II. A Brief Literature Review

The nexus between socio-economic conditions and alcohol consumption patterns are far from trivial. During phases of economic growth and increasing wages, alcohol consumption should increase proportionately if alcohol is a normal or luxury good (De Goeij et al., Reference De Goeij, Suhrke, Toffolutti, Van de Mheen, Schoenmakers and Kunst2015). Alcohol consumption could also increase during recessions, and especially depression phases of the business cycle with increased levels of unemployment due to more leisure time. Additionally, consumption of alcoholic beverages has lower opportunity costs for those who lost their jobs or have fewer working hours due to low business activity (Krüger and Svensson, Reference Krüger and Svensson2010). Increasing consumption of alcoholic beverages could be driven by demographic aspects (Hart and Alston, Reference Hart and Alston2019) and could be triggered during recessions and periods of unemployment due to increased stress, depression, and family discord (Baker, Reference Baker1985; Catalano et al., Reference Catalano, Goldman-Mellor, Saxton, Zilko-Margerison, Subbaraman, Lewinn and Anderson2011; Collins, Reference Collins2016; Fenwick and Tausig, Reference Fenwick and Tausig1994; Jones, Reference Jones1989; Karsek and Theorell, Reference Karsek and Theorell1990; McGee and Thompson, Reference McGee and Thompson2015; Ritter and Chalmers, Reference Ritter and Chalmers2011; Sokejima and Kagamimori, Reference Sokejima and Kagamimori1998). However, empirical evidence on the relationship between the decline in emotional well-being because of economic problems and increased alcohol intake is scant, and its scrutiny yields rather a mixed picture (Freeman, Reference Freeman1999).

Wang, Shan, and Cochran (Reference Wang, Shan and Cochran2016) analyze the data of 4,585 individuals over 45 in China, with the data set representative of Zheijang and Gansu provinces in 2008 and 2012, provided by China Health and Retirement Longitudinal Study. The authors find that the unemployment rate is positively associated with smoking behavior. This could be an indirect indication of the relevance of economic well-being for the consumption of drugs. Based on weekly data sets from San Francisco, Catalano, Novaco, and McConnell (Reference Catalano, Novaco and McConnell2002) find that to avoid the potential job loss or decreased earnings, people tend to reduce their alcohol intake during a recession.

In addition to unemployment, income inequality and wealth status are also shown to be powerful macroeconomic indicators of alcohol consumption, as shown by Kossova, Kossova, and Sheluntcova (Reference Kossova, Kossova and Sheluntcova2017) for Russian regions, and Niklas and Sadik-Zada (Reference Niklas and Sadik-Zada2019) for a panel of 12 major wine-producing and consuming countries. Based on data from France, Chaix and Chauvin (Reference Chaix and Chauvin2003) find a positive relationship between the level of household income and alcohol consumption. Fogarty and Voon (Reference Fogarty and Voon2018) and Voon and Fogarty (Reference Voon and Fogarty2019) indicate that there is no systematic correlation between alcohol taxes or policies and alcohol consumption, demonstrating different methods of forecasting alcohol consumption, but without modeling macroeconomic indicators.

The empirical literature on “economy and alcohol consumption” is dominated by the country-specific symmetric time series estimators and individual-level data analyses, which, overwhelmingly, establish a procyclical relationship (Ettner, Reference Ettner1997; Ruhm, Reference Ruhm1995; Freeman, Reference Freeman1999). A panel study by Helble and Sato (Reference Helble and Sato2011), which encompasses 159 countries and a time interval from 1960 to 2004, is based on the fixed and random effects panel estimators and also validates the procyclicality of the nexus.

