Business long has played a pivotal role in European integration. Given the economic orientation of the European Union (EU), beginning with its roots in the European Coal and Steel Community (ECSC), business interests affect – and are affected by – the policies and decision-making of the organization. In fact, some of the anti-EU sentiment that often is heard in Europe today is grounded in a view that perceives the organization as slavishly tied to business interests, with the concerns of workers, environmentalists, consumers, and other public interest groups given less serious consideration. While the reality is far more complicated, this caricature can be appreciated with a better understanding of the role that business historically has played in European integration and policymaking. This chapter focuses on two issues: the role that business plays in theories of European integration, and the practice of lobbying EU institutions. While these two subjects at first glance may appear not to have much in common, it should be clear by the end of the chapter that there is an on-going reflection by scholars of European integration on the day-to-day impact of business groups in the larger picture of governance in the region as a whole.

Explaining the influence of business in European integration

It is beyond the scope of this book to explore the range of theories about European integration. Literally hundreds of books have been written on the subject, and this one will not be adding a new “ism” to such well-trammeled ground. However, it is worth spending a little time trying to understand the role that business has played in shaping the development of the EU for two reasons. First, business activities do not take place in a vacuum. Companies, whether operating within a shareholder or stakeholder model, seek to improve their position vis-à-vis their competitors. This requires market-based actions (such as developing new products, devising pricing strategies, and organizing supply chain and distribution systems, among others) as well as non-market actions. David Baron’s research on this subject contends that successful companies need to develop non-market strategies (i.e. those that require interactions with governments, regulatory bodies, labor groups, non-governmental organizations (NGOs), and so forth) that are integrated with traditional market strategies (Baron 1995).

Following the approach used in Chapter 8, we continue with the multilevel governance structure to describe the regulatory aspects of business in Europe. That chapter focused on those policy areas that both the EU and national governments use to promote business by: ensuring that companies have greater and fair access to member states’ and global markets; reducing the costs of doing business by, for example, creating a common currency; and providing incentive for companies to invest in Europe through tax, R&D, and other policies. This chapter examines some of the most important regulatory aspects of the EU and member countries. To some extent, the separation of this content from Chapter 8 is somewhat arbitrary. The policies in that chapter are of a regulatory nature, too. Deciding whether mergers should be allowed, issuing directives to further the single market, and setting tax rates all require creating rules that affect business. Likewise, stricter environmental rules (say, on carbon emissions) may be viewed as burdensome regulations by some companies (coal power plants), but business-promoting by others (those involved in green energy). The case of the wine industry described in Box 9.1 illustrates the point that regulatory actions by the EU and national governments are sometimes intended to protect industries from illegitimate competitors. The topics presented in this chapter are chosen because most companies across a wide range of industries would generally regard them as constraining in nature, and because there are rather clear interest groups advocating their consideration in formulating public policies. There are valid reasons why governments do not give complete free rein to companies to do what they wish. One of the roles of governments is to weigh the merits of competing groups and their interests, and make decisions that serve the wider common good. These decisions often do not please business, but are an essential component of the democratic process and the legitimacy of governments. By the end of this chapter, the reader should have a better understanding of the rationale for consumer, environmental, and labor regulations in Europe, and how they affect the decisions that companies and their managers make.

We now have reviewed two of the three models of capitalism. In this chapter, we turn our attention to state capitalism. This term may seem out of place in the twenty-first century, where globalization appears to have strengthened business and weakened national governments (the “state”). But this form of capitalism is alive and well in Europe and has even been adapted by many of the world’s emerging markets, notably China and Russia, in ways that are less market-friendly than their European counterparts. To understand this phenomenon, one must accept that some citizens view the roles of their governments differently than is the case in market and managed capitalism societies. Because of historical circumstances, the state has played an important and not always negative role in the economic development of countries that fall into this category. As you read this chapter, think about the areas of similarity and difference with the market and managed capitalism models, as well as how European integration may be affecting the future of state capitalism in countries like France, Italy, and Spain.

Overview of the state capitalism model

The third model of capitalism described in this book is state capitalism. Its defining characteristic is a more active role by the state (government) than either the market or managed capitalism models. While such a feature may sound somewhat “uncapitalist” to some readers, since a reliance on market forces rather than government intervention is what sets capitalism apart from other economic systems like communism or socialism, it is important to note that government intervention is present in some form in every country, including those labeled “capitalist.” Chapter 4 and Chapter 5 made this evident in the market and managed capitalism models, respectively. During certain times since the end of World War II, governments in the UK sought greater degrees of influence over the economy (in terms of ownership, subsidies, and intervention in labor strife) than the ideal market capitalism model would suggest. Governments in Germany have long played a supportive role for that country’s industry, albeit avoiding outright nationalization of companies, as have governments in Scandinavia and other countries bordering Germany.

PURPOSE

This text is written for a first course on statistics and quantitative methods for Ph.D. students in social science and allied fields. Anyone undertaking to write such a book must sooner or later confront the question of whether the world really needs another introductory statistics textbook. In my surveys of the market for my own classes on this subject in two social science Ph.D. programs, I clearly decided that it does.

