Category Archives: Regional Policy



C. Criscuolo et. Al

A Short Summary 

In a Nutshell

Most governments have industrial policies that claim to foster productivity and employment etc. but it is not possible to tell if they are just financing activities that would have been undertaken regardless of state funding. Comparison of outcomes with other firms does not work as a counterfactual as the sorting into firms that receive funding is far from random. This paper uses data from a quasi-experiment where changes in the eligibility criteria for assistance for the UK Regional Selective Assistance programme caused a change in the areas that were eligible to receive funds. Using RSA data they find that there is a large effect on the treated for employment, investment and probability of exit, and these effects are seriously underestimated if endogeneity is ignored. They cannot however rule out the possibility that there are negative aggregate productivity effects from protecting inefficient incumbents 

Further Details

The EU changed the eligibility rules such that areas were subject to different constraints in terms of how much funding they could receive from the government. Generally applied to manufacturing firms who needed funds for capex to create jobs, in a viable project setting. The applicant had to demonstrate need, and should be meeting the other expenses himself or otherwise from the private sector.


Yjt = αDjt + βXjt + ηj + τt + vjt  (4)

Due to data limitations, they aggregate across all plants in the same firm, and run above regression at the firm level. Note however that they use plant level data in order to later analyse area-level impact of industrial policy (thus capturing general equilibrium effects).

yjt is outcome of interest for firm j at time t. Authors consider three outcome variables: employment, investment and productivity. Xjt are covariates used as controls that vary depending on outcome of interest.

Djt is participation dummy– authors mainly use binary indicator to reflect if the firm received any treatment.

Instrument for Djt with Zjt , which is the level of the maximum investment subsidy (Net Grant Equivalent/NGE) available in the area where the firm’s oldest plant is (oldest as then the location decision will not have been made because of changes to the EU assistance map). Baseline results use mutually exclusive dummies for each of the different rates.

They instrument for participation in the programme using the changes to the system imposed by the EU.

The data set is a panel that combines admin data on the RSA participants, and matching them to a firm level database that gives employment, investment and entry/exit information. This means they can track firms before and after participation and compare them to a control group that did not participate.


OLS results indicate a 37% increase in employment rising sensibly with the level of subsidy. The IV result is much larger suggesting serious downward bias in the OLS estimates. This is reduced to a much more sensible level when firm level dummies are included to control for heterogeneity of response to the treatment, but they are sensible and much larger than the OLS estimates with the same dummies included.

They do the same for labour productivity (measured as ration of gross output to employment) but the effects are small and insignificant.

They find a positive effect on employment at the area level (based on travel to work area) which indicates that there are spillovers from the RSA, and there is also a rise in the number of plants in an area. However, the employment effects on incumbents are stronger than an incentive to new entry as RSA dampens reallocation effects (due to less exit) and as there is no productivity effect from receiving RSA, it would appear that the scheme supports less productive firms and this could dampen aggregate productivity (especially since in the summary stats they show that the firms that receive RSA tend to be larger than the control firms).


Positive effect of programme on investment and employment but not on productivity. As the RSA helps firms to expand this could have a negative impact on aggregate productivity. Seems to be more characteristic of a welfare payment.




M. Greenstone, R. Hornbeck & E. Moretti


Principal Research Question and Key Result Are there economic spillovers that accrue to incumbent plants from agglomeration and through what mechanisms do such benefits arise? The paper finds that there are positive spillover effect on total factor productivity in incumbent firms that persist to be 12% for firms 5 years after the location of a “million dollar plant” (MDP) in the vicinity. This productivity gain seems to occur only for plants that are close in economic distance, meaning that they share worker flows, and they employ similar technologies. This is consistent with a labour force and knowledge spillover. There is no evidence for input/output based spillovers (see theory). 
Theory The entry of a new firm creates spillovers, and this leads to the entry of firms that want to benefit from these spillovers. This leads to competition for inputs and hence labour/land/other local input values rise. This continues until the value of the increased output is equal to the increased cost of production (as firms are assumed to be price takers and so cannot raise prices). This simple model yields four testable hypotheses:

  1. The opening of a new plant increases TFP of incumbent plants
  2. The increase may be larger for firms that are economically closer to the new plant
  3. Density will increase as new firms move in to take advantage of the spillovers created by the new plant (if they are large enough)
  4. The price of locally supplied factors of production will increase.

What are the possible channels for these spillover effects?

  1. Labour market – the labour market is “thicker” thanks to agglomeration. This can reduce search frictions and improve the match between worker and firm (this implies increased productivity). Alternatively/complimentarily the availability of a larger local workforce reduces the possibility that there are positions unfilled (this does not necessarily imply improved productivity, only that posts will be filled).
  2. Transportation costs – transportation costs for local suppliers of intermediate inputs and services will be lower if there is agglomeration and reduces production costs in dense areas.
  3. Knowledge spillovers – the sharing of knowledge and skills through formal and informal interactions may generate production externalities across workers. This may be important particularly in hi-tech areas (think Silicon Valley). This implies increased productivity. However, it is not clear who will gain from this productivity, as the knowledge spillovers may lead to increased investment in new technologies in which case the benefit will accrue to capital, or it may increase worker productivity in which case labour wages will gain.
  4. Amenities – local amenities are valued differently between different types of worker and firms need to be located such as to attract the right kind of worker. This implies no productivity differences between high/low density areas once type of worker is controlled for.
  5. Natural advantages – oil companies near oil fields, wine makers in the Loire. Since most natural advantages are fixed over time this is not relevant for empirical analysis which looks at changes in agglomeration over time.


