Skip to main content

Advertisement

Log in

Product Market Competition and Lobbying Coordination in the U.S. Mobile Telecommunications Industry

  • Published:
Journal of Industry, Competition and Trade Aims and scope Submit manuscript

Abstract

This paper empirically investigates market behavior and firms’ lobbying in a unified structural setup. In a sequential game, where firms lobby for regulation before they compete in the product market, we derive a testable measure of lobbying coordination. Applying the setting to the early U.S. cellular services industry, we find that lobbying expenditures, as measured by campaign contributions, and market conduct were consistent with a one-shot Nash equilibrium and that price caps were binding on average. Furthermore, campaign contributions from cellular firms effectively lowered the burden of the price caps and reduced production costs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. We interpret various types of regulation as being, in effect, a form of price cap regulation. See Section 2 for details.

  2. For example, see the State Highway Maintainance Manual issued by the Wisconsin Department of Transportation (DOT) in 1996, policies 96.31 and 96.41 (Wisconsin Department of Transportation 1996), which regulate longitudinal antenna installations on freeways: one among many requirements is that the utility shall pay a full-time inspector representing the DOT during the installation period. This illustrates the cost-related state level regulatory issues in this industry.

  3. We abstract from lobbying for entry regulation, because the market structure of the U.S. mobile telecommunications industry was settled on a long-run basis at the federal level before the sample period. See Hazlett and Michaels (1993) for a rent-seeking analysis of this process.

  4. Ansolabehere et al. (2002) find a strong positive association between PAC (Public Action Committee) contributions and actual expenses of registered lobbyists.

  5. Exceptions include Goldberg and Maggi (1999), Gawande and Bandyopadhyay (2000), and Eicher and Osang (2002) who structurally test whether industrial lobbying has successfully influenced trade protection. Interest groups’ lobbying decisions are, however, exogenous in these contributions.

  6. In a different modeling approach Grossman and Helpman (1994) study trade protection lobbying in a framework, where (exogenously given) interest groups bid contingent on future policy decisions. They do not, however, model the market game, even though they show that competition among rivaling interest groups shapes their policy preferences.

  7. The market data originate from many different sources, such as Cellular Price and Marketing Letter, Information Enterprise, Cellular Business, Cellular Market Data Book, EMCI, BOMA Experience Exchange Report, U.S. Department of Energy, U.S. Department of Labor, Bureau of Labor Statistics, U.S. Department of commerce, and Bureau of Census. We refer the interested reader to Parker and Röller (1997) for a more precise description of the market data. We are very grateful to Phil Parker and Lars-Hendrik Röller for allowing us to use their data.

  8. In particular, we thank Douglas Weber from the Center for Responsive Politics for making available the unpublished data on political contributions for our sample period.

  9. The price of a singular cellular operator is defined as the monthly bill paid by a costumer for 500 min of usage, assuming that he chooses the least expensive among the different plans offered. Since output levels are not directly observable, the quantity is proxied by the number of cellular antenna sites used by operators. Parker and Röller calculated from a sub-sample with available output measures a correlation index between the number of antennas and the number of subscribers equal to 0.92 (p-value < 0.0001).

  10. High potential business establishments include the number of firms engaged in business services, health care services, professional and legal services, contract construction, transportation, finance, insurance, and real estate.

  11. In principle it would be desirable to distinguish among different flavors of price cap regulation, such as explicit and implicit caps. Due to the limited number of states in each sub-category this paper does not differentiate among different types of price caps. Furthermore, our price cap variable excludes New York and South Carolina from the list of price cap regulated states. Officially these states imposed caps. The caps were, however, set by the companies.

  12. Notice that the presence of a Bell company is significantly negatively correlated with the dummy for regulation, which suggests that these firms might be better able to avoid being regulated.

  13. Quantity competition is assumed, although the actual game in the early cellular industry is better understood as a pricing game with capacity constraints. For the two models to be equivalent, equilibrium prices in the latter must be such that the capacity constraint is binding (see Kreps and Scheinkman 1983). For the U.S. cellular industry, where capacitity is determined by the number of antennas, while the number of subscribers or air time minutes reflect actual production, we argue that the equivalence holds at least approximately during the industry’s early development phase for two reasons. First, upon receiving licenses from the Federal Trade Commission, operators were not obliged to immediately cover the entire market with antennas. The data reveals that, indeed, the licencees did not install a huge capacity in the beginning of their business, but rather extended their networks gradually. Second, capacity and production measured by the number of subscribers are closely related (see footnote 9).