Students in social science Ph.D. programs outside of economics have widely divergent levels of previous exposure to statistical methods, as well as comfort with mathematical expression of concepts. The typical Ph.D. program does not have the luxury of multiple “tracks” to suit different backgrounds, so one course must accommodate all of them. That course must be accessible to students with divergent levels of preparation but must also prepare them technically for future quantitative methods coursework ahead of them.

More important, I have found that students of whatever background will plunge relatively enthusiastically into methods training once they understand why it is essential for the purely substantive elements of their research. Simply put, many students, particularly those without much prior exposure to statistics, do not understand what it is or how it can help them as social scientists. Without this understanding they lack the buy-in necessary to make the technical rigors of the course seem worthwhile.

Recognizing a distinction between events that did occur and events that did not occur but might have is the point of the previous chapter. Given this distinction, we are generally uncertain, before a process unfolds, about what its outcome will be. We would like to relate the observable to the more fundamental data-generating process (DGP) behind it. But if the DGP is stochastic, we will always be uncertain about its defining features, and we need a language to express that uncertainty. Turning this around, given some stochastic DGP, we are generally uncertain about what might result from it. We need to be able to express this uncertainty explicitly and carefully.

To do so, we need some tools from the theory of probability. Probability theory is a conceptual apparatus in mathematics for expressing and evaluating uncertainty. As such, it is an important foundational component of statistical inference. But it is different from statistics. In probability theory, we start with a DGP with basic properties that we know (or assume, or pretend to know) and work out the consequences for the events that might be observed (e.g., their probabilities, how those probabilities are related to the DGP).

In (classical) statistical theory, by contrast, we start with a set of events we have observed and attempt to infer something about the properties of the stochastic DGP that generated them. In other words, probability contemplates what data will be observed from a given stochastic process.

In hypothesis testing, we make conjectures about the data-generating process (DGP) and assess the weight of evidence that the sample offers in support of them. The conjectures about the DGP that are subject to testing should have some theoretically interesting foundation, but they are made before any evaluation or analysis of the observed sample data. Hypothesis testing does not address where the conjectures come from; they are taken as given, supplied by theory, and are tested against data.

Statistical estimation, by contrast, does not take as given conjectures to be evaluated. Instead it uses the observed data to make conjectures about the unobserved DGP that are in some sense good or reasonable. There are two general types of estimation, interval estimation and point estimation. These types of estimation are treated in this chapter.

Interval estimates specify a range of values that are all “reasonable” guesses about an unknown parameter of a DGP. Typically the interval estimate contains the parameter of a DGP in a user-specified probability of random samples. Interval estimates are useful because they combine a sense of the “best guess” of a parameter's value and some uncertainty about that best guess into one statement. Another name for an interval estimate in classical statistics is a confidence interval. Although a hypothesis test asks whether a particular conjecture about the DGP is reasonable in light of the data, a confidence interval can be thought of as a range of reasonable conjectures about the DGP.

Moving from a mass of data to an informative description of patterns within that data is a basic point of quantitative techniques social science, an area of quantitative analysis called descriptive statistics. It is not the fanciest math, nor does it comprise the most subtle concepts, but it requires serious attention in any quantitative work. Descriptive statistics is an important part of constructing an argument using data and includes making apparent the tendencies, patterns, and relationships in that data. Equally important is that descriptive statistics helps a researcher get a feel for the behavior of the variables in a dataset and for conjectures about relationships that are worth further analysis, both statistically and theoretically.

Before meaningful analysis can proceed, it is necessary to understand how we observe and measure the concepts of interest in any research. Thus this chapter begins with a brief overview of empirical measurement of variables. It then discusses common graphical and statistical summaries and descriptions of aggregate data, first for one variable at a time (univariate distributions) and then for relationships (bivariate or multivariate distributions). It also covers some important theoretical properties of these descriptive tools.

Some basic concepts are important. To fix ideas, imagine a dataset with a set of observations of one or more characteristics of some collection of units. For instance, on the “democratic peace” (see Chapter 1), the units might be pairs of nation-states.

Chapter 2 dealt with the summary and analysis of data that is actually observed. It is possible that a researcher or analyst has no interest in the variables or concepts being analyzed beyond the particular set of observations available to him or her. If a university wants to know whether its admissions decisions last year were less favorable to members of underrepresented groups than to others, taking as given other aspects of each application file (grade point average, entrance exam scores, etc.), it need only analyze last year's admissions data. A linear regression, purely descriptive of this data, would shed light on the question.

However, the remainder of this book covers elements of probability theory and statistical inference and modeling, which itself rests heavily on probability theory. Before launching into that treatment, it is necessary to consider why (or conditions under which) we need it.

When a theory relating two or more variables is part of the consideration, it is unusual that a researcher's interest in the variables ends with the data that happens to have been observed. Such a theory deals with the process or behaviors that give rise to the data that was observed, not just that data itself. That data helps to inform whether the theory has any drawing power in reality, but that data is not the sum total of possible observations of the social process in question.

