Fixed effects

As it was mentioned in the beginning of the chapter, observational panel data are used in the exploration of the health implications of economic transitions at a na- tional and individual level. At a national level, cross-country comparisons are con- ducted with the inclusion of international databases providing various indicators of interest. More details for those indicators will be given in the following chapter. The information collected covers four decades staring from the year 1971 across fifteen European countries. This results in a time-series cross-sectional dataset (TSCS). At an individual level, the data originates from the Scottish Longitudinal Study (SLS) and covers two time frames 1991 and 2001. In the case of Scotland the interest lies on the comparisons across occupational groups (industries versus services) and across occupational transitions (unemployment, re-employment, inactivity).

The main difference between panel and time series cross-sectional data (TSCS) is that usually the general panel structure has a large number of units (large N) with every subject observed only a few times. Similarly, the panel structure provided by the SLS contains few time frames but many individuals. In contrast, the TSCS datasets demonstrate a large (or not) number of units with multiple time frames (Kittel, 1999). In this case, there are both short (many individuals few time points) and long panel (few countries and long time series). For the needs of the analysis at a national and individual level, fixed effects modeling is used, which is a special case of matching estimators.

The main advantage of panel data is the capability to explore dynamic relationships and to model differences among subjects. Fixed effects investigate the association between predictor and outcome variables within entities such as countries or indi- viduals. Since the attributes of an entity may influence the predictor and outcome indicators, fixed effects have the ability to control for unobserved and time invariant characteristics and that is the main advantage. Thus, fixed effects are not suitable for exploring the association of variables that demonstrate very little within cluster variation (Frees, 2004).

Therefore, the choice of this statistical method is based on two main reasons. First, in every cross-country comparative research, variations across countries, such as in- stitutional differences, public health and social policy systems, influence statistical associations. These are time invariant confounding factors that must be controlled

Chapter 3. Methodology 55 for in order to isolate any associations. These differences can be included by us- ing county-specific slopes in the form of dummy variables. Second, fixed effects explore within entity (individuals or countries) variations, which is essential in ex- amining annual economic transitions and mortality variations across longer periods of time. In the case of individual variations, fixed effects can assess the health implications of occupational and socio-economic transitions.

Assumptions

The following section outlines the main assumptions associated with fixed effects. For any causal inference to be made, the assumptions must be met. The main as- sumption of the fixed effects is related to the constant variance (homoskedasticity) across subjects. In this case information for different countries should vary differ- ently. However, in one country, indicators can fluctuate widely, whereas in other countries remain relatively stable. Thus heteroskedasticity is a common element of this data (Cameron and Trivedi, 2005).

Secondly, indicators in various countries are assumed to be independent from each other. For example, economic performance in one country can differ in relation to what occurs in other countries. In statistical terms this is described as cross- sectional autocorrelation. Nevertheless, institutional changes do not have long- lasting implications on economic performances thus the intensities of these effects gradually disappear. Another assumption is that there is no correlation between the error term of an entity (individual or country) and the predictor variables. However, fixed effects allow for some form of endogeneity, where covariates can be correlated only with the time-invariant error term. Therefore, serial correlation occurs since the error term is correlated between different time points (Kittel, 1999).

Furthermore, time-invariant attributes are distinctive across country/individuals and should not be associated with other attributes of an entity. It is assumed zero corre- lation between time-constant variables and an entity’s error (Frees, 2004). In fixed effects or within estimator, the idiosyncratic error is independently and identically distributed. Due to the fact that every entity (country or individual) is dissimilar, a country’s error and time invariant attributes should not be correlated with other countries. However, this method permits correlation between time invariant and time varying covariates. Finally, independence over entities is assumed, however

Chapter 3. Methodology 56 for observations within a subject serial dependence is allowed. The independence (homoskedasticity and normality) assumption can be relaxed by using cluster robust standard errors (Cameron and Trivedi, 2005).

Limitations

The main advantage of fixed effects is that it controls for time invariant country at- tributes, which is necessary when institutional differences are evident and influence associations. However, as with every statistical method, there are certain limita- tions that must be considered. The main limitation of fixed effects is that it cannot explore time invariant origins of the outcome variables and are confined only in in- vestigating changes within an entity. In the case of TSCS, the coefficients describe the combined average partial effect of time and space. Thus since the coefficients (time and space) remain constant no additional information can be obtained for these dimensions of cross-country variations (Kittel, 1999).