III. Methodology and Data Sources

As previously mentioned, within the framework of this inquiry, at different stages of a business cycle, consumption patterns of gross alcohol in general and specific alcoholic beverages in particular, could be asymmetric. To approach the research question empirically, the study employs a PNARDL, developed by Shin, Yu, and Greenwood-Nimmo (Reference Shin, Yu, Greenwood-Nimmo, Sickles and Horrace2014). In contrast to standard cointegration techniques, PNARDL enables modeling asymmetric relationships between the variables of interest and cointegration in a single equation. Because PNARDL is fully analogous to the autoregressive distributed lag model (ARDL), it has the advantage of flexibility in terms of the order of integration: the time series can be I(0), I(1), or a mixture of both (Pesaran and Smith, Reference Pesaran and Smith1995; Pesaran, Smith, and Shin, Reference Pesaran, Smith and Shin1996). Furthermore, the proposed methodology is appropriate for handling the longitudinal data sets with heterogeneous panels (Blackburne and Frank, Reference Blackburne and Frank2007; Salisu and Isah, Reference Salisu and Isah2017). For the traditional large N and small T the fixed- and random-effects estimators, their combination, and the generalized method-of-moments estimators are appropriate (Blackburne and Frank, Reference Blackburne and Frank2007). The assumption of the homogenous slope parameters is, however, mostly not appropriate for large T panels such as ours (Salisu and Isah, Reference Salisu and Isah2017). Mean group (MG), pooled mean group (PMG), and dynamic fixed effects (DFE) are the most proliferated techniques for modeling dynamic heterogeneous panels. MG estimates N time-series regressions and identifies N long- and short-run slopes. PMG estimates the homogenous long-term cointegrating and heterogeneous short-term estimators (Pesaran, Smith, and Shin, Reference Pesaran, Smith and Shin1996). DFE estimates the homogenous, that is, for all the panels equal cointegrating vector. The adjustment term and short-term coefficient are also assumed to be equal for all panels. Only the panel-specific intercepts differ across individual panels (Blackburne and Frank, Reference Blackburne and Frank2007). The choice between MG, PMG, and DFE estimators is predicated on the Hausman test statistics.

A. The Symmetric Panel ARDL

The symmetric version of the panel ARDL can be expressed as

(1)$$\eqalign{ {\rm \Delta }Alcohol_{it} &= \beta _{0i} + \beta _{1i}Alcohol_{i, \;t-1} + \beta _{2i}Lag\;1\_Unemp_{t-1} + \beta _{2i}PCI_{t-j} \cr & \quad + \mathop \sum \limits_{\,j = 0}^{N1} \lambda _{ij}{\rm \Delta }Alcohol_{i, \;t-j} + \mathop \sum \limits_{\,j = 0}^{N2} \gamma _{ij}\Delta Lag\;1\_Unemp_{t-j}\cr &\quad + \mathop \sum \limits_{\,j = 0}^{N2} \psi _{ij}\Delta PCI_{t-j} + \mu _i + \varepsilon _{it}\mu _i + \varepsilon _{it},}$$

where $i = \overline {1, \;N}$, $t = \overline {1, \;T}$; Alcohol _it is the average alcohol consumption for each country i over a period of time t; Unemp _t denotes the level of unemployment at period t; μ _i is the group-specific effect; i indicates the countries in the sample; and t is the number of time periods. For each cross-section, the long-run slope coefficient is calculated as $-{{\beta _{2i}} \over {\beta _{1i}}}$. The short-run estimate for the unemployment rate and PCI are γ _ij and PCI ψ _ij, respectively (Salisu and Isah, Reference Salisu and Isah2017).

B. The Asymmetric Panel ARDL

The asymmetric version of Equation (1) is expressed as

(2)$$\eqalign{{\rm \Delta }Alcohol_{it} & = \beta _{0i} + \beta _{1i}Alcohol_{i, \;t-1} + \beta _{2i}^ + Lag1\_Unemp_{t-1}^ + { + } \beta_{2i}^- Lag1\_Unemp_{t-1}^- \cr &\quad +\beta_{3i}^ + PCI_{t-1}^{+} + \beta _3^- PCI_{t-1}^{-} + \sum \limits_{\,j = 1}^{N1} \lambda _{ij}{\rm \Delta }Alcohol_{i, t-j} \cr &\quad + \mathop \sum \limits_{\,j = 0}^{N2} ( \gamma_{ij}^{+}{\Delta Lag}1\_Unemp_{t-j}^{+} +\gamma_{i,j}^- {\Delta Lag}1\_Unemp_{t-j}^-)\cr &\quad + \sum_{j=0}^{N3}(\psi_{ij}^{+}{\rm \Delta }PCI_{t-j}^{+} + \psi_{ij}^- {\Delta }PCI_{t-j}^{-}) + \mu _i + \varepsilon _{it},} $$

where $Unemp_t^ +$ denotes the labor market shock that leads to increasing unemployment, and $Unemp_t^-$ implies a labor market shock that triggers a decrease of unemployment. Analogously, $PCI_t^ +$ implies a positive and $PCI_t^-$ a negative income shock. These shocks are computed as positive and negative partial sum decompositions of average income and unemployment indicator changes, respectively (Shin, Yu, and Greenwood-Nimmo, Reference Shin, Yu, Greenwood-Nimmo, Sickles and Horrace2014).