As datasets can be usefully summarized to compress much information into small pieces (Chapter 2), probability distributions can be as well. We review some of these summaries in this chapter. These summaries of probability distributions revolve around various kinds of expectations of the behavior of a random variable. These expectations are important because they typically relate to the specific aspects of stochastic data-generating processes (DGPs), called parameters, that social scientists connect to substantive theory and attempt to uncover in empirical work. In one sense, the material in this chapter helps to explain why that is. In addition, this chapter also provides some basic familiarity with these important formal constructs that we use repeatedly in subsequent material.

One of the most important points of this chapter is to define the regression function, or expected value of Y as a function of X, as it exists in a DGP specified as a joint distribution function. We then proceed to establish some important properties about this DGP regression that help to motivate and justify the widespread interest in this function in empirical social science. Later chapters spend a great deal of time developing common models for this regression (and techniques to make inferences about the DGP regression from regression models fit to sample data), so it is important to get a handle on why anyone should care about it.

In Chapter 6, we saw a variety of examples of data-generating processes (DGPs) that are common in statistical models, which consist of a DGP for the data and a link from its parameters to explanatory factors. Although the parametric family of the DGP may be assumed, the specific parameters of the DGP that gave rise to data we have available are generally not known. The whole point of empirical analysis is to use observable data to learn something about those parameters. A theory may assert that the conditional mean of Y increases as X increases, for instance, but in empirical analysis, we wish to determine how credible this assertion actually is. That is the point of statistical inference, which is the subject of the rest of this book.

To understand how we can use data to inform ourselves about parameters of DGPs when they are unknown, it is useful to see what happens when we pretend these parameters are known. If we have data drawn from a known DGP with known parameters, we can see how summaries and statistics computed from that observed data are related to those parameters. That is the subject of the present chapter. Here we study how summaries and statistics computed exclusively from observable data are related to the DGP, given a collection of observations from the DGP.

Statistical inference and modeling are different topics. One involves expressing the properties of DGPs in terms of parameters and parameters as functions of other variables. The other involves making inferences from observable data back to DGPs, whether those inferences are structured by a model or not.

Statistical inference is necessary to learn about a social process broader than the mere data in front of a researcher any time the process that generates that data or makes it observable has a stochastic element. Statistical modeling is not a necessary implication of any particular metaphysical view about DGPs. But it is helpful to social scientists attempting to learn about stochastic ones. First, statistical models allow for crisp expressions of the link between the positive theory that is often of ultimate interest in social science and data-generating processes (DGPs). Second, statistical models narrow the range of possible DGPs that might have generated the data considerably. This does a great deal of work in structuring the estimation and inference problems that researchers face. In a sense, a model represents a sort of “prior belief” about the workings of the social process under analysis. The analysis precedes from and is informed by that prior belief. And in another sense, models represent “free” information: generating equally certain, equally strong conclusions without a model as are possible with the aid of a model requires a massive increase in available data.

Positive theories in social science assert relationships among concepts. An example is that the number of police officers on the beat leads to a drop in crime or that monolingual education in linguistically diverse schools leads to stronger attachments of group members to their own ethnic identity. Postulating a statistical model – (some aspects of) a data-generating process (DGP) for the data and (some aspects of) a link from its parameters to covariates – is how we translate theories into statements about stochastic DGPs. Often, as we have seen (Chapter 6), these translations relate to the conditional mean of Y given some explanatory factors. For instance, a (very simple) theory might assert that the mean of the DGP (generating crime levels conditional on beat cops) is higher when there are “few” beat cops than when there are “many.” This can also be stated in terms of the slope coefficients in a regression model; for example, in a linear model for the conditional mean of turnout levels given voters’ information, saying the former is positively related to the latter is the same as saying the slope term on information levels is positive.

Sampling distributions of the sort covered in Chapter 7 provide a basis for evaluating theoretical claims about the DGP based on evidence from a collection of draws from that DGP (i.e., a sample). This is the subject of hypothesis testing, which provides a set of statistical techniques for evaluating the strength of sample evidence supporting specific conjectures about the DGP.

We have ignored the elephant in the room long enough. Social science theory does not just imply relationships. It often implies causal relationships – that a change in an explanatory factor causes a change in the dependent variable for some specified reason. Yet all the methods and concepts we have covered are simply about assessing relationships, causal or not. We have tools to describe relationships in observed data, and tools to make inferences about whether the relationships observed in data hold in the underlying process that generated the data or whether they can be ascribed to idiosyncratic chance. We have tools to make a best guess about the relationships that hold in the data-generating process (DGP) and quantify our uncertainty about those guesses. But none of this deals in a sustained fashion with whether relationships are causal relationships.

When we spin theories, we often spin them in causal terms. And when we make policy recommendations, we often do so on the belief that the recommended intervention will cause a change in some important outcome. But causation is not just a theoretical concept. “Correlation does not imply causation” has reached the status of a truism. In light of this maxim, it might seem that the best approach to inferring causation from correlation is not to try it. But this causal nihilism is not reasonable. Claims of causation are more credible in some empirical research than others. We need to ask why this is so, and if possible, try to make empirical research with causal aspirations follow a template that makes causal claims credible.