# Cigarette Smoking in Indonesia: Examination of a Myopic Model of Addictive Behaviour

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Methodology

_{i}is an individual fixed effect controlling for time-invariant preferences and marginal utility of wealth), d

_{t}is a time dummy controlling for unanticipated macro changes in wealth, and ε

_{it}is the error term. A significant and positive effect of previous consumption (measured by coefficient β

_{1}) on current cigarette consumption (C

_{it}) indicates myopic addictive behavior [14].

_{2}) on lead consumption (Equation 2) together with a reasonable estimate of the discount rate gives a direct test of a rational addiction model against an alternative model in which consumers are myopic [14–16].

_{it}may be serially correlated with and through lagged and lead consumption. That is, there could be an omitted variable bias from unobserved time-invariant preferences, marginal utility of wealth (v

_{i}) and other demand shifters (e

_{it}) that may be serially correlated. These unmeasured variables may be correlated with C

_{it}

_{−1}in Equation (1) and both C

_{it}

_{−1}and C

_{it+}

_{1}in Equation (2). Second, there is measurement error in recorded values of C

_{it}

_{−}

_{1}in Equation (1) and both C

_{it}

_{−1}and C

_{it}

_{+1}in Equation (2). Equations (1) and (2) were derived assuming perfect certainty on prices and other variables; when unexpected changes in these variables cause individuals to revise their consumption plans, C

_{it}

_{−1}and C

_{it}

_{+1}then measured with error. Measurement error in either dependent or independent variables leads to biased OLS coefficient estimates.

_{it}

_{−1}:

_{i}are the potential instruments and all else is as defined in Equation (1). The instruments will be valid if they are good predictors of lagged consumption and uncorrelated with the error in the demand Equation (1). In addition to checking that the instruments are significant in the first stage regression (3), we performed several tests of the instruments, including relevancy, validity and orthogonality. Recent applications of these tests and the selection process to choose the best estimator are described elsewhere [18].

## 3. Data and Variables

## 4. Results

#### 4.1. Model Selections

^{2}with 1 degree of freedom. The value of the test was about 7.9 with a p-value of 0.005. We thus rejected the null hypothesis of exogeneity (Table 2), suggesting OLS results in inconsistent parameter estimates [17].

_{it}in Equation (1), suggesting the RE2SLS is preferable than the FE2SLS.

_{it}

_{−1}equations are given in Table 3.

^{2}shows that the models explained a high proportion (12 percent) of the variation for lagged consumption. Table 3 also reports the Partial R

^{2}and Shea Partial R

^{2}. A gap between the Partial R

^{2}and Shea partial R

^{2}in our study considerably a small, suggesting the model is well-identified [23]. The relevance of the instruments was also investigated using an F-test to determine whether the instruments were correlated with the potentially endogenous variable [24,25]. The null hypothesis of the F-test that the parameters of the covariates are jointly equal to zero was rejected, indicating that all the instruments were jointly significant (see, the last row of Table 3). A conservative rule of thumb for a single endogenous regressor would suggest that a less than 10 F-value could be an indicator of a weak instrument [23]. In this study, the F-test for all instruments and for five instruments yielded 61 and 18, respectively.

#### 4.2. Model Estimation Results

_{it})/ ∂E(LnPc

_{it}) = β̂

_{2}/ (1–β̂

_{1}). The short-run and long-run price elasticities, evaluated at the mean, were −0.28 and −0.73, respectively. The findings that the long-run price elasticity, in absolute value, exceeds the short-run one is in line with both theoretical expectations and empirical findings. For Equation 2, the long run price elasticity is calculated using the expression ∂E(LnC

_{it})/ ∂E(LnP

_{it}) = β̂

_{3}/(1–β̂

_{1}– β̂

_{2}), and the implied discount factor and the discount rate: β

_{2}/β

_{1}, β

_{1}/β

_{2}-1, respectively.

## 5. Discussion

_{it}, and the regressors, x′

_{it}).

## Acknowledgments

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Variable | Definition | Mean | Std. Dev. |
---|---|---|---|