The error correction version of Equation (2) is

(3)$$\eqalign{ {\rm \Delta }Alcohol_{it} &=\tau _i\xi _{i, \;t-1} + \mathop \sum \limits_{\,j = 1}^{N1} \lambda _{ij}{\rm \Delta }Alcohol_{i, t-j} \cr &\quad + \mathop \sum \limits_{\,j = 0}^{N2} ( \gamma _{ij}^ + {\rm \Delta }Lag1\_Unemp_{t-j}^ + + \gamma_{ij}^- {\Delta Lag}1\_Unemp_{t-j}^-) \cr &\quad +\sum_{j=0}^{N3}(\psi_{ij}^{+} {\rm \Delta }PCI_{t-j}^{+} + \psi_{ij}^- {\rm \Delta }PCI_{t-j}^{-}) + {\rm \;}\mu _i + \varepsilon _{it} ,} $$

where ξ _{i, t−1} is the error-correction term that captures the long-term equilibrium in the asymmetric PNARDL, and τ _i is the speed of adjustment that indicates the time that the system requires to converge to the long-run equilibrium in the face of a shock (Salisu and Isah, Reference Salisu and Isah2017).

To account for multicollinearity between average per capita income and unemployment, the study instruments the lagged values of the unemployment rate as an indicator of average income. The data on average income have not been lagged. In order to interpret the regression coefficients as percentages, all nonnegative time series have been transformed to their natural logarithms.

The required data series to run a PNARDL has been compiled into a single data set from two sources. These sources are Holmes and Anderson (Reference Holmes and Anderson2017) and World Development Indicators (WDI) of the World Bank (2020). We draw on annual data on average consumption of beer, wine, and gross alcohol in total from Holmes and Anderson (Reference Holmes and Anderson2017).Footnote ¹ Per capita GDP data (in constant 2010 US$) and unemployment rate come from the World Bank (2020). Our panel is comprised of 24 countriesFootnote ² and spans the period 1961 to 2014.

IV. Estimation Results

Due to the possible differences in the consumption behavior of different alcoholic beverage types, three analogous pooled mean group-based PNARDL models with three different independent variables have been conducted: the natural logarithm of average alcohol consumption in liters in Model 1; the natural logarithm of average wine consumption in Model 2; and the natural logarithm of average beer consumption in Model 3. The estimation results are presented in Table 1. Adjustment terms in all three cointegration models are statistically significant at 1 percent level, negative, and range between –0.280 and –0.522. Hence, the cointegration models are valid.

Table 1

Panel Nonlinear ARDL Estimations, 1961–2014

Notes: Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1; a plus sign ⁺ indicates an increasing portion and a minus sign ^– indicates a decreasing portion of the respective variable. To account for the short-term effects of the individual time periods, time period has been included to the error-correction equation.

V. Concluding Remarks

The present inquiry addresses the differences in the responsiveness of alcohol consumption during the expansion and contraction phases of economic growth. Findings show that during the expansion phase of a business cycle, increasing levels of average income lead to a substantial increase in the average consumption of gross alcohol, wine, and beer. However, expansions with pronounced job creation effects could lead to a net reduction of average level alcohol and wine consumption.

During the recession phase, there is no symmetric reduction in average gross alcohol or beer consumption. Wine consumption reduction is statistically significant, but the elasticity is rather weak. Consumption of alcoholic drinks during recessions mostly persists at the pre-recession phase's levels. During recessions, with insufficient public labor market interventions to prevent increasing levels of unemployment, the surge in unemployment could trigger an additional increase in the average alcohol intake.

Based on this quantitative inquiry, the asymmetric responsiveness of alcohol intake to fluctuations of central macroeconomic variables, such as per capita income and level of unemployment, could be established. In addition, it is found that the consumption behavior of wine and beer diverges from the aggregate indicator of average alcohol consumption. Findings further show that, during the boom phase, alcoholic beverage consumption is in line with the hypothetic consumption of normal and during the recession phase with the hypothetic consumption of luxury or culture goods.

This study is based on highly aggregated macroeconomic data and provides only a tendentious bird's-eye perspective on the nexus between macroeconomic variables and the average intake of alcoholic drinks. This macro-perspective is, nonetheless, sufficient in showing the risks of persistent or increasing levels of alcohol consumption during contraction. For the formulation of concrete health policies, case studies with less aggregated data sets in the respective contexts are necessary.