Fixed effects are sensitive to attenuation bias derived from measurement error, which usually changes from year to year. In the same context, fixed effects can- not control for unmeasured predictors that vary over time. Another issue is cross- sectional autocorrelation, which occurs when variables are assumed not to be in- dependent from each other. Economic performance or institutional conditions can depend on events occurring in other countries and can lead to parallel deviations from the values of the dependent variables. Another limitation is that the error term is correlated between different points in time for every subject or country (serial correlation) (Kittel, 1999).

A common alternative to fixed effects is random effects. The main advantage of random effects is that time-invariant attributes can be included in the model as ex- planatory indicators. It assumes that the fixed element is uncorrelated with the independent variables and variations across entities are expected to be random. It is necessary to specify those time-invariant indicators otherwise this can lead to omit- ted variable bias. However, for the current analysis, fixed effects has been consid- ered more suitable for two reasons. First, in cross-country research the randomness of the variations across units is a very strong assumption and is often violated. This method assumes that the time-invariant error is not associated with the covariates, which is frequently unlikely (Cameron and Trivedi, 2005; Baltagi, 2008).

Chapter 3. Methodology 57 Similar strategies with fixed effects are the deviation from mean, named as within estimator, where the residuals from a set of country dummies in a country/year panel are expressed as deviations from country means. An equivalent to fixed effects is the inclusion of the least squares dummy variable (LSDV). This is an ordinary least squares (OLS) regression with dummy variables and contains the usual assump- tions of normality, independence and homoskedasticity (Kittel, 1999; Cameron and Trivedi, 2005).

An extension of fixed effects, named pooled effects, allows for dynamic analysis of cross-country and time variations. The assumption of pooled models is that the residuals are independently and identically distributed. If all independent variables are taken into account, there is no correlation with the error or dependence between entities. More precisely the model supposes that countries or individuals are identi- cal and a single coefficient is estimated for countries and periods. However, pooling regression, without the inclusion of dummy variables, ignores unobserved hetero- geneity (Baltagi, 2008).

Furthermore another method, differencing, is suitable when two or more periods occur. It assumes that homoscedasticity exists and there is no serial correlation in errors. Differencing and within estimators although minimize the omitted variables bias, however they remove potentially useful information. The inclusion of first differencing allows the error terms to be correlated with each observation. Like- wise, the implementation of instrumental variables and lagged dependent variables as regressors can deal with measurement error issues. First differencing has the ad- vantage of a weaker exogeneity assumption, by including one less year from data. This way it permits future values of the covariates to be correlated with the error (Cameron and Trivedi, 2005). Finally, the last section outlined some of the most common techniques for panel data. There are numerous other statistical strategies for panel data and of course it is not possible to refer to their advantages and impli- cations in detail. After exploring those strategies the inclusion of fixed effects has been considered as the most suitable for the analysis.

Chapter 3. Methodology 58

3.6 Summary

This chapter outlined the main theoretical and methodological considerations asso- ciated with comparative research. The basic implications are concentrated on the selection of appropriate measurements in defining events, processes and outcomes. Secondly, social and economic phenomena include interactions of elements operat- ing at different levels and across various dimensions. This thesis overcomes those complexities by including observational data that refer to macro (aggregated) and micro (individual) levels. For that reason each subsequent chapter explores different sets of research questions and presents different methods of analysis. Although this chapter has briefly described the data used in this thesis, it has mainly aimed at dis- cussing the overall advantages and limitations of comparative research and various statistical methods.

From an empirical perspective, there is a great diversity among statistical methods commonly used for identifying causal associations in panel data. Those strategies contain their own assumptions, strengths and weaknesses. The fixed effects method has been considered the most suitable after taking into account the nature of the data and its diverse context. This strategy has the ability to control for unobserved time invariant attributes across entities (countries or individuals). This is vital since countries are characterized, for example, by institutional, social policy and public health differences that should be controlled for in order to isolate further associ- ations. Although institutional and policy changes occur in every country, never- theless those changes do not fluctuate as widely as the overall economy and their effects on economic performances are not ongoing or so intensive in the long-term (Kittel, 1999). Subsequently, those elements are perceived by fixed effects method as time invariant confounding factors. The next chapter is the first results chapter that explores the time trends of deindustrialization and mortality variations across different European countries.

