metadata: authors: 0: author: Chase,Robert S.
1: author: Labonne,Julien Bernard
count: World,Philippines
docna: 0: docna: So you want to quit smoking : have you tried
            a mobile phone ?
docty: Policy Research Working Paper
subtopic: Health Care Services Industry,Telecommunications Infrastructure,Disease Control & Prevention,Public Health Promotion,Educational Sciences,Social Development & Poverty,Alcohol and Substance Abuse
teratopic: Health, Nutrition and Population,Macroeconomics and Economic Growth,Private Sector Development
abstracts: cdata!: Tobacco use, which is rising quickly in
            developing countries, kills 5.4 million people a year
            worldwide. This paper explores the impacts of mobile phone
            ownership on tobacco consumption. Indeed, mobile phone
            ownership could affect tobacco consumption because
            individuals might pay for their communication with money
            they would have spent on tobacco. Using panel data from
            2,100 households in 135 communities of the Philippines
            collected in 2003 and 2006, the analysis finds that mobile
            phone ownership leads to a 20 percent decline in monthly
            tobacco consumption. Among households in which at least one
            member smoked in 2003, purchasing a mobile phone leads to a
            32.6 percent decrease in tobacco consumption per adult over
            the age of 15. This is equivalent to one less pack of 20
            cigarettes per month per adult. The results are robust to
            various estimation strategies. Further, they suggest that
            this impact materializes through a budget shift from tobacco
            to communication.
geo_reg_key: 517198,517191
repnme: repnme: So you want to quit smoking : have you
            tried a mobile phone ?
display_title: So you want to quit smoking : have
            you tried a mobile phone
listing_relative_url: /research/2008/06/9608509/so-want-quit-smoking-tried-mobile-phone
topicv3: Alcohol and Substance Abuse m1326793 1438,Education m1327048 470,Social Development m1327660 134,Health m1504661 3480,Mobile Phone m1505675 3092
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Health, Nutrition and Population,Macroeconomics and Economic Growth,Private Sector Development
no_of_pages: 21
author_keys: 0: author_key: 321125
1: author_key: 763908
model_extractions: WPS4657 Policy ReseaRch WoRking PaPeR 4657 So You Want to Quit Smoking: Have You Tried a Mobile Phone?

_Julien Labonne Robert S. Chase_ The World Bank Sustainable Development Network Social Development Department June 2008
Policy ReseaRch WoRking PaPeR 4657

# **Abstract**

Tobacco use, which is rising quickly in developing countries, kills 5.4 million people a year worldwide. This paper explores the impacts of mobile phone ownership on tobacco consumption. Indeed, mobile phone ownership could affect tobacco consumption because individuals might pay for their communication with money they would have spent on tobacco. Using panel data from 2,100 households in 135 communities of the Philippines collected in 2003 and 2006, the analysis finds that mobile phone ownership leads to a 20 percent decline in monthly tobacco consumption. Among households in which at least one member smoked in 2003, purchasing a mobile phone leads to a 32.6 percent decrease in tobacco consumption per adult over the age of 15. This is equivalent to one less pack of 20 cigarettes per month per adult. The results are robust to various estimation strategies. Further, they suggest that this impact materializes through a budget shift from tobacco to communication.

This paper—a product of the Social Development Department, Sustainable Development Network—is part of a larger effort in the department to analyze how increased information flow affect household welfare. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at jlabonne@worldbank. org and rchase@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **So You Want to Quit Smoking: Have You Tried a Mobile Phone?**<sup>**1**</sup>

Julien Labonne and Robert S. Chase<sup>2</sup>

> 1 We are grateful to Charles Kenny and Mark Williams for fruitful discussions that led to this paper.  We would like to thank Damien de Walque, Gillette Hall, Ben Olken and Melody Tulier for helpful comments. We also wish to thank Bhuvan Bhatnagar, Andrew Parker, Arsenio Balisacan, Rosemarie Edillon, Sharon Piza and all the staff of APPC without which the field work would have been impossible. We are grateful to the Philippines Department of Social Welfare and Development for allowing us to use the data. All remaining errors are ours. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors and should not be attributed in any manner to the World Bank, to its affiliated organizations or to members of its Board of Executive Directors or the countries they represent

> 2 <u>jlabonne@worldbank.org</u> and <u>rchase@worldbank.org</u>
# **_1. Introduction_**

Tobacco use kills 5.4 million people a year.<sup>3</sup> This is likely to increase over the next decades as tobacco consumption grows in developing countries. In addition to health impacts, smoking diverts poor households’ resources away from other more productive use. For example, Banerjee and Duflo (2007) report that in Mexico, the extremely poor in rural areas spend about 8.1 percent of their budget on tobacco and alcohol.

