Accessible Unlicensed Requires Authentication Published by De Gruyter December 9, 2016

Does Eco-labeling of Services Matter? Evidence from Higher Education

Daniel C. Hickman and Andrew G. Meyer

Abstract:

Eco-labeling of services has become increasingly common, yet little empirical evidence exists concerning its effectiveness. We address this gap in the literature by analyzing a highly visible eco-label, the American College and University Presidents’ Climate Commitment (ACUPCC), in the sector of higher education. We match information about the ACUPCC to the US Department of Education IPEDS database to examine the impact of signing on student applications, admissions, and enrollment. We mainly utilize a difference-in-difference approach to identify the effects of interest but confirm results with an interrupted time series model. We find that signing the ACUPCC increases applications and admitted students by 2.5–3.5 %. However, the evidence regarding enrollment is weaker with only some specifications finding increases of around 1–2 %. Overall, there is considerable heterogeneity across sectors and selectivity of the institutions. These results show that, at the minimum, voluntary and information-based approaches (VIBAs) for services can be effective in generating visibility and influencing less-costly consumer behavior.

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Appendix: Interrupted Times Series Details

For each signatory IHE, i, define the ACUPCC effective signing year[37] as ki. Normalize by the effective signing year so that

(3)Tˆit=Titki

represents the year relative to the effective signing year. We specify a bandwidth, b, and include all IHEs in the sample that are observed for b years prior to and post signing. Then, for Tˆitb, the baseline[38] econometric model is

(4)Yit=α+γi+τ×ITˆit>ki+β1×Tˆit+β2×Tˆit×ITˆit>ki+φt+εit,

where γi are IHE fixed-effects, I(.) represents the indicator function, and φt are year fixed-effects (to account for common changes in the dependent variable over calendar years). For the baseline results, we set b=4; therefore, we include all IHEs with an effective signing year of 2009 or earlier so that each IHE has an equal number of observations prior to and post signing. Thus, τ is the effect of signing on the outcome variable, β1 is the pre-signing slope, and β2 is the change in the slope in the post-signing period relative to the pre-signing period.

Appendix Tables

Table 11:

Summary statistics of DID sampling groups.

Green GuideSignatoriesAll 4-year IHEs
All IHEsSignedNot20072008–2013SignedNot
Applications4,925.48,521.510,354.5*7,652.26,172.6*6,857.83,931.0***
Admissions2,986.35,145.25,408.54,598.13,747.0*4,141.12,392.0***
Enrolled1,072.11,728.32,007.31,537.21,300.01,409.8898.2***
Common application0.2190.3850.3620.360.340.350.15***
In-state tuition16.9917.420.5*16.217.416.917.0
Out-of-state tuition20.723.725.422.722.022.319.9***
HS graduates (1,000s)106.8113.3133.7*113.8101.9107.4106.4
Income per capita (1,000s)43.444.945.045.144.844.942.7***
Undergraduate application Fee53.364.367.961.758.760.149.6***
Average professor salary (1,000’s)94.39109.6118.9***104.2101.8102.989.9***
Private0.610.480.540.470.530.500.66***
Number of IHEs1036208105163189352684

Notes: Statistics come from the 2005–2006 academic year, before the impact of ACUPCC signing takes place. Tuition, income, application fee and professor salary variables measured in constant 2014 dollars. The number of observations is slightly lower for the tuition, application fee, and professor salary variables.

  1. indicates means different at 10 % level,

  2. indicates means different at 5 % level,

  3. indicates means different at 1 % level.

Table 12:

Regression results: levels.

(1)(2)(3)(4)(5)(6)
ApplicationsAdmissionsEnrollment
Signed183.68*186.51*87.6376.8319.50*13.66
(96.10)(101.52)(55.21)(52.50)(11.82)(12.13)
IHE-level controlsNoYesNoYesNoYes
Observations12,35311,21612,35311,21612,35011,214
R-squared0.9880.9870.9840.9870.9890.990

Notes: Table presents regression results from sample of all 4-year IHEs. Standard errors, clustered at IHE level, presented in parentheses. All specifications include IHE fixed effects, IHE-specific linear trends, and year fixed effects. IHE-level controls are common application, HS graduates, income per capita, undergraduate application fee, lagged in-state tuition, lagged out-of-state tuition and lagged professor salary. All monetary variables measured in constant 2014 dollars.

  1. denotes significance at 0.10 level.

  2. denotes significance at 0.05 level,

  3. denotes significance at 0.01 level,

Table 13:

Interrupted time series falsification tests.

(1)(2)(3)(3)
Application feeLn application feeLn tuition% tenure track
Signed0.475–0.00540–0.00527–0.000495
(0.837)(0.0105)(0.0122)(0.00700)
Observations2,6982,5822,6972,638
R-squared0.3480.3920.210.035
Number of IHEs319313319309

Notes: Table presents result from interrupted time series models, with dependent variable listed. Undergraduate application fee is measured in constant 2014 dollars and tuition is measured in thousands of constant 2014 dollars. Each specification includes linear trend variables. The bandwidth of 4 years in these models means we include IHEs signing in 2009 or earlier. Standard errors, clustered at IHE level, presented in parentheses. Each regression includes year and IHE fixed effects.

* denotes significance at 0.10  level.

** denotes significance at 0.05  level,

*** denotes significance at 0.01 level,

Published Online: 2016-12-9
Published in Print: 2016-10-1

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