Áreas de negocio

There are many factors that influence my dependent variable, the socio-economic status of women and consideration of these influences will allow for a truer understanding of the relationship between the dependent and independent variables or ‘ceteris paribus’. A variety of control variables is appropriate when the socio-economic status of women or one of its disaggregated health, education or labour force participation variables is being considered. 5.5.1 HDI: Human Development Index

The Human Development Index (HDI) is a composite indicator that measures average achievement in key dimensions of human development. First designed by Pakistani economist Mahbub ul Haq and Indian economist Amartya Sen in 1990, it is published annually by the UN.24 _{Sen (1999:24) harks back to Aristotelian theories on the quality of life and Adam Smith’s} ‘necessities and conditions of living’ as he outlines the importance of considering ‘quality of life and substantive freedoms, rather than just on income and wealth’. The HDI considers life expectancy, education and standard of living through GNI (GDP was used in earlier iterations), however, it does not consider inequality, security or poverty. In 2010 a new version of the HDI was developed. Ravallion (2010) highlighted that the new version comes with a relaxation of assumptions of ‘perfect substitutability between its three components’ and restructuring of the weighting systems for the longevity of poor countries relative to rich countries and a move away from the previously equal weighting of the components. Ravallion (2010:6) illustrates that these scaling and weighting issues may obscure successes in a component, using Zimbabwe as an example. Zimbabwe has the lowest HDI value of states covered. A closer look finds that Zimbabwe has an extremely low score in the income component which pulls down the overall average despite having a schooling value 56th _{from the bottom.} However, despite these concerns and considering the positive trade-off of a composite index with data available back to 1990, the HDI proves to be a valuable measurement for researchers and is an appropriate control variable for testing the impact of IMF programmes upon women’s SES, whether health, education or labour force related. The data for the HDI is available from the World Development Indicators (WDI), and I have sourced the data from the World Bank Data Bank to build my dataset.

5.5.2 Health expenditure per capita

The World Bank databank specifies the Health Expenditure per capita indicator25 _{as being the} sum of public and private health expenditure as a ratio of the total population. This value is expressed in $US and covers the provision of health services – preventative and curative, family planning activities nutrition activities and emergency aid. It does not include the provision of water and sanitation. This indicator is an important control variable most especially when analysing the impact of the IV (IMF Participation) on maternal health related

24_{http://hdr.undp.org/en/content/human-development-index-hdi Details of UN HDI}

Indicator

25 _{The details of the health expenditure per capital indicator are to be found here:}

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DV’s. The 1995 Fourth World Conference on Women drew attention to how gender inequalities and lack of spending on women’s health was hampering development, and this provided an impetus to increase focus upon maternal health in the MDG’s later on in 2000. In other academic research Elola, Daponte, and Navarro (1995) in an extensive cross-country statistical analysis of 17 European countries found that health care expenditures were inversely correlated to infant mortality rates and positively correlated to female longevity while Anyanwu and Erhijakpor (2007) in a study of 47 African countries found that health expenditure had a statistically significant effect on infant mortality and under-five mortality. The connection between better health care and a reduction in depreciation of educational capital and thus ultimately increased economic growth is also underscored by Barro (1996). Research by Bloom and Canning (2000, 2003) demonstrates a multitude of benefits to labour force participation and the economy as a result of a healthier workforce, including a reduction of absence due to sickness, an increase in investment in self-education leading to greater productivity along with greater saving and spending potential. This points towards health expenditure being a highly valuable control variable – in particular when considering the impact of the IV (IMF Participation) upon the DV (maternal health). The data for the total health expenditure per capita is available from the World Development Indicators (WDI), and I sourced the data to build the dataset from the World Bank Data Bank.

5.5.3 Education expenditure per capita

The World Bank databank specifies the public expenditure on education as a percentage of total government expenditure26 _{as being the total public education expenditure – both} current and capital as a percentage of the total government expenditure across all sectors in a given year. This value is expressed in $US and includes government spend on both public and private educational institutions, related educational administration and subsidies for private entities such as students/households. This indicator is an important control variable most especially when analysing the impact of the IV (IMF Participation) on education related DV’s. The achievement of universal primary education is set out as Goal 2 in the MDG’s, and significant emphasis is placed on international institutions and governments to attain this goal. There is a substantial body of work exploring the impact of investment in education. Jorgenson and Fraumeni (1992) look at the impact of investment in education upon economic growth in the US finding a positive correlation. They highlight the benefits of such investment citing increased labour force participation and increased income in the labour force which in turn provides for the potential of increased tax revenues. This is echoed in research by Rivera- Batiz (1992) and Iyigun and Owen (1999) who find that greater levels of education improve employment opportunities. Research by Klepinger et al. (1999), Brien and Lillard (1994) and Rosenzweig and Schultz (1989) have established links between greater education and fertility control while Vila (2005) refers to Scarpetta et al. (2000), when highlighting that technological and organisation advancements are dependent upon the educational levels of a labour force.

26 _{The details of the education expenditure per capital indicator are to be found here}

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The data for the total public education expenditure as a percentage of GDP is available from the World Development Indicators (WDI), and I have sourced the data for my dataset from the World Bank Data Bank.