  14. Because the matrix \(X_{s}^{P}\) does not contain outcomes of competition in the cellular market, the regulators are assumed to be completely captive to campaign contributions. A structural investigation of regulators’ behavior would require actually observing the price caps, as well as a formalization of the different forms of cost-related regulations.

  15. This assumption is motivated by the data. The regimes were determined before the markets actually started to operate and very rarely changed. Amendments occurred only towards less regulation, reflecting a general political trend during the 80s. Within our model firms in regulated states can de facto abolish the price cap by increasing it through lobbying to a sufficiently high level.

  16. Strictly speaking, price cap regulation implies for each firm i a residual inverse demand function with a kink at a critical quantity \(\overline{Q}_{ims}\) that is derived from market demand at a price \(\overline{P}_{s}\) and the rivals’ production. For \(Q_{ims}\leq\overline {Q}_{ims}\) the inverse residual demand is flat at the level of \(\overline {P}_{s}\), while for\(\ Q_{ims}\geq\overline{Q}_{ims}\) it has the same shape as without a price cap. Provided the usual regularity conditions for demand hold, profit maximization involving such a kinked inverse demand is analytically identical to maximizing profits with the original inverse demand function and subject to Eq. 2.

  17. It is straightforward to set up an alternative model where firms choose lobbying expenditures and quantities simultaneously. In order to be reasonable in the current context, such a model requires lobbying to affect policy with a lag—otherwise it would imply that firms do not observe the price cap which actually applies to their production decisions. The sample in our empirical application is too short in the time dimension and too unbalanced to accommodate the lagged version.

  18. A standardized conduct measure establishes a monotonic relationship between conduct and equilibrium choices of endogenous variables, irrespective of the number of players. This is important for empirical analysis where conduct is estimated as a constant parameter for observations with a varying number of players.

  19. Throughout this section, Greek characters denote parameters. Exogenous variables, as well as their corresponding parameters, should be read as vectors.

  20. We use the first index in error terms to denote the type of equation and the remaining to indicate the observation. We refrain from a distinction between the within-firm and across-firms variation because the panel that we use is unbalanced with many firms being represented only once.

  21. In principle \(\overline{P}\) could be estimated from the observed lobbying expenditures and other variables. However, due to the lack of a full structural model for the policy choice such estimates cannot be expected to be precise enough to satisfactorily sort out the binding constraint cases from the data.

  22. Note that Eq. 11 has two solutions in the endogenous variable Q ms . One of these solutions is excluded by concavity of the maximization problem in Q ims (sufficient condition: α 1 ≥ 0, β 1 ≤ 0) and the fact that the parameters θ Q and ϕ as well as the optimal quantity must be positive.

  23. This implies summing the first-order conditions for lobbying of all firms operating in the same state.

  24. State and time fixed effects would be alternative instruments for lobbying. The structure of our data, however, prevents us from adopting them. Furthermore, we do believe that there is a benefit per se in detecting if and how political variables impact on firms’ ability to lobby.

  25. In all iterations we restricted ϕ to be greater or equal to zero. While this might have had an effect on the search direction for the parameter estimates, it was not a binding constraint upon convergence. Imposing the remaining Kuhn-Tucker conditions \(\overline{P}(\cdot )-P(\cdot )\geq 0\) and \(\phi ( \overline{P}(\cdot )-P(\cdot )) =0\) could improve the efficiency of the delta and beta parameters. Since ϕ is estimated as a constant and, furthermore, because we consider the empirical implementation of \(\overline{P}(\cdot )\) and P(·) as approximations to their true functional form, we refrain from imposing these additional requirements.

  26. Estimating a model containing the full set of PUC and state variables led to problems in empirically identifying all of their parameters.

  27. The estimates in the inverse demand equation and marginal cost function qualitatively reproduce the findings by Parker and Röller (1997).

