RADIO’S IMPACT ON PUBLIC SPENDING

RADIO’S IMPACT ON PUBLIC SPENDING

D. Stromberg

The Quarterly Journal of Economics Vol. 119, No. 1 (2004) pp. 189-221

Principal Research Question and Key Result Does penetration of mass media such as radio create better informed voters that consequently receive more favourable policies? In the context of early radio expansion, this paper finds that an increase in the share of households with a radio by 1% increases spending in that area by 0.54%. 
Theory Mass media creates a distribution of informed an uninformed citizens. Informed citizens may be able to achieve better policy results. For this to occur they must vote, and they must know whether their representative has done something for them, and information from the media helps them. Thus is it more costly for politicians to neglect voters with access to political information via the media. This indicates that government spending s should be higher on groups that have access to the media, higher on groups where more people vote and voter turnout should be higher where people have access to media.The model indicates that if:

xi(uc)(zc) – βi η > ui

then the incumbent will be reelected. X is 1 if the population knows that something has been done for them. U is the utility they receive from the amount of spending Z. Beta is the ideological preference for the challenger, and Eta is the general popularity of the other candidate.

The governor knows that the voter will vote with some probability t and that the voter knows of his responsibility for the relief programme with some probability α that is an increasing function of r (radio coverage)

This generates the following propositions:

  1. If the voters cannot know if money is spent in their county or not (x = 0) then the politician has no incentive to spend there, as he will get no political credit for doing so.
  2. If Beta is distributed such that the ideological preference for the challenger is such that the incumbent cannot win, then he will not spend in that county as he will not be reelected in any case.
  3. He will allocate more funds where there are more gains to be had on the margin i.e. where turnout is higher, and there are more radios, there are more swing voters and the need for relief spending is high (where Uc is high).

 

Motivation  
Data 2500 US counties in panel from 1920-1940. This was in the middle of radio expansion, and also during the FERA programme which distributed funds to those whose income was inadequate to meet their needs. It was locally administered and local officials decided who would and would not receive the assistance. Governors were the main arbiters.
Strategy

Ln(zc) = αln(rc) + βln(tc) + δ1xc1 +εic

State specific fixed effects are also included and standard errors are clustered within state. The main hypothesis is that α>0

 

Results Factors indicating low socio-economic status are positively correlated with spending indicating that income assistance was directed to places where utility was likely to be highest (i.e. where they needed it most).The elasticity of spending with regard to radio ownership imply that increasing radio coverage by 1% would raise spending by 0.54%, and increasing turnout by 1% increases spending by 0.57%. The most important explanatory variable is unemployment which indicates that this was not just pork barrel politics, but that spending was directed where it was needed.

If radio use increases turnout, and turnout increases spending, then this is another mechanism through which radio is working. A fixed effects PANEL regression is estimated with turnout as dependent variable, with a host of controls. The coefficient on radio coverage is 0.117 and significant at 5% levels. Thus increasing radio coverage by 10% would increase turnout by 1.2%. Since every increase of 1% of turnout increases spending by 0.57% then the effect of radio on spending through turnout is 0.12*0.57 = 0.07%.

 

Robustness It is recognized that there could be bias in the estimates. Specifically, if richer counties (not otherwise captured by controls) have lots of radios, but no need of assistance then results would be downward biased. But it more people seek out radio ownership and are also better at getting their preferred policies, then this would create upward bias. In recognition of this, he implements an IV strategy, which uses geological features ground conductivity and woodland cover as instruments for radio ownership (as these variables both affect the quality of the received signal). The F-stats in the first stage are all strong. Exogeneity might be questioned, as geological features especially wood cover could be correlated with poverty or exclusion and hence relief spending which would downward bias the IV estimates. However, despite these concerns the IV results are actually more positive than the OLS results. As the author therefore takes the OLS results as his baseline, the IV just indicates the direction of the bias (i.e. people seek out radios who are better at getting what they want), and as such the main results of the paper are conservative, and this lends credence to the story.Property values, employment stats, income, wages, bank deposits etc. are all controlled for as well as share of votes in last election, voter density etc.

If the model is correct there should be more spending where elections were more closely fought. This is tested by excluding noncompetitive states, and the coefficients are nearly twice as large.

The effects should be larger in rural areas, as urban dwellers had better access to other types of media. When the specification is tested on a rural subsample the coefficient increases nearly 50%

If radio use is simply proxying for some other variable relating to the use of consumer durables then we should see similar results for other durables e.g. car ownership. Indeed gasoline sales are shown to have correlations with wages, employment etc. (just as radio does), but gasoline sales per capita are not related to spending in regressions.

 

Problems
  • This is a cross section, with data being pooled cumulatively. Panel data would have been ideal as we could see how outcomes changed with increased radio penetration, and particularly if funds are limited, then as radio coverage becomes near universal the limited pot of FERA funds may not be significant enough for use for political capital in all counties covered by radio. This would be akin to a general equilibrium effect. Panel data would have allowed.
  • Sadly no interaction terms are used. For example an interaction term between unemployment and radio coverage could have given an estimate of the differential effect that radio coverage has in the presence of a given level of unemployment, or need. This would have been interesting to see, as the levels estimates are not as readily intelligible.

 

Implications Mass media can carry politically relevant information to voters who can then use this to update their voting positions. This can make politicians more accountable as people are more likely to vote.Simply extending the franchise to the poor is not enough as this paper makes clear. What is important is how informed people are, for if certain sections are not informed as to the spending policies of the government, then such spending may be cut without fear of losing votes, and redirected to areas that may have less objective need for the spending.

As the inclusion of welfare indicators made the estimates stronger it seems clear that spending was not just directed at those who were rich enough to own radios.

The bottom line is that radio improved the relative ability of rural America to attract government transfers.

 

 

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