Should We Put Ice in Wine? A Difference-in-Differences Approach from Switzerland

Transcription

1 Should We Put Ice in Wine? A Difference-in-Differences Approach from Switzerland Alexandre Mondoux KOF Swiss Economic Institute, ETH Zurich 11th Annual AAWE Conference - Padua 2017 Thursday, 29 June / 30

2 Table of Contents 1 Introduction 2 Data 3 Identification strategy: Difference-in-Differences 4 Results 5 Robustness checks 6 Conclusion 2 / 30

3 Introduction Motivations Current theme Weather shocks have regained their relevance in recent weeks with a spring frost in most Swiss vineyards. Natural experiment: We exploit a natural experiment (weather hail shock affecting the grape harvest) from the Swiss wine region Three Lakes in 2013 on the consumption in the Swiss retail market. Presentation from the study: Mondoux, A. (2017): Should We Put Ice in Wine? A Difference-in-Differences Approach from Switzerland, KOF Working Paper Series (soon to be published). 3 / 30

4 Introduction Strategy Idea: Using a Difference-in-Differences (DID) approach, we are able to estimate the Average Treatment Effect (ATE) of this exogenous supply shock. Literature: Seminal and recent works of this econometric method as well as in the field of wine economics: Ashenfelter (1978), Card and Krueger (1994), Ashenfelter et al. (2007), Egger et al. (2006) and Angrist and Pischke (2008). Key findings: We find negative and statistical significant effects of the hail weather shock on Three Lakes wines in terms of consumption (-22.8%) and price (+2.8%). 4 / 30

5 Introduction The six Swiss wine regions Figure 1: The six Swiss wine regions. (SWP, 2015) 5 / 30

6 Introduction General considerations Panel data constructed from the database Nielsen that Swiss Wine Market Observatory ( made available retail market, data scanned at the till about 1/3 of whole Swiss wine consumption Main variables: income, quantity and price (interpreted as equilibrium between supply and demand) Other channels of distribution: direct sale, Horeca, wholesalers (see Mercuriale of OSMV) Lack of wine vintage information (year) of the bottles we assume that the Production t (example: from the harvest 2013) will enter the retail market in t + 1 (more precisely, 04/2014) 6 / 30

7 Data Data Structure of the panel 4 weekly data (13 observations per year) 5 years of time span ( ) updated every trimester Identification variables: color, region, subregion, AOC (protected designation of origin), foreign, etc. 80 Swiss AOC types of wines (examples: Merlot red Ticino, Fendant white Valais) For identification, we use only AOC wines Type of variables Dependent variables: income, quantity, price and promotions facts Economic variables: exchange rates, Swiss CPI, wine import prices Climatic variables: temperature, sunshine and rainfall 7 / 30

8 Data Descriptive statistics Table 1: Descriptive statistics by treatment and control group Treatment group Control group Variable Mean Before After Mean Before After Dependent variable Income Quantity Price Income Promo Quantity Promo Price Promo Economic variable Exchange Rate EUR/CHF Exchange Rate GBP/CHF Exchange Rate USD/CHF Consumer Price Index Import Price Italy Red Import Price Italy White Import Price France Red Import Price France White Import Price Spain Red Import Price Spain White Climatic variable Temperature Mean ( C) Temperature Minimum Temperature Maximum Wind Mean (km/h) Wind Maximum Sunshine (hours) Rainfall (mm) Pressure Minimum (hpa) Pressure Maximum (hpa) Observations No. of labels / 30

9 Data Swiss wine is a heterogeneous good Figure 2: Proportion of wine color by region. 9 / 30

10 Data Examples of types of wines (a) Neuchâtel (b) Vaud (c) Ticino 10 / 30

11 Data Swiss wines experience strong seasonality in consumption Figure 3: Seasonality consumption of three treated wines. 11 / 30

12 Data Swiss wine consumption by group (retail market) (a) Treated group (OSMV, 2016) (b) Control group (OSMV, 2016) 12 / 30

13 Data Three Lakes wine consumption (FOAG, 2016) Figure 4: Volume for all channels of distribution. (FOAG, 2016) 13 / 30

14 Identification strategy: Difference-in-Differences Identification strategy Appropriate econometric model: Difference-in-Differences (DID) Treated group (Three Lakes) Hail weather shock date (20/06/2013) Cut-off point (04/2014) Limitations? Start date (01/2012) End date (03/2015) Wine data from the retail market only (about 1/3 of the whole wine consumption) Strong assumption: lack of vintage information 14 / 30

15 Identification strategy: Difference-in-Differences Theoretical approach What do we expect from a negative exogenous supply shock? Consumption of treated wines Prices of treated wines 15 / 30

16 Identification strategy: Difference-in-Differences Econometric model We use the following DID regression equations: Outcome i,t = β 0 + β 1 Treat i + β 2 Post t + β 3 (Treat i Post t ) i,t + ɛ i,t (1) ln(q i,t ) = β 0 + β 3 (Treat i Post t ) i,t + S i,tθ + z tγ + c i + δ t + ɛ i,t (2) ln(p i,t ) = β 0 + β 3 (Treat i Post t ) i,t + S i,tθ + z tγ + c i + δ t + ɛ i,t (3) With: { 0 if control group Treat i = 1 if treated group (Three Lakes) { 0 if before-treatment period (01/ /2014) Post t = 1 if post-treatment period (04/ /2015) 16 / 30

17 Identification strategy: Difference-in-