  28. The cost factors ENERGY, OPERATE, and PRIME were omitted in the final specification, because their effect appeared to be collinear to the effects of the year and RENT. The inference regarding the impact of the industry’s campaign contributions on costs is not affected by this omission.

  29. For a rigorous statistical test we would have to evaluate the empirical significance of \(dQ_{ims}^{\ast}/dL_{s}\) as specified in Eq. 12 taking into account the variation of all variables and parameter estimates in the denominator.

  30. In both, Tables 6 and 7, \(\widehat{\delta }_{0}\) is significant only at the 10% level. The loose relationship between campaign contributions and price caps may result from the fact that campaign contributions are not a perfect measure of firms’ attempt to influence price regulation. Indeed, the connection between campaign contributions and price caps is more indirect than the link between contributions and cost reducing policy decisions: While politicians have a very direct impact on the laws passed, they can only try to exert influence on members of the Public Utility Commissions, who eventually decide on the level of price caps.

  31. We also find Nash behavior in the Cournot game by estimating inverse demand and the quantity relation with exogenous lobbying (θ Q = 0.64, t-value 1.244 for the difference to Nash behavior and 3.2 for the null hypothesis of cartel). The remaining market parameters are very similar to those displayed in Tables 4 and 5 with the notable exception of ϕ, which is not significant. Hence, ignoring the endogeneity of lobbying seems to underestimate the relevance of the price caps in regulated markets. Consequently, we would underestimate the effectiveness of the price regulation applied to the cellular industry, if its endogeneity, which is induced by the firms’ incentive to lobby, were ignored.

  32. The necessary adjustments to the estimation equations are discussed in Appendix B.

  33. Table 8 excludes most estimates, since they were very similar to those displayed in Tables 4 and 5.

  34. As noted in Appendix B, firm varying α 2i imply that firms’ quantities within each market will generally differ. With only market-level data available, the specification of the lobbying equation in Eq. 13 is based on averaging, since it relies on the assumption that Q ims  = Q ms /I ms . In order to test the reliability of the α 2i estimates in Table 8, we also estimate the market equations separately, because the market-level quantity relation is additively separable in Q ims and thus not sensititve to asymmetry. With the caveat of ignoring the endogeneity of lobbying, the results confirm the findings shown in Table 8.

  35. The difference between Bell companies and their independent rivals might also be due to the former having more experience with regulation in the telecommunication sector, which led them to learn that they were more effective in lobbying for cost-reducing measures than in attempting to persuade regulators to set higher price caps. This argument put forward by Teske (1991) is, however, beyond the scope of our model.

  36. The results of the robustness checks are not displayed in the appendix but they can be obtained from the authors on request.

  37. In a sensitivity check, we imposed σ PL  = σ QL  = 0. The estimates are qualitatively not affected by this change.

  38. Note that, although marginal costs are now a firm-specific function, the aggregation of the quantity relation to the market level does not require symmetry regarding production. This is due to the linearity of C i (·) in Q ims .

References

  • Ansolabehere S, Snyder JM, Tripathi M (2002) Are PAC contributions and lobbying linked? New evidence from the 1995 Lobby Disclosure Act. Bus Polit 4:131–155

    Article  Google Scholar 

  • Baron D (1999) Integrated market and nonmarket strategies in client and interest group politics. Bus Polit 1:7–34

    Google Scholar 

  • Besley T, Case A (2000) Unnatural experiments? Estimating the incidence of endogenous policies. Econ J 110:F672–F694

    Article  Google Scholar 

  • Besley T, Case A (2003) Political institutions and policy choices: evidence from the United States. J Econ Lit 41:7–23

    Article  Google Scholar 

  • Bresnahan TF (1989) Empirical studies in industry with market power. In: Schmalansee R, Willig RD (eds) Handbook of industrial organization. North-Holland, Amsterdam, pp 1011–1057

    Chapter  Google Scholar 

  • Corts KS (1999) Conduct parameters and the measurement of market power. J Econom 88:227–250

    Article  Google Scholar 

  • Damania R, Fredriksson PG (2000) On the formation of industry lobby groups. J Econ Behav Organ 41:315–335

    Article  Google Scholar 

  • Damania R, Fredriksson PG (2002) Trade policy reform, endogenous lobby group, and environmental policy. J Econ Behav Organ 15:1–23