Motivation Increasingly, local governments compete for big plants to locate in their region by offering generous subsidies. The main economic rationale for doing so is that these plants create agglomeration spillovers which benefit the local economy. Yet we lack any rigorous testing of these effects, and if they are in fact found to be small, then this questions the use of taxpayers’ money being used to finance such subsidies. 
Data They identify 47 usable MDP openings. They combine this with information on incumbent plants in the winning/losing county (of which there had to be at least one pair of incumbents for the MDP to qualify) including capital stocks, materials, value of shipments, etc. from annual manufacturing surveys. The focus on existing plants eliminates problems of endogenous openings of new plants.In order to investigate mechanisms they code variables relating to % of output sold to manufacturers, % of inputs from same three digit industry, labour market transitions between industries, % of patents manufactured in each three digit industry, R&D expenditure.


Strategy Firms do not randomly locate, therefore simple comparison of regions is not appropriate for obvious reasons of endogeneity. So the authors use an industry property source to compare regions that did get MDPs with the regions that narrowly lost out to winning those same plants, on the assumption that those two regions are significantly similar in TFP enough to allow for use of the “loser” and control for the “winner” – formally the assumption is that but for the location of the MDP the TFP in the winner and loser regions would have developed identically. The summary statistics show that observables are generally balanced between winning and losing counties relative to the rest of the USA, and even more balanced as between firms in winning/losing counties indicating that the identifying assumption is quite strong. The authors state that even if it is not perfect, this is still a better method than simple comparison.The strategy is pretty extensive. The dependent variable is TFP defined as total value of shipments minus changes in inventory. The left hand side includes time trend dummies, a dummy equal to 1 if the plant is in a winning county and 0 otherwise. There is also a dummy that turns on when the MDP is open. Then there is:

α[(Winner)*(Open)] which is the difference in difference estimator, with alpha as the effect of being in a winning county in a time when a MDP has opened.

β[(Winner)*(Open)*(Trend)]  where Trend are the time dummies. Beta shows the differential effect being in a winning county with a MDP has on time trends beyond the year that the plant opens and the Open dummy turns on
They actually do two estimations, one which is the simple DID (which sets the Winner*Open*Trend coefficient to 0, and a full estimation where it is allowed to vary.  I.e. the specification allows for a mean shift and trend break.

They include plant and industry and region fixed effects.


Results Differences in TFP in the years before the opening of an MDP as between winning and losing counties are all small and insignificant. In the years following the opening coefficients on the Winner*Open year specific dummies become significant at the 5% level. This reveals a sharp upward break in the difference between the TFP of the counties (which is in fact a decline in the losing counties relative to a flattening out in the winning counties – showing the importance of the losing counties counterfactual, as if not included negligible results would have been found). The coefficients from suggest a mean shift of 4.8% in TFP in winning counties relative to losing counties. Moreover, when the second model is used that includes separate time dummies, the effect seems to be getting stronger as time passes, so having an MDP is associated with a 12% increase in TFP in winning counties in year 5 after opening which confirms the importance of including the trend break. These numbers translate into approx. $170m in year one and $430m increase in total output in year 1 and 5 respectively.Coefficient on the pre-opening trend is not significant which lends support to the identifying assumption.

There is significant heterogeneity, and in some specific cases there are negative effects of introducing a MDP.

The increase in TFP in incumbent plants seems to come from incumbent plants producing more with less after the MDP opening.

They investigate the mechanisms at work. When they compare results for incumbents in the same 2 digit industry the effects are much greater, and no significant effects are found for other two digit industries. The construct measures of PROXIMITY that captures worker flows, technological proximity, input-output flows, and re-estimate interacting it with OPEN*WINNER*PROXIMITY. All coefficients are significant except flow of goods/services. Thus there is evidence that intellectual or technological linkages, sharing workers increase the spillover – i.e. intellectual spillovers.

If these spillovers are big enough there should be new entrants, and indeed a DID with log(plants) as depvar is positive and significant – i.e. new economic activity was attracted. They also find that wages increase as demand for labour increases.


Robustness There could be unobserved productivity shocks coincidental to the opening of the MDP. They do a variety of specification checks; they allow inputs to be endogenous. Also since MDPs are associated with public good investment etc. this could be what is driving the results, but they find no meaningful relationship between government expenditure in the area and the plant opening.There could have been differential attrition, however, in the winning plants 72% remained at end of period, and this was 68% in losing plants i.e. very similar.


Problems The theoretically correct depvar is quantity, but this is not comparable across plants. So they have to use value measures. In that sense, the results could be reflecting some change in price rather than a change in actual productivity.Do the results hold for smaller plants and plants outside the manufacturing industry?


Implications There do seem to be externalities associated with the location of MDPs. However it is not clear that this is a justification for offering generous subsidies. In particular it may be the case that the plant will locate domestically no matter what, in which case subsidies are wasteful from a national perspective. Even at local level it is probable that all the gains will be bargained away, and hence they will be zero sum games.However, given that there is significant heterogeneity of effects there could still be a place for subsidies. For example as spillovers are greatest where there is economic proximity, MDPs should be encouraged to locate where the spillovers will be largest. The MDP may locate only where its profits will be highest (as it does not itself benefit from the spillovers it creates). In this case, some subsidy should be offered to internalize the benefit they provide and in so doing encourage them to locate where the spillovers will be greatest.