Chapter 4

The progression of

deindustrialization

4.1 Introduction

This first chapter of the empirical part of this thesis aims to explore the progression of industrial decline. It is hypothesized that industrial decline is characterized by uneven progression and severity across countries. This is important since this thesis assumes that the magnitude or severity of deindustrialization can be associated with mortality fluctuations, whereas periods of recession can have an additional effect on mortality trends.

Before the transition towards services, the industrial sector has been the main em- ployer of most European counties. Therefore, during periods of recession, when unemployment rises, industrial contraction can be more severe and therefore influ- ence mortality variations. In order to explore the nature of deindustrialization, it is necessary to adopt a comparative approach that illustrates in detail the timing and progression of this event. In parallel to this, an inclusion of a single approach that measures the magnitude of industrial contraction can detect potential common patterns of the severity of deindustrialization.

Due to the diversity of the current literature concerning the determinants and method- ology of measuring industrial decline, it is essential to decide on a framework that meets the purposes of this chapter. The main interest lies on the country level indus- trial decline, triggered by internal policies and institutions, resulting in the growing

Chapter 4. The progression of deindustrialization 60 importance of the service sector. Thus, this thesis adopts the two-fold (positive versus negative) approach introduced by Rowthorn and Wells.

In the maturity argument deindustrialization is perceived as a positive transforma- tion of the economically advanced countries. It is a natural evolution of mature and successful economies moving towards a service-based financial system. Therefore, employment in manufacturing declines, while employment in the service sector in- creases. In particular, Rowthorn and Wells (1987, p.5) perceive deindustrialisation as a“the normal result of sustained economic growth in a fully employed, and al- ready highly developed, economy [which] occurs because productivity growth in the manufacturing sector is so rapid that, despite increasing output, employment in this sector is reduced, either absolutely or as a share of total employment”. This aspect of deindustrialisation has been named as positive or mature deindustrialisa- tion, where the labour surplus from manufacturing can be absorbed by the services (Tregenna, 2008).

Contrary to the positive or maturity argument, the failure thesis or negative deindus- trialization is the second argument identified in the literature. It covers three main aspects: loss of competitiveness, expenditure patterns and specialization (Rowthorn and Coutts, 2004). More precisely deindustrialization could be“a product of eco- nomic failure and occurs when industry is in severe difficulties. Labour derived from the manufacturing sector - because of falling output or rising productivity- will not be reabsorbed into the service sector”(Rowthorn and Wells, 1987, p.5), thus unem- ployment increases. The failure of manufacturing is caused by changes in expendi- ture patterns of the population. In advanced economies where the national incomes rise, the public expenditure patterns are altered towards more service - based goods (Rowthorn and Coutts, 2004). Furthermore, specialization in industries as well as advances in technology and competitiveness can contribute to the acceleration of productivity and at the same time create a demand for more sufficient, profitable, less labour intensive, highly-skilled industries. As a main outcome, less labour intensive industries reduce the amount of employees needed in the manufacturing sector (Rowthorn and Ramaswamy, 1997, 1999).

The first stage of the project addresses the issue that very few studies have measured the extent and magnitude of deindustrialization in a comparative perspective and distinguished between different types of industrial decline across countries (Tre- genna, 2008). Therefore, this chapter has a dual aim. The first stage of this thesis

Chapter 4. The progression of deindustrialization 61 has recognized the lack of a single measurement that comparatively assesses the extent and magnitude of industrial decline. Thus, this is achieved by computing a single index to quantify deindustrialization.

Having decided upon the framework and computing the index the second objective is to construct a typology that differentiates between various categories of deindus- trialization and subsequently allocates countries according to their industrial em- ployment performance. Since industrial contraction is an event portraying uneven progression and intensity, a classification may provide an initial illustration of its severity across countries. This chapter concludes on two research questions: 1 A. What has been the pace and timing of deindustrialization across Europe? 1 B. Is it possible to detect common patterns of industrial decline across different countries?