    Google Scholar 

  • Duso T (2005) Lobbying and regulation in a political economy: evidence from the US cellular industry. Public Choice 122(3–4):251–276

    Article  Google Scholar 

  • Duso T, Röller L-H (2003) Endogenous deregulation: evidence from OECD countries. Econ Lett 81:67–71

    Article  Google Scholar 

  • Duso T, Jung A (2007) Market conduct and endogenous lobbying: evidence from the US mobile telecommunications industry. J Ind Compet Trade 7:9–29

    Article  Google Scholar 

  • Eicher T, Osang T (2002) Protection for sale: an empirical investigation: comment. Am Econ Rev 92:1702–1710

    Article  Google Scholar 

  • Gawande K, Bandyopadhyay U (2000) Is protection for sale? Evidence on the Grossman–Helpman theory of endogenous protection. Rev Econ Stat 82:139–152

    Article  Google Scholar 

  • Goldberg P, Maggi G (1999) Protection for sale: an empirical investigation. Am Econ Rev 89:1135–1154

    Article  Google Scholar 

  • Grossman G, Helpman E (1994) Protection for sale. Am Econ Rev 84:833–850

    Google Scholar 

  • Hazlett TW, Michaels RJ (1993) The cost of rent-seeking: evidence from cellular telephone license lotteries. South Econ J 59:425–435

    Article  Google Scholar 

  • Kreps DM, Scheinkman J (1983) Quantity precommitment and Bertrand competition yield Cournot outcomes. Bell J Econ 14(2):326–337

    Article  Google Scholar 

  • Ludema RD (2001) Market collusion and the politics of protection. Eur J Polit Econ 17:817–833

    Article  Google Scholar 

  • Olson M (1965) The logic of collective action. Harvard University Press, Cambridge

    Google Scholar 

  • Parker P, Röller L-H (1997) Collusive conduct in duopolies: multimarket contact and cross-ownership in the mobile telephone industry. RAND J Econ 28:304–322

    Article  Google Scholar 

  • Puller SL (2007) Pricing and firm conduct in California’s deregulated electricity market. Rev Econ Stat 89:75–87

    Article  Google Scholar 

  • Röller L-H, Sickles RC (2000) Capacity and product market competition: measuring market power in a puppy-dog industry. Int J Ind Organ 18:845–865

    Article  Google Scholar 

  • Shew WB (1994) Regulation, competition, and prices in the US cellular telephone industry. Working paper, American Enterprise Institute

  • Teske P (1991) Rent-seeking in the deregulatory environment: state telecommunications. Public Choice 68:235–243

    Article  Google Scholar 

  • The Council of State Governments (1984) The book of the states 1984–1985, vol 25. The Council of State Governments, Lexington

    Google Scholar 

  • The Council of State Governments (1986) The book of the states 1986–1987, vol 26. The Council of State Governments, Lexington

    Google Scholar 

  • The Council of State Governments (1988) The book of the states 1988–1989, vol 27. The Council of State Governments, Lexington

    Google Scholar 

  • US Bureau of Census (1989) Statistical abstract of the United States: 1989, 109th edn. US Bureau of Census, Washington, DC

    Google Scholar 

  • Wales TJ, Woodland AD (1983) Estimation of consumer demand systems with binding non-negativity constraints. J Econom 21:263–285

    Article  Google Scholar 

  • Wisconsin Department of Transportation (1996) State highway maintainance manual. Policies 96.31, 96.41

Download references

Acknowledgements

We are indebted to Marc Ivaldi and Lars-Hendrik Röller for their advice and to Raja Chakir for discussing aspects of the empirical implementation. We are also grateful to Zava Aydemir, Christopher Klein, Eugenio Miravete, Jennifer Rontganger, Ralph Siebert, and seminar audiences at the WZB, the IUI in Stockholm, the meetings of the EEA, EARIE, IIOC, SEA, and EC-2, as well as three anonymous referees for their comments. The second author would like to thank the IDEI in Toulouse, where part of this paper was completed, for their hospitality. Both authors gratefully acknowledge partial financial support from the Deutsche Forschungsgemeinschaft grant number Ro 2080/4. Tomaso Duso also gratefully acknowledges financial support from the Deutsche Forschungsgemeinschaft through SFB/TR 15.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tomaso Duso.