The literature on the determinants of smoking behavior points to a clear link between price increases and a reduction in the number of cigarettes smoked. Increasing taxes on tobacco has therefore often been advocated as a means to reduce smoking. However, recent evidence casts some doubts on the health impacts of tobacco taxes. Indeed, Adda and Cornaglia (2006) show that price increases lead smokers to smoke more intensively ( _i.e._ , extract more nicotine per cigarette) which is detrimental to health.

Recently, some in the public health community have suggested that, in developed countries, mobile phone usage could play a role in reducing smoking, especially among cash constrained teenagers (Charlton and Bates, 2000). Analyses undertaken in European countries appear to contradict this claim, however (Steggles and Jarvis, 2003). No analysis has been carried out in a developing country context where mobile phones are spreading rapidly, even though the potential impact of such phones on tobacco consumption is the greatest because of stricter household budget constraints.

The paper uses household panel data to examine the impact of mobile phone ownership on tobacco consumption. Our results point to a large and robust negative impact of mobile phone ownership on tobacco consumption. Among households in which at least one member smoked in 2003, purchasing a mobile phone leads to a 32.6 percent decrease in tobacco consumption per adult over the age of 15. This is equivalent to one less pack of 20 cigarettes per month. In addition to simple OLS difference-in-differences estimates

> 3 <u>http://www.who.int/tobacco/mpower/tobacco_facts/en/index.html</u> Accessed 5/17/2008

2
we also report matched difference-in-differences estimates. Both methods yield similar results. Further, our results suggest that this impact materializes through a budget shift from tobacco to communication.

The paper is organized as follows. The data and basic descriptive statistics are described in Section 2. The estimation strategy is presented in Section 3. Results are discussed in Section 4. The final section concludes.

# **_2. The Data_**

Our analysis relies on household panel data collected in 135 villages of the Philippines. The first round of data collection took place in the fall of 2003 and the sample included 2,400 households of which 2,092 were re-interviewed in the fall of 2006. The dataset contains detailed information on consumption patterns, mobile phone ownership, household structure, education achievements as well as asset and land ownership (Chase and Holmemo, 2005). Because consumption information was collected item by item, we can extract tobacco and communication consumption ( _i.e.,_ all phone-related expenses) from total consumption.

Over the survey period, mobile phone ownership spread quickly in the sampled communities. Indeed, while the proportion of households owning a mobile phone in 2003 was 8.4 percent, it rose to 35.4 percent in 2006.

Smoking is prevalent in our sample with 41.6 percent of households reporting some tobacco consumption in 2003. The average monthly tobacco consumption was about 31.2 Philippine Peso (PHP) per adult over the age of 15. This is equivalent to 1.24 packs of 20 sticks.<sup>4</sup> This rose to 75.9 PHP for households in which at least one member smoked in 2003. Overall, tobacco consumption represented 2.01 percent of their total budget.

> 4 According to WHO (2008), the price of a pack of 20 sticks for the most popular brand in the Philippines is 25 PHP.

3
Results from t-tests and Kolmogorov-Smirnov tests (Table 1) indicate that there is no difference in the 2003 distribution of tobacco consumption between the households who purchased a mobile phone between 2003 and 2006 and those who did not.

Over the period 2003-2006, the average monthly tobacco consumption decreased to 30.4 PHP per adult over the age of 15. A different picture emerges once we separate households who owned and who did not own a mobile phone in 2006. Indeed, for households without a mobile phone, monthly tobacco consumption rose to 33.9 PHP while it decreased to 23.1 PHP for households who such a phone (cf. Figure 1).

# **_3. Estimation Strategy_**

## _3.1. Basic Setup_

5 Let _Ln_ ( _Cijt_ ) be household _i_ ’s (log) per adult (over 15) tobacco consumption. It is determined by: where α and β are coefficients to be estimated, _M ijt_ is a dummy equal to one if household _i_ in village _j_ owns a mobile phone at time _t_ , _X ijt_ is a vector of control variables that vary across households and time, _uij_ is a time-constant household effect, _v jt_ is an effect common across all households in village _j_ at time _t_ and, _wijt_ is the usual idiosyncratic error term.

' We can eliminate _uij_ by differencing equation (1). Rewriting _v j_ = ( _v j_ 1 − _v j_ 0 ) and ' _wij_ = ( _wij_ 1 − _wij_ 0 ) leads to:

> 5 The main results of the paper are basically unchanged if we run our regressions with a different age cutoff. Results available upon request.