C_{t} | Current cigarette consumption (ln) | 2.207 | 0.746 |

C_{it}_{−1} | One lag cigarette consumption (ln) | 2.203 | 0.760 |

Pc_{t} | Current price cigarette (ln) | 4.623 | 0.701 |

Pa_{t} | Current price alcohol (ln) | 8.612 | 1.263 |

Ln-exp | Monthly per-capita income (ln) | 11.156 | 1.004 |

Working | 1 if working, 0 otherwise | 0.582 | 0.493 |

Ln-age | Individual age (ln) | ||

Child14 | 1 if children aged ≤14, 0 otherwise | ||

Instruments (z) | |||

Pc_{t}_{−1} | One lag price cigarette (ln) | 4.280 | 0.520 |

Wall | 1 if dwelling wall is brick, 0 otherwise | 0.588 | 0.492 |

Floor | 1 if dwelling floor is permanent, 0 otherwise | 0.155 | 0.362 |

Hhown | 1 if dwelling is owned/bought, 0 otherwise | 0.805 | 0.396 |

Moslem | 1 if Moslem, 0 otherwise | 0.871 | 0.335 |

Test statistics | Statistics |
---|---|

1. Endogeneity of lagged C_{it}_{−1} | |

Wu-Hausman | 7.91^{***} |

Durbin Wu Hausman (DWH): x^{2}(1) | 7.91^{***} |

2. Instrumental variable: | |

a. Heteroskedasticity: x^{2}(11) | |

Pagan-Hall general test statistic | 66.52^{***} |

Pagan-Hall test with assumed normality | 125.30^{***} |

White/Koenker nR^{2} test statistic | 75.57^{***} |

Breusch-Pagan/Godfrey/Cook-Weisberg | 148.77^{***} |

b. Overidentifying: | |

Sargan (2SLS) | 4.03 |

Basmann (2SLS) | 4.03 |

Hansen-J (GMM) | 4.01 |

c. Orthogonality: | |

C-statistics: x^{2}(1) | 0.37 |

Coef. | SE | |
---|---|---|

Pc_{t} | −0.0559^{***} | 0.020 |

Pa_{t} | 0.2253^{***} | 0.012 |

Ln-exp | −0.027** | 0.014 |

Ln-age | 0.3470^{***} | 0.031 |

If Child14 | 0.2775^{***} | 0.050 |

If working | 0.0995^{***} | 0.027 |

Excluded instruments: | ||

Pc_{t}_{−}_{1} | −0.0033 | 0.022 |

If dwelling wall is brick | −0.0976^{***} | 0.020 |

If dwelling floor is permanent | −0.1715^{***} | 0.029 |

If dwelling is owned or being bought | −0.0744^{***} | 0.024 |

If Moslem | −0.1404^{***} | 0.029 |

Constant | −0.3265 | 0.213 |

R^{2} | 0.121 | |

Shea partial R^{2} | 0.016 | |

Partial R^{2} | 0.016 | |

Test of F: | ||

All instruments, F(11, 4107) | 61.04^{†} | |

Excluded instruments, F(5, 4107) | 18.04^{†} |

^{**}5%; SE is robust standard errors.

Myopic Addiction Model [Equation (1)] | Rational Addiction Model [Equation (2)] | |
---|---|---|

Lagged consumption (C_{t−1}) | 0.625*** [0.081] | 0.509*** [0.117] |

Lead consumption (C_{t+1}) | n.a n.a | 0.112 [0.157] |

Price cigarette (Pc_{t}) | −0.275*** [0.019] | −0.135** [0.066] |

Price alcohol (Pa_{t}) | 0.143*** [0.021] | 0.159*** [0.033] |

Per–capita income (Ln) | 0.015 [0.012] | −0.01 [0.019] |

Individual age (Ln) | −0.098*** [0.038] | 0.053 [0.063] |

If child14 exist | 0.048 [0.044] | 0.097 [0.071] |

If working | 0.094*** [0.024] | 0.029 [0.039] |

Constant | 1.089*** [0.171] | 0.094 [0.437] |

Observations | 5696 | 1783 |

R–squared | 0.27 | 0.34 |

Short–run price elasticity | −0.275 | −0.135 |

Long–run price elasticity | −0.733 | −0.356 |

Discount factor | n.a | 4.54 |

Discount rate | n.a | 3.54 |

_{3}; the long-run price elasticity is calculated using the expression ∂E(LnC

_{it})/ ∂E(LnP

_{it}) = β̂

_{3}/(1–β̂

_{1}– β̂

_{2}); and the implied discount factor is β

_{2}/β

_{1}and the implied discount rate is β

_{1}/β

_{2}-1.

© 2007 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

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**MDPI and ACS Style**

Hidayat, B.; Thabrany, H.
Cigarette Smoking in Indonesia: Examination of a Myopic Model of Addictive Behaviour. *Int. J. Environ. Res. Public Health* **2010**, *7*, 2473-2485.
https://doi.org/10.3390/ijerph7062473

**AMA Style**

Hidayat B, Thabrany H.
Cigarette Smoking in Indonesia: Examination of a Myopic Model of Addictive Behaviour. *International Journal of Environmental Research and Public Health*. 2010; 7(6):2473-2485.
https://doi.org/10.3390/ijerph7062473

**Chicago/Turabian Style**

Hidayat, Budi, and Hasbullah Thabrany.
2010. "Cigarette Smoking in Indonesia: Examination of a Myopic Model of Addictive Behaviour" *International Journal of Environmental Research and Public Health* 7, no. 6: 2473-2485.
https://doi.org/10.3390/ijerph7062473