Additional information

A. Jung is an Independent Economic Consultant.

Appendices

Appendix A: The log-likelihood function

The FIML estimation applied in this study matches the specific data structure: policy and lobbying decisions are taken at the state level but each state contains an idiosyncratic number of markets, M s . Denote the vector of residuals for state s with \({\boldsymbol\varepsilon}_{s}\), with \( \dim ({\boldsymbol\varepsilon}_{s})=2M_{s}+1\). The residuals are a vector valued function f s of all endogenous variables \(\mathbf{y} _{s}=(P_{1s},\ldots P_{M_{s}s},Q_{1s},\ldots Q_{M_{s}s},L_{s})^{\prime}\) and all exogenous variables x s :

$$ \mathbf{\varepsilon}_{s}=\mathbf{f}_{s}(\mathbf{y}_{s},\mathbf{x}_{s}). $$

The log-likelihood of estimating Eq. 13, M s inverse demand equations (Eq. 8), and M s quantity setting equations (Eq. 11) by nonlinear FIML is

$$ l=const+\sum\limits_{s}\ln|\det\mathbf{J}_{s}|+\frac{1}{2} \sum\limits_{s}\ln(\det\boldsymbol{\Sigma}_{s}^{-1})-\frac{1}{2}\sum\limits_{s} \mathbf{f}_{s}^{\prime}\boldsymbol{\Sigma}_{s}^{-1}\mathbf{f}_{s}, $$
(14)

where \(\boldsymbol{\Sigma}_{s}\) is the state specific covariance and \(\mathbf{J} _{s}=\partial\mathbf{f}_{s}/\partial\mathbf{y}_{s}^{\prime}\). Rewriting \( \boldsymbol{\Sigma}_{s}\) yields

$$ \left(\begin{array}{lll} \boldsymbol{\Sigma}_{P} & \boldsymbol{\Sigma}_{PQ} & \boldsymbol{\Sigma}_{PL} \\ \boldsymbol{\Sigma}_{PQ} & \boldsymbol{\Sigma}_{Q} & \boldsymbol{\Sigma}_{QL} \\ \boldsymbol{\Sigma}_{PL}^{\prime} & \boldsymbol{\Sigma}_{QL}^{\prime} & \sigma_{L} \end{array} \right) , $$

where \(\boldsymbol{\Sigma}_{P}\) and\(\ \boldsymbol{\Sigma}_{Q}\) are covariance matrices of the inverse demand and supply equations respectively, while σ L denotes the variance of the lobbying equation. The matrices \( \boldsymbol{\Sigma}_{PL}\) and\(\ \boldsymbol{\Sigma}_{QL}\) are the covariances between the market equations and the lobbying equation.

We assume that all markets and all states are independent and that all residuals of a specific type of equation are drawn from the same normal distribution with mean zero and variance σ P , σ Q , and σ L . Thereby \(\boldsymbol{\Sigma}_{P}=\mathbf{1}_{M_{s}}\cdot\sigma_{P}\) , \(\boldsymbol{\Sigma}_{Q}=\mathbf{1}_{M_{s}}\cdot\sigma_{Q}\), and \(\boldsymbol{ \Sigma }_{PQ}=\mathbf{1}_{M_{s}}\cdot\sigma_{PQ}\), where \(\mathbf{1}_{M_{s}}\) is a M s -dimensional identity matrix and σ PQ denotes the covariance between the inverse demand equation and the supply equation in the same market. Furthermore, let the covariance between the market equations and the state equation be such that (I) the general ”affinity” of the state equation to a specific type of market activity (i.e., demand or supply) within this state is independent of the number of these markets and (II) the covariances between the state equation and all market equations of the same type in this state are equal. Assumption (I) is reflected by \( cov(\varepsilon _{Ls},\varepsilon_{Ps1}+\cdots+\varepsilon_{PsM_{s}})=\sigma_{PL}\) and \( cov(\varepsilon_{Ls},\varepsilon_{Qs1}+\cdots+\varepsilon_{QsM_{s}})= \sigma_{QL}\) while assumption (II) leads to \(cov(\varepsilon_{Ls}, \varepsilon_{Ps1})=\cdots=cov(\varepsilon_{Ls},\varepsilon_{PsM_{s}})\) and \( cov(\varepsilon_{Ls},\varepsilon_{Qs1})=\cdots=cov(\varepsilon_{Ls}, \varepsilon_{QsM_{s}})\). This implies that cov(ε Ls , ε Psm ) = 1/M s σ PL and cov(ε Ls , ε Qsm ) = 1/M s σ QL for all markets m = 1,...,M s . Hence, \(\boldsymbol{\Sigma}_{PL}=u_{M_{s}}\cdot\sigma_{PL}/M_{s}\) and \(\boldsymbol{ \Sigma}_{QL}=u_{M_{s}}\cdot\sigma_{QL}/M_{s}\), where \(u_{M_{s}}\) is a M s -dimensional column vector of ones. With this structure, the correlation between the lobbying equation and the sum of the residuals of the market equations of either type decreases in M s .Footnote 37

