and women, the results of the testing point towards a disproportionately negative impact on women in autocracies. This is important as it illustrates that there is variation in how participation in an IMF programme affects women and men.
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6.6 Summary
These findings on the relationships between IMF programmes and the SES of women as represented by maternal health, female education and labour force participation of women (and men) are crucial. I have argued that the IMF may harm women’s SES in countries under their programmes, particularly if their programmes are loaded with spending cuts and lacking structural reforms. Moreover, I have argued that the level of harm may be greater than in countries in similar circumstances that are not under such an IMF programme. However, I have found that on balance IMF programmes are often not strongly associated with harmful effects on women’s SES. While there is some evidence of a negative impact on health, and across several, health, labour market, and education sub-indicators, these statistical associations are often not robust to the inclusion of programme design and programme implementation levels in the statistical tests. When the models incorporate information about the design of an IMF programme and the extent to which a borrowing country has implemented programme content, differences in women’s SES no longer diverges from countries in similar economic circumstances that are not under an IMF programme.
This illustrates firmly that it is not enough for research to consider only participation in an IMF agreement when exploring the impact that the IMF can have. It is essential that, along with participation, programme implementation and design need to be considered or the findings from the statistical analysis may be flawed. Where research fails to account for implementation levels or programme design, it will find itself without robust or nuanced outcomes, and its recommendations then become inappropriate and unsuitable.
My results are important to policy makers as they illustrate the importance of the design and implementation of an IMF programme. The focus of the programme design and the levels of implementation of a programme can drive variation in the impact of IMF programmes upon important socio-economic areas. Such a finding can empower states and the IMF to ensure that programme design is appropriate and that implementation of the programme is prioritised so that negative impacts upon important public policy areas are minimised. Additionally, my results also point towards different processes happening in autocracies and democracies, and this is visible across participation, programme design and programme implementation.
Therefore, my overall finding is that while IMF programmes have the potential to be harmful they are not typically harmful on average, and that differences in programme design and (or) failure to implement potentially harmful programmes are two important reasons why we do not see strong associations. Previous studies on the impact of IMF programmes on social outcomes often omit these two key variables and may have may reach flawed conclusions as a consequence.
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