How does regional economic development work?

Countries, states, regions, and cities are interested in stimulating economic development in their jurisdictions. Various possible strategies are often mentioned:

• encourage entrepreneurship
• improve the talent base
• enhance the attractiveness of the region to outsiders with creative talents
• create a legal, fiscal, and regulatory environment that encourages new businesses
• create larger pools of venture capital
• attract out-of-region businesses through regional business-attraction centers
• encourage research and development in local universities
• facilitate the movement of inventions from the lab to the business plan

The primary question here is a causal one: what strategies actually work, and how would we use empirical research to evaluate alternative policy interventions by governmental and non-governmental organizations? How would we decide which policies to invest in?

In order to begin to answer this question, we need to get a little more specific about what we mean by "regional economic development." I'll just stipulate an answer to this:

Regional economic development entails the creation of new businesses and expansion of existing businesses, in a way that expands the total number of jobs and results in a rising average wage.

So regional economic development aims at creating more employment and a rising standard of living in the region, and it seeks to do this through causing expansion of profitable business activity in the region. And, in order to create higher-paid jobs, the businesses created or expanded need to be on the high-value-added end of the spectrum; this often means skill- or knowledge-intensive industries. New jobs in low-productivity manufacturing or service businesses will not increase the average wage.

(The last statement isn't necessarily true in a region with persistent unemployment. A new low-productivity factory with a low average wage will raise the average standard of living if it significantly reduces unemployment. However, it seems perverse to have the goal of stimulating the growth of low-productivity sectors and businesses rather than higher-wage opportunities.)

There are several ideas that arise from these simple statements and questions. One concerns the limits that exist on the ability of state or local governments to actually influence the rate of growth of business activity, jobs, and wages at all. It is an open question whether a state or region that has done an optimal job of aligning its policies and investments will actually have a higher probability of growth in these outcomes than one that has no strategies, bad strategies, or poorly aligned strategies. This is because the decisions made by investors and entrepreneurs are influenced by many other factors besides the policy tools available to the municipal or state governments. And the success or failure of business choices is determined by events outside the policy arena. The best way of capturing this thought is to recognize that the causal influence of good policies may still be small relative to other non-policy factors.

Second, though, we might have strong theoretical reasons for thinking that one policy choice is likely to have larger effects on the desired outcome than another. If we can demonstrate a theoretical basis in economics or organizational theory, for example, for concluding that factors X, Y, and Z are favorable for producing the outcomes we want, even if they are not decisive, then it is logical that we should try to identify the most influential of these factors; design a coherent package of policies that enhance these factors (i.e. avoid combinations that are self-defeating), and make the effort possible to implement this package of policies. That is, there may be a basis in social science theory for judging that policy X should have a positive effect on the probability of desired outcome Y. The reasoning may be economic (businesses will have an incentive to locate in regions where they can have a high confidence of recruiting a qualified workforce) or perhaps logistical (businesses will choose locations where transport is convenient) or fiscal (firms will take careful account of the total cost of doing business in Michigan versus California or North Carolina). But if we can demonstrate on theoretical grounds that X should positively influence Y, then we have reason to implement X even if we don’t have direct empirical evidence of X’s effectiveness.

This is the role of theory in justifying the choice of a policy package: a set of antecedent theoretical reasons for believing that this set of policies will work to increase the outcomes we are interested in achieving. There is another more empirical basis for assessing alternative policies, based on comparison of cases: look at a number of cases in which X is present or not present, and measure the status of Y. From a social science point of view, this inquiry is feasible but difficult. If we select six cities for comparison and find that the cities with a high percentage of college grads have high growth while those with low college grad percentages have low growth -- does that demonstrate that "talent causes economic growth"? Not exactly; the observed correlation could be spurious, accidental, collateral, or reversed. And likewise with the large-N version of the study. Suppose we find either that Y is positively correlated with X or that Y is not correlated with X, based on the values of X and Y over a large number of cases. Does the first finding demonstrate that “X causes Y”, and does the second case demonstrate that “X does not cause Y”? Neither conclusion is justified on the basis of just these facts. The non-correlation finding may be the result of a genuine causal influence masked by a number of disturbing influences; and the positive correlation may be the result of a common cause that is influencing both X and Y. So, as econometricians and epidemiologists know very well, the design of empirical studies that sort out the causal impact of various treatments is challenging.

In addition to these issues having to do with our ability to assess the causal weight of various policy treatments, we also have to consider the ratio of costs and benefits associated with various bundles of policies. Policy makers are forced to arrive at some estimate of the relative costs and benefits of the available policy options in order to make sensible choices among them. But here too there are the same problems of estimation: it is difficult to estimate either the probability of success of a given policy or the net value of the success of the policy.

The upshot of this short reflection, if there is one, is that it is very difficult to arrive at solid, precise, and rigorous estimates of either the economic impact of various policy choices or of their likelihood of success. And yet the policymaker does not have the luxury of indecision; economic development is a crucially important component of the wellbeing of a region's population. So it seems unavoidable that the best we can do is to assess policies on the basis of their likely contribution to economic growth, based on available economic and social theories, and be ready to finetune our choices as experience indicates.