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The welfare ranking of prices and quantities under noncompliance

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Abstract

We study the welfare ranking of an emission tax and emissions trading when firms self-report their emissions to the regulator and may be noncompliant. We allow for the subjective probabilities of auditing and, using conventional assumptions, find that an emission tax produces a higher level of welfare than emissions trading under noncompliance. The main driver of the result is that the compliance pattern of the firms affects the marginal compliance cost (price of emissions) in the case of permits, thus affecting the level of emissions and welfare. The result also holds when enforcement and sanctioning costs are taken into account and differs from the result found by Montero (J Public Econ 85:435–454, 2002). We also show that the ranking may be reversed if these costs are taken into account and the regulator must audit at least one firm. We also analyze the welfare ranking when the expected penalties depend on relative violations.

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Notes

  1. Montero’s model is used in Rohling and Ohndorf (2012) to analyze the effect of fiscal cushioning in the choice between emissions trading and emission tax. They found evidence in favor of emissions trading.

  2. The existing literature on noncompliance and on the comparison of different instruments has mainly focused on enforcement costs (Malik 1992), emission levels (Keeler 1991) and the incentives to adopt new abatement technology (Arguedas et al. 2010; Villegas-Palacio and Coria 2010).

  3. This assumption is used, for example, in Malik (1992), Stranlund and Dhanda (1999), Montero (2002), Macho-Stadler and Pérez-Castrillo (2006), Stranlund (2007), Stranlund et al. (2009) and Stranlund and Moffitt (2014).

  4. For example, Malik (1990), Van Egteren and Weber (1996), Malik (2002), Hatcher (2005) and Hatcher (2012).

  5. For example, a firm may think that its choice of violation affects the chance for the firm to belong to the set of audited firms. This is something like speeding on the motorway; the probability of the police stopping you is essentially subjective and may depend on how much over the limit you are speeding.

  6. From page 91.

  7. As Hatcher (2012) explains, expected penalties may depend on the relative violations, since penalties in addition to probabilities may depend on the relative violations.

  8. Throughout we assume that the firms must self-report their emissions to the regulator. For example, in the EU ETS, self-reporting is carried out during accredited inspections and compliance checks by the competent authority.

  9. We assume throughout the paper that the regulator can commit to the monitoring scheme and that the firms regard the scheme credible. See Franckx (2002) for an analysis of the case where the regulator cannot commit to the auditing probability it sets.

  10. Most of the research has assumed absolute violations. To our knowledge, only Hatcher (2005, 2012) and Rousseau and Proost (2005, 2009) have analyzed relative violations.

  11. Relative violations are discussed in Sect. 3.

  12. These assumptions are similar to the ones made in Van Egteren and Weber (1996) and Malik (2002). Sandmo (2002) thinks that it is more realistic to allow the auditing probability to depend on the level of violation. Also, among many others, Harford (1978) and Rousseau and Proost (2005, 2009) assume that the auditing probability is increasing in violations.

  13. Sandmo (2002) sees \(m_i'(v_i)>0\) and \(m_i''(v_i)>0\) as ”natural” assumptions. If we allowed \(m_i''(v_i)\ge 0\), we should also analyze corner solutions. However, we are only interested in interior solutions.

  14. Perhaps because firm’s subjective auditing probability depends on parameters that are characteristic to that firm such as its stakeholders or its geographical distance to the regulator.

  15. This follows from the concavity of the benefit function: Assume that firm \(i\) is compliant when there are \(n-k\) compliant firms and noncompliant when there are \(n-(k+1)\) compliant firms. The other firms’ behavior between compliance and noncompliance is fixed. Then

    $$\begin{aligned} \pi _i'(e_i^h)=p_{n-k}>p_{n-(k+1)}=\pi _i'\left( e_i^d\right) , \end{aligned}$$

    where the last equation follows after summing Eqs. (3) and (4). The (strict) concavity of the benefit function \(\pi \) implies then that \(e_i^h<e_i^d\).

  16. The result in the lemma has also been observed, for example, in Sandmo (2002).

  17. The strict convexity of \(D\) is not actually needed for the main results.

  18. This assumption is sometimes used in the literature (Van Egteren and Weber 1996; Malik 2002). It is possible, for example, that the regulator believes that the firms see the probability of auditing objectively and that the enforcement is set such that \(S_i'(0)\ge p\) holds for every firm.

  19. Note that the argument of function \(D\) is not the total initial allocation, but the total actual emissions at price \(p\).

  20. A similar argument is also given in the analysis made by Arguedas et al. (2010) in the case of investing in abatement technology.

  21. We could let the enforcement costs depend continuously and smoothly on the level of auditing, namely we could let the enforcement costs be a function \(\mathcal {E}(a^j)\) with \(\mathcal {E}'(a^j)>0\), where \(a^j\in [0,1)\) is the proportion of firms audited. The discrete choice is more intuitive, though.