5.5.4 Log of GDP

The Gross Domestic Product is used to describe the health of a country’s economy representing the monetary value of all the goods and services generated by that economy in a given time period – usually per year. The GDP also reflects the size of the economy. Using the log of GDP allows for the consideration of compounded values, as values increase or decrease, and the Log of GDP provides a more exact, meaningful and robust way to measure changes. Using GDP as a measurement, a country’s relative economic development becomes clear. Countries with a higher GDP value may also have higher industry levels, output levels, employment levels and foreign direct investment levels which in turn may have an impact upon any of the DV’s maternal health, education or particularly labour force participation – and as such it is appropriate to include Log of GDP as a control variable. The data for GDP is available from the World Development Indicators (WDI) and sourced from the World Bank Data Bank, and for the purpose of this study, an additional variable was created GDP_Log to contain the log value of GDP.

5.5.5 GNI

Gross National Income is the sum of GDP plus the net income received from overseas. While HDI, of course, includes GNI as one of its components, it is important to distinguish GNI as a separate entity from being a composite of HDI and show its relevance as an additional indicator. Ravallion (2010) highlighted that the weighting system of the HDI components has the potential to mask extremely positive or negative values of the other two components. His example highlighted how Zimbabwe’s education score was concealed by its highly negative income score. Should the reverse be true in an example then the specific controlling of a very strong or very weak GNI value would be lost if GNI were not to be utilised as a specific control variable and as such it is appropriate and wise to include GNI as separate a control variable? The data for GNI is available from the World Development Indicators (WDI) and sourced from the World Bank Data Bank.

5.5.6 Regime type

I argue that regime type drives variation in the impact of IMF programmes upon women. As such I argue that it is important to control for regime type in the statistical tests which follow. The Polity dataset is a widely used dataset that grades all independent states as to their level of democracy or autocracy. Polity IV reviews a states’ electoral process and using a 20-point scale from -10 to +10 determines whether a state is autocratic (-10 to -6), anocratic (-5 to 5) or democratic (6 to 10). This dataset has proven invaluable and has facilitated research that explores the impact regime type has on a number of other variables. However, the Polity dataset has some challenges and Cheibub, Gandhi and Vreeland (2009) introduced a new dataset called Democracy-Dictatorship providing a dichotomous measure of a state’s regime

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type in the context of either democracy or autocracy. This new dataset aimed to improve existing measures by extending country and year coverage and their six-fold regime type classification cover 199 countries with full codification and no missing data. Research has already benefited from this new dataset in particular Bauer et al. (2012) hypothesised that the regime type of the state is a critical component in the attraction of FDI for states undergoing an IMF agreement. This dataset is also particularly appropriate for my thesis. This dichotomous measure of regime type enables me to categorise my data into democracies and dictatorships and facilitates analysis to determine whether regime type drives variation in the effect that IMF agreements have upon the socio-economic status of women. Importantly this will allow me to highlight any potential ‘hiding’ of possible important findings when data is pooled. However, in the interest of robustness, I have used both datasets in my modelling to ensure the robustness of my tests. Table 5.20 displays the split of programme types by regime type.

Table 5.20 IMF programme types with regime split Programme

Type

Total programmes Democracy Autocracy

ECF / EFF 76 45 31 ESF / ESAF 153 52 101 SBA 177 129 48 SCF 2 0 2 PCL 3 1 2 PRGF 100 45 55 PSI 16 4 12 Total 529 276 251

5.5.7 Country fixed effects and year effects

Observational studies are often prone to omitted variable bias which is dangerous and can mislead the interpretation of results. As such it is important to approximate a randomised experiment as closely as possible. Already specified are a number of important control variables, but additional unobserved factors might also affect the regression. It is possible to extend these controls with the use of fixed effect methods which enables me to control for possible characteristics of the countries in the study as long as the characteristics do not change over time. Allison (2005:2) outlines two requirements which are that:

‘Each individual in the sample must have two or more measurements on the same dependent variable. At least some of the individuals in the sample, the values of the independent variable(s) of interest must be different on at least two of the measurement occasions.’

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This is true for the data in my study and as such fixed effects become a useful tool. This allows me to eliminate a large source of bias and as such, it is wise to include country fixed effects and year effects within the regression, thus eliminating sources of omitted variable bias, namely, unobservable cross-country and across-year differences.

5.5.8 IMF agreement types

The focus of IMF lending has evolved since its inception in order to meet various crises. Post- war lending focused on industrial countries as the IMF concentrated on rebuilding the international monetary system while the 1970’s oil shock saw lower and middle-income countries entering IMF agreements in order to cope with increasing debt. The fall of communism and the transition of Eastern European States towards market economies in the 1990’s led to increased demand for IMF resources.

While the IMF SBA (Stand-By-Arrangement) facility was initially a prominent funding instrument, the IMF has evolved their funding mechanisms to deal with the variety of fiscal and monetary needs of states in economic difficulty.

Many original agreement types – such as the ESAF – are no longer present but have evolved into more appropriate facilities with greater ability to provide solutions to the members’ economic issues. As such, in the dataset, countries undergo varied IMF agreements – represented by the variable ‘IMF Arrangement Type’ which allows for interrogation and data analysis to provide a more nuanced view of the impact of IMF agreements upon the socio- economic status of women.