tony siesfeld is a managing director with the Monitor Institute by Deloitte, a team within Deloitte LLP. lara wigmore is a senior manager in Deloitte Consulting LLP’s Real Estate & Location Strategy practice.
Published September 4, 2019
For decades now, economic development authorities have sought to attract corporations to their counties and cities. They’ve been offering incentives ranging from tax breaks to red-tape-cutting to infrastructure investments, all to bring home perceived benefits usually summed up in a word: jobs. But wishing is not necessarily getting. Corporate investment in existing facilities and siting decisions for new ones usually take place in a buyers’ market — even in periods of low national unemployment. And critics see the scramble to dangle more goodies as a race to the bottom.
But whatever one thinks of the process, everyone on reflection should be able to agree that the job count alone doesn’t measure the value of a new facility to the locality. And, as a practical matter, that narrow focus on jobs to the exclusion of other social benefits can undermine local support — perhaps even kill the investment altogether. A more complete model of social outcomes is needed for localities to calculate where their interests truly lie.
SIMM and the City
Deloitte recently developed such a model — one that we believe allows a broad and nuanced view of the social impact that involves factors including everything from education outcomes to poverty rates to the stability of homeownership. And to demonstrate how it works, we designed a “twin study” to observe how a pair of similar counties changed over time, one with and one without corporate investment. The study was inspired by NASA’s Twins Study, in which two astronauts who were identical twins were compared by a variety of criteria after one spent an extended period at the international space station and the other stayed earthbound.
To that end,we compared about 100 matched-pair counties across five-year spans on the basis of some 100 variables. The model thereby derived, the Social Impact Measurement Model (SIMM), offers the basis for predicting the impact of new investment by dozens of criteria.
We then put the model to work in January 2019 to estimate the social impact of $3 billion in corporate investments announced since 2015 in two counties in Alabama: Limestone and Madison. We found that investments in which the aggregate expenditure exceeded $100 million should have a significant impact on variables that signal upward economic mobility, such as education scores and employment rates. But the type of capital investment, not just how much capital and how many jobs the investment creates, leads to important differences in outcomes.
In Madison County, a mix of manufacturing and service investments — data center and headquarters investments in the latter category — will likely result in a higher-income population with more disposable income. Additionally, higher incomes may also attract young, more highly skilled labor into the area who are more likely to rent apartments than own homes.
In Limestone County, by contrast, where the capital largely went to manufacturing-intensive projects, the investments will likely benefit the community with stabler, somewhat higher-income jobs than manufacturing jobs typically provide. The county should expect a decrease in the poverty rate, for example, coupled with an increase in individuals at or above 200 percent of the poverty line. While the standard of living will likely improve, disposable incomes should fuel homeownership rather than other types of local spending.
The smaller impact on disposable income of middle-sector jobs means the investment won’t create many ancillary jobs in the county. By contrast, higher-income jobs are more likely to attract more highly skilled migrants rather than cutting local unemployment.
There’s an important point here that should not be lost. There are trade-offs between attracting “middle-sector” and “higher-income” jobs to the county. Middle-sector jobs — think production and operation workers — are projected to provide stability for the local population and reduce the poverty rate. However, the smaller impact on disposable income means the investment won’t create many ancillary jobs in the county.
By contrast, higher-income jobs like software engineers are more likely to attract more highly skilled migrants rather than cutting local unemployment. Residents may become more inclined to living in suburbs, incurring slightly higher housing expenses as they choose to buy single-family detached homes. Local retailers might actually take a hit as money is spent elsewhere.
Indeed, not all of the likely social changes are positive. For instance, among the outcomes linked to family and migration, the $990 million invested in Madison County is predicted to generate a modest increase in the divorce rate (0.8 percent) and in the portion of single-parent households (0.7 percent), according to the model.
Our research also points to the conclusion that some counties “metabolize” capital investments better than others. All else equal, there seems to be an S-shaped curve that best describes the relationship between changes in social outcomes as a function of initial conditions. The counties that show the most change from capital investments are those that are about average on many or most of the social indicators we tracked.
Not surprisingly, counties that are already relatively high on their social indicators are likely to get relatively less lift from the same capital investments. What did surprise, though, was that counties with the lowest social indicators showed relatively small improvements in terms of bang for a buck.
We do not know why this is the case. Perhaps there are fewer institutions and organizations to help translate increased financial flows into positive social outcomes, or perhaps there are threshold effects, or perhaps the improvements come on dimensions of social outcomes we did not measure. We see great value in continuing research to better understand the phenomenon.
One of our early academic reviewers asked whether changes in social indicators were “zero sum” — whether changes such as increased school class size, occupancy rate increases, and other social indicators were matched by decreases in the same variables in adjacent counties. But that is not the case. Improvements do not come at the expense of surrounding areas; they appear to be “net new.”
To be clear, our model is in its first generation. It is limited to predicting the impact of capital investments of at least $100 million and only considers tangible investments like new manufacturing facilities or headquarters. It forecasts likely changes, other things equal, to about 75 social variables. Currently, the model applies only to the United States (where we have a complete data set for all counties). We think of it as proof of concept establishing a robust, but at times subtle, association between financial investment and change to social capital four years later.
We are also refining our model with better data (i.e., a broader vector of outcomes, broader geography and different statistical areas, increased data quality for the social outcomes, an expanding consideration of types and levels of investments) and modeling more types of capital investments (e.g., infrastructure) and lower amounts of capital.
There has been a long (and not always reputable) history of measuring social outcomes from businesses’ individual contributions — typically in reaction to localities’ financial concessions. However, with no consensus on what or how to measure social outcomes, it is difficult to find out after the fact whether often ill-defined goals have been met. In our work, we set out to answer a slightly different question: how specific sorts of investments affect the longer-term social outcomes defined by specific criteria.
Both companies and communities should apply more rigor to the evaluation of the impact of potential capital investments and broaden the aperture beyond simply the economics (e.g., jobs, wages, tax revenues). More jobs might come not only with lower poverty and better schools, for example, but also with a dip in local business activity or even higher divorce rates. Is that worth the tax breaks? Policymakers need a clear-eyed view of those kinds of trade-offs before they can decide.