4
We will estimate equation (2) through OLS, include village dummies and compute standard-errors robust to arbitrary variance structure within villages. Further, as smokers are predominantly men in the Philippines (WHO, 2008), we will also run (2) with a measure of tobacco consumption per male household member over the age of 15 (in effect assuming that only males in the household smoke).

The vector _X ijt_ of control variables includes an index of household wealth, the number of household members working in the farm sector, the number of household members working in the non-farm sector, the total number of household members, number of household members above sixty, number of household members under five, household head age, maximum years of education in the household, household head’s spouse age as well as a dummy indicating if the household owns land for purposes other than residence. In addition, we also include in equation (2) a dummy equal to one if a household member migrated over the period, a dummy equal to one if a household member died over the period and a dummy equal to one if a household member suffered a serious illness over the period.

A small proportion of households (8.4 percent) owned a mobile phone in 2003. Comparing those households with households who did not own a mobile phone in 2003 along the baseline covariates indicate that the differences between the two groups were large and significant. Thus, while we will present results obtained with the full sample, we will focus our analysis on households who did not own a mobile phone in 2003.

# _3.2. Dealing with Selection on Observables_

There are some differences along baseline covariates between households who purchased a mobile phone between 2003 and 2006 and those who did not. This might lead to biased estimates. It is possible to remove such bias by estimating equation (2) through Weighted Least Squares (WLS) using specific weights (Hirano et al. 2003). Those weights are 5
estimate of the propensity score: _P_ ( _M ij_ 1 = 1 | _X ij_ 0 ) .<sup>6</sup> This leads to efficient and consistent estimates of α as long as either the propensity score or the regression model are specified correctly (Imbens and Wooldridge, 2007).

The validity of our WLS estimates relies on the Conditional Independence Assumption: conditional on the covariates, owning a mobile is exogenous ( _i.e.,_ not driven by unobservables that also affect the decision to smoke). While we cannot directly test this assumption we can assess if it is plausible by regressing _Ln_ ( _Cij_ 0 ) on _M ij_ 1 . The closer the estimated coefficient is to zero, the greater the plausibility that the assumption holds (Imbens and Wooldridge, 2007). Results are available in Table A1. None of the coefficients are statistically different from zero at the usual levels and none of the t- statistics are greater than 0.67. Our results are thus unlikely to be driven by selection on unobservables.7

# **_4. Results_**

In this section, we first empirically assess the impact of mobile phone ownership on smoking behavior by implementing the estimation strategy discussed in Section 3.

> 6 Estimates are available in Table A2.

> 7 While the results indicate that selection on unobservables is not very likely, as a further test of robustness, we also estimate equation (2) through IV. The validity of such estimates relies on the availability of an instrument that explains the decision to buy a mobile phone but is not correlated with the decision to smoke (other than through the impact of mobile phone).  We use the 2003 asset index as an instrument for mobile phone purchase. Results available in Table A2 indicate that this is a good predictor of the decision to buy a phone between 2003 and 2006 (t-stat is equal to 10.8). Further, a regression of the 2003 level of tobacco consumption on the 2003 asset index does not yield any significant results (t-stat is equal to -0.51). (Results available upon request). While not a direct test of the validity of our instruments, this suggests that our asset index is not directly correlated with the decision to smoke. We use the Limited Information Maximum Likelihood (LIML) estimator as it performs better than 2SLS in finite samples.  We test whether our instrument set is weak against the alternative hypothesis that it is strong using the test put forward in Stock and Yogo (2005). In all regressions we can reject the null hypothesis that our instruments are weak. All our IV results are consistent with both our OLS and WLS results.

6
Results are available in Table 2. We then discuss potential channels through which this effect could materialize.

# _4.1. Basic Results_

Mobile phone ownership leads to a sharp decrease in tobacco consumption. Specifically, our estimates indicate that mobile phone ownership leads to a 9.5 percent decline in tobacco consumption. This effect rises to 18.2 percent once we exclude households that owned a mobile phone in 2003.

Our results might capture a reduction in the decision to start smoking rather than a decrease of tobacco consumption among those who smoked. We also estimate equation (2) but exclude all households that did not report any tobacco consumption in 2003. Results are available in Columns 3 and 6-7 of Table 2. Our estimates are larger than with our broader sample. In this subsample, mobile phone ownership is associated with a 32.6 percent drop in tobacco consumption. This is equivalent to about one pack of 20 cigarettes a month per adult over the age of 15 in the household. Mobile phone ownership does lead to a drop in tobacco consumption among those who smoked in 2003.

