Trade models

The table below displays the same estimators as the table above, but instead of using import and export as a percent of GDP as two separate variables, trade (the sum of the two) is used.

10 The time variable is the linear time trend not accounted for by other variables in the model.

69 Table 7 Regressions: trade

First of all, the models are similar to the other models concerning the explanatory variables. We can see that the within trade variable is positive in all the models, which we would expect given that both import and export were positive in the earlier models. The variable is significant in all the models, except for the mixed model with al observations.

Interesting to note is that the interaction between the country mean trade variable and the within labor union density is positive, as earlier when the country mean import variable was used. As in previous models, the interaction is significant in all models except for the FD fixed model.

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8 Discussion

This chapter reviews the findings of this thesis, answer the research question and places the results in the context of earlier research. First, the models and robustness are discussed.

Second, a remainder of the sample and population is given, before a clarification about what support for hypotheses actually means. The rest of this chapter goes through the relationships of interest one by one.

Method

The main model used is a random effects model with between and within variables, separating level effects from changes over time. Because of the separation of the effects, the model is very close to a fixed effects model. The advantage of this approach, as opposed to a regular fixed effects model, is that the random effects model yields estimates of the level effects of the variables. These are consumed in the regular fixed effects model by the dummy variables. Due to the sample size and the likely omitted country level variables there are limits to what we can read out of the country level coefficients, but they can give valuable indications discarded in the fixed effects model. In addition, this approach allows for estimating other country level variables of interest, as well as incorporating random coefficients. These opportunities, however, can only be exploited with a sufficient sample, which is rarely the case in cross-national studies (Möhring, 2012).

Robustness of the findings were tested by omitting outliers, using a fixed-effects model, a first-difference mixed model, and a first-differenced fixed model. The models largely agree, with a few exceptions discussed below. The strong agreement between the models gives confidence in the results. In addition, this agreement reflects the model performance, despite considerable challenges, such as clear trends in the dependent variable and some of the explanatory variables.

Diagnostics were performed, and the results was encouraging, as no severe violations seems to have been made. There is always the issue of missing data, and whether these are missing at random. This is of course unknown. There were perhaps slight time trends in the missing data, possibly biasing the estimates, as we know that the trend in the top decile share have seen a general increase in the period.

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The time trends in the tax, labor union, import, export, trade and the income share variables causes some problems concerning hypothesis tests and potential spurious findings.

In the mixed and fixed models, this was handled using a residual structure (unique AR1 parameters). However, specifying a residual structure is no guarantee, and the analysis was supplemented with first differenced models. This increases the confidence in the results, as the trend issue seems to be overcome with the first differencing. The same residual structure was also specified in the FD models to tackle potential autocorrelation left after first differencing the variables.

Sample and generalizations

The sample here is the countries and the years where the countries have complete data.

The population is the full period for the countries. The statistical tests is thus a test if the findings can be generalized to the whole period, for all the countries.

Since the selection of the sample of observations is correlated with the dependent variable (we know that the trend has generally been an increase in the top decile income share in the period under investigation), these findings need not be valid for other periods. The sample does not cover the full range of variation on the top decile income share. The “cycles”

are extremely slow, and at present, there does not exist good, comparative data for the variables of interest to cover a full cycle on the top decile income share. This should be partly overcome by the use of longitudinal analysis, as we also get the cross sectional variation.

Nonetheless, when the selection rule is correlated with the dependent variable, the numerical estimates of causal effects will be closer to zero than they really are (King et al., 1994, p. 130).

Causality and the hypotheses

The hypotheses implies causality, and indeed, this can be viewed as the overarching goal (to use Collier et al. (2004)’s terminology). When the hypotheses are evaluated, they are evaluated from a falsifying principle. The support for a hypothesis is not the same as claiming causality. It is simply a statement that in the data and with the analytical methods used; there is sufficient support for the hypothesis, relative to the null-hypothesis (of no relationship).

Support for the hypothesis is not a validation of a causal relationship.

72 This thesis aims at answering the question:

“Can collective bargaining, tax policy and trade openness explain the increase in the top deciles' gross income share in OECD countries in the period 1981-2011?”

Each part of the research question is discussed below, before a final assessment of each component is made.