  22. Stranlund (2007) has analyzed enforcement and sanctioning costs in an emissions trading model but uses an objective auditing probability. Also, Stranlund and Moffitt (2014) include these costs in their analysis on hybrid policies and noncompliance, but they too use an objective auditing probability.

  23. The penalty function is given. Of course, at least the unit penalty can also be chosen by the regulator (as well as many other factors). We abstract away from these possibilities.

  24. Numerical analysis may be useful in this case.

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Acknowledgments

The author thanks Markku Ollikainen, Essi Eerola, Anastasios Xepapadeas, Arun Malik, and Nils-Henrik von der Fehr for their helpful comments. Financial support from the OP-Pohjola Group Research Foundation is gratefully acknowledged.

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Correspondence to Pauli Lappi.

Appendix

Appendix

1.1 Proof of Lemma 1

Fix \(k =0,\ldots ,n-1\) and define a function \(F :\mathbb {R}_+ \rightarrow \mathbb {R}\) with

$$\begin{aligned} F(p)=\sum _{i=1}^{k} \hat{e}_i(p)+\sum _{i=k+1}^{n} e_i^h(p)-E^0. \end{aligned}$$
(38)

The function \(F\) is strictly decreasing in \(p\) since emission reports and actual emissions are strictly decreasing in \(p\). Since \(p_{n-k}\) is the equilibrium price with \(k\) noncompliant firms, we have \(F(p_{n-k})=0\).

We evaluate the difference \(F(p_{n-k})-F(p_{n-(k+1)})\) as follows.

$$\begin{aligned} F\left( p_{n-k}\right) -F\left( p_{n-(k+1)}\right)&=\sum _{i=1}^{k} \hat{e}_i(p_{n-k})+\sum _{i=k+1}^{n}e_i^h\left( p_{n-k}\right) -E^0 \nonumber \\&\quad -\sum _{i=1}^{k} \hat{e}_i\left( p_{n-(k+1)}\right) -\sum _{i=k+1}^{n}e_i^h\left( p_{n-(k+1)}\right) +E^0 \nonumber \\&=-\sum _{i=1}^{k} \hat{e}_i\left( p_{n-(k+1)}\right) -\sum _{i=k+1}^{n}e_i^h\left( p_{n-(k+1)}\right) +E^0 \nonumber \\&<-\sum _{i=1}^{k+1} \hat{e}_i\left( p_{n-(k+1)}\right) -\sum _{i=k+2}^{n} e_i^h\left( p_{n-(k+1)}\right) +E^0=0, \end{aligned}$$
(39)

where the inequality follows from the definition of noncompliant behavior, that is, from the inequality \(\hat{e}_i(p)<e_i^h(p)\) for any \(p\) and for any noncompliant firm, and the last equality follows from the market clearing condition for \(k+1\) noncompliant firms.

Now we show that \(p_{n-k}>p_{n-(k+1)}\) by contradiction. Assume that \(p_{n-k}\le p_{n-(k+1)}\) holds. If \(p_{n-k}=p_{n-(k+1)}\), then \(F(p_{n-k})=F(p_{n-(k+1)})\), contradicting (39), and if \(p_{n-k}<p_{n-(k+1)}\), then \(F(p_{n-k})>F(p_{n-(k+1)})\), since \(F\) is strictly decreasing, which again contradicts (39). To conclude, the assumption \(p_{n-k}\le p_{n-(k+1)}\) leads to a contradiction implying that the inequality \(p_{n-k}> p_{n-(k+1)}\) holds.

1.2 Proof of Lemma 3

If \(v_i=0\), we have from Eq. (28) that \(S'_i(0)\frac{1}{e_i }=p+\lambda \ge p\). We give a proof for the other direction by showing that \(v_i>0\) implies that \(S'_i(0)\frac{1}{e_i }<p\). When \(v_i>0\), we have \(\lambda _i=0\). Therefore equality

$$\begin{aligned} S'_i\left( \frac{e_i-\hat{e}_i}{\hat{e}_i}\right) \frac{e_i}{\hat{e}_i^2}=p \end{aligned}$$
(40)

holds. Using the strict convexity of \(S_i\) and the inequality \(e_i>\hat{e}_i\), we obtain

$$\begin{aligned} p=S'_i\left( \frac{e_i-\hat{e}_i}{\hat{e}_i}\right) \frac{e_i}{\hat{e}_i^2}>S'_i\left( \frac{e_i-\hat{e}_i}{\hat{e}_i}\right) \frac{1}{e_i}>S'_i(0)\frac{1}{e_i}. \end{aligned}$$
(41)