Our measure of tobacco consumption is not individual-specific, and thus household-level changes could be driven by changes in household composition. For example, if the household head was the only smoker in the household and died during the period, tobacco consumption should go down regardless of mobile phone ownership status. If households with and without mobile phones were affected differently by migration and death that could bias our estimates. As a result, we run equation (2) but restrict our sample to those households in which the set of household members over the age of 15 did not change over the three-year period. Available in Panel C and D of Table 2, results are consistent with the ones obtained previously: mobile phone ownership leads to a drop in tobacco consumption.

7
A potential concern with our results is that the observed drop could be explained by a drop in price rather than a decrease in the number of cigarettes smoked. Indeed, tobacco sellers could decrease their prices in response to the introduction of mobile phones in their community. However, changes in tobacco prices are unlikely to explain our results as we control for time-varying village effects and prices are likely to be the same for all households in a given village.

Further, households might shift to less expensive brands. To deal with those concerns, we assess the links between mobile phone ownership and the decision to quit smoking. Indeed, if the impact of mobile phones was merely capturing a shift to less expensive brands, we should not observe any impact on the decision to quit smoking. We run Probit regressions of the decision to quit smoking on the mobile phone dummy and a set of covariates (measured as 2003-2006 changes). We include village dummies and compute standard errors robust to arbitrary covariance structure within villages. Households for which tobacco consumption was greater than zero in 2003 but was equal to zero in 2006 are classified as having quitted smoking. Results are available in Table 3. Households who purchased a mobile phone between 2003 and 2006 are 20.4 percentage points more likely to have stopped smoking. This result holds if we restrict our sample to households in which the set of household members over the age of 15 did not change over the threeyear period (Panel B in Table 4). This indicates that shifts to less expensive brands cannot fully explain our results.

# _4.2 Alternative Explanations and Potential Channels_

Our results could also capture the links between wealth, mobile phone ownership and tobacco use. Indeed, if as they get richer, households buy a mobile phone and reduce their tobacco consumption, we would be merely capturing a wealth effect rather than the actual impact of mobile phone ownership. We assess if tobacco consumption is explained by (non tobacco) consumption. We run equation (2) but include the change in total (non tobacco) per capita consumption as a dependent variable. Results are available in Panel A of Table 4. Our results are consistent with the ones obtained above: even after controlling 8
for household-fixed effects, tobacco consumption goes up with overall (non-tobacco) consumption. It is important to note that the mobile phone dummy is still significant. Overall, the results discussed above indicate that ‘wealth effects’ are not driving our results.

We now assess if the impact of mobile phone is specific to tobacco consumption. Indeed, it is possible that mobile phone-related expenses negatively affect not only tobacco consumption but also the consumption of all other goods consumed by households. We test if mobile phone ownership has any impact on alcohol consumption (per adult over 15) and per capita food consumption. Results are available in Panel B and C of Table 4. We find that mobile phone ownership does not negatively impact on alcohol and food consumption. There is something special about the relationship between mobile phones and tobacco consumption.

Having shown that mobile phone ownership leads to a sharp decline in tobacco consumption, we now turn our attention to the channels through which this effect might materialize.

Households might need to reduce tobacco consumption to pay for their communication (i.e., there is a cross-price elasticity between tobacco and communication). We assess if changes in tobacco consumption can be explained by change in ‘communication consumption’ ( _i.e.,_ all phone-related expenses). We start by plotting ‘communication consumption’ in 2003 and 2006 for households by mobile phone ownership status. Communication consumption increased by 34.2 PHP (per adult over 15) in households who purchased a mobile phone between 2003 and 2006 while it only increased by 3.27 PHP for households who did not (cf. Figure 2).

We also run equation (2) but substitute the mobile phone dummy by the change in the ‘communication consumption.’ Results are available in Panel D of Table 4. There is a negative and significant relationship between communication and tobacco consumption. This indicates that the impact of mobile phone on tobacco consumption is driven by a 9
shift of resources from tobacco to communication. This shift might be explained by the role of tobacco consumption and mobile phone use as a signal for social status. It is possible that while tobacco consumption used to be a signal for social status, it it now slowly being replaced by mobile phone ownership and use.

# **_5.  Conclusion_**

In this paper, we explore the impact of mobile phone ownership on tobacco consumption. Using household panel data, we find that purchasing a mobile phone leads to a sharp decline in tobacco consumption. This effect is large and materializes through a budget shift from tobacco to communication.

An interesting avenue for further analysis is to understand the role of social status in explaining the observed shift between tobacco and communication. Indeed, it might be that smoking used to be a signal for social status and this signal is slowly being replaced by mobile phone ownership and use.

Finally, it appears important to assess if those results hold in other countries. First, in countries at similar levels of incomes, other goods might get crowded out by mobile phones. Second, in richer countries, the budget constraints might not be as strong and thus mobile phone ownership might not lead to such a crowding-out.

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