Appendix B: Estimation equations with firm-specific lobbying effects

Allowing for firm specific cost reductions through lobbying requires the estimation of a firm specific parameter α 2i in the marginal cost function (Eq. 9). This changes the empirical quantity relation (Eq. 11) to

$$ 0=-\alpha_{0}-\alpha_{1}\frac{Q_{ms}}{I_{ms}}-\frac{L_{s}}{I_{ms}} \sum\limits_{i=1}^{F}\alpha_{2i}F_{ims}-\alpha_{3}X_{ms}^{C}+P_{ms}+\theta ^{Q}\beta_{1}\left( 1-\phi R_{s}\frac{I_{ms}}{Q_{ms}}\right) , $$
(15)

where F ims is an indicator equal to one if firm i operates in market m in state s and F denotes the total number of firms in our sample.Footnote 38

Adjusting the lobbying equation (Eq. 13) to accommodate firm specific α 2i -parameters, requires firm-level quantity data, which are not available in our context. Therefore we estimate the adjusted version of Eq. 13 at the market average of production, Q ms /I ms :

$$\begin{array}{rll} 0 &=&\theta^{L}\sum\limits_{m=1}^{M_{s}}\sum\limits_{i=1}^{F}\\ &&\times\,\left[ \frac{ \alpha_{2i}F_{ims}\left( \beta_{1}\left( 1-\phi R_{s}\frac{I_{ms}}{Q_{ms}} \right) +P_{ms}-\alpha_{0}-\alpha_{1}\frac{Q_{ms}}{I_{ms}} -\alpha_{3}X_{ms}^{C}\right) -\left( \alpha_{2i}F_{ims}\right) ^{2}L_{s}}{ \theta^{Q}\beta_{1}\frac{I_{ms}}{Q_{ms}}\left( 2+I_{ms}\theta^{Q}\left( \frac{\phi R_{s}}{Q_{ms}}-\frac{1}{I_{ms}}\right) \right) -\alpha_{1}}\right. \\ &&\quad\;\;\left. -\;\alpha_{2i}F_{ims}\frac{Q_{ms}}{I_{ms}}\vphantom{\frac{ \alpha_{2i}F_{ims}\left( \beta_{1}\left( 1-\phi R_{s}\frac{I_{ms}}{Q_{ms}} \right) +P_{ms}-\alpha_{0}-\alpha_{1}\frac{Q_{ms}}{I_{ms}} -\alpha_{3}X_{ms}^{C}\right) -\left( \alpha_{2i}F_{ims}\right) ^{2}L_{s}}{ \theta^{Q}\beta_{1}\frac{I_{ms}}{Q_{ms}}\left( 2+I_{ms}\theta^{Q}\left( \frac{\phi R_{s}}{Q_{ms}}-\frac{1}{I_{ms}}\right) \right) -\alpha_{1}}}\right] +\theta^{L}\phi N_{s}R_{s}(\delta_{0}+\delta_{1}X_{s}^{P})-1. \end{array}$$
(16)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Duso, T., Jung, A. Product Market Competition and Lobbying Coordination in the U.S. Mobile Telecommunications Industry. J Ind Compet Trade 12, 177–201 (2012). https://doi.org/10.1007/s10842-010-0090-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10842-010-0090-1

Keywords

JEL Classification

Navigation