1.3 Proof of Lemma 4

Part (i). (We assume differentiability where ever necessary.) We first use Theorem 5 (Topkis’s Theorem) in Milgrom and Shannon (1994) to show that the actual and reported emissions are nonincreasing in the permit price. The objective function \(\Pi _i^p\) is supermodular in \((e_i,\hat{e}_i)\) and satisfies increasing differences in \((e_i,\hat{e}_i;-p)\), since

$$\begin{aligned} \frac{\partial ^2 \Pi _i^p}{\partial e_i \partial \hat{e}_i}&=S''_i\left( \frac{e_i-\hat{e}_i}{\hat{e}_i}\right) \frac{e_i}{\hat{e}_i^3}+S'_i\left( \frac{e_i-\hat{e}_i}{\hat{e}_i}\right) \frac{1}{\hat{e}_i^2}>0, \end{aligned}$$
(42)
$$\begin{aligned} \frac{\partial ^2 \Pi _i^p}{\partial e_i \partial (-p)}&=0, \quad \text { and } \quad \frac{\partial ^2 \Pi _i^p}{\partial \hat{e}_i \partial (-p)}=1>0. \end{aligned}$$
(43)

Therefore, \(\mathop {\text {argmax}}_{(e_i,\hat{e}_i) \in X} \Pi _i^p(e_i,\hat{e}_i;-p)\), where \(X\) is the feasible set \(\{ (e_i,\hat{e}_i) \in \mathbb {R}^2 \ | \ e_i>0, \hat{e}_i>0, e_i-\hat{e}_i\ge 0 \}\), is nondecreasing in \(-p\) or equivalently nonincreasing in \(p\). In particular,

$$\begin{aligned} \frac{\partial e_i(p)}{\partial (-p)}\ge 0. \end{aligned}$$
(44)

Now we show that the reported emissions of a noncompliant firm are strictly decreasing in the permit price. We do this by applying the aggregation method described in Milgrom and Shannon (1994) together with Strict Monotonicity Theorem 1 (SMT1) in Edlin and Shannon (1998). Define

$$\begin{aligned} K_i\left( \hat{e}_i; -p\right) := \max _{e_i> \hat{e}_i} \Pi _i^p\left( e_i,\hat{e}_i;-p\right) , \end{aligned}$$
(45)

where \(\hat{e}_i \in \mathbb {R}_{++}\). The function \(K_i\) has increasing marginal returns (that is, \(\frac{\partial K_i}{\partial \hat{e}_i}\) is increasing in \(-p\)), since

$$\begin{aligned} \frac{\partial ^2 K_i}{\partial \hat{e}_i \partial (-p)}=\frac{\partial ^2 \Pi _i^p}{\partial \hat{e}_i \partial e_i }\frac{\partial e_i}{\partial (-p)}+1 > 0 \end{aligned}$$
(46)

by (44). Let \(-p'>-p^*\) (the other case is derived similarly) and define

$$\begin{aligned} \hat{e}_i^*&=\mathop {\text {argmax}}\limits _{\hat{e}_i \in \mathbb {R}_{++}}K_i(\hat{e}_i; -p^*) \quad \,\text { and } \end{aligned}$$
(47)
$$\begin{aligned} \hat{e}_i'&=\mathop {\text {argmax}}\limits _{\hat{e}_i \in \mathbb {R}_{++}}K_i(\hat{e}_i; -p'). \end{aligned}$$
(48)

From the beginning of the proof, we know that \(\hat{e}_i'\ge \hat{e}_i^*\), which can be strengthened to \(\hat{e}_i'> \hat{e}_i^*\) by (46) and by SMT1. In other words, we have that \(p'<p^*\) implies \(\hat{e}_i'> \hat{e}_i^*\), as desired.

Part (ii). We can use the same reasoning as in the proof of Lemma 1, since the reported emissions (and the actual) are strictly decreasing in the permit price.

1.4 Proof of Lemma 5

A noncompliant firm chooses its emissions and reported emissions such that

$$\begin{aligned} \pi _i'(e_i^d)-t+S_i'\left( \frac{e_i-\hat{e}_i}{\hat{e}_i}\right) \left[ \frac{e_i^d}{\hat{e}_i^2}-\frac{1}{\hat{e}_i}\right] =0. \end{aligned}$$
(49)

The term in the brackets is positive, since \(e_i^d>\hat{e}_i\). When the firm is compliant, equation \(\pi _i'(e_i^h)=t\) holds. Since \(\pi _i''<0\), we have \(e_i^d>e_i^h\).

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Lappi, P. The welfare ranking of prices and quantities under noncompliance. Int Tax Public Finance 23, 269–288 (2016). https://doi.org/10.1007/s10797-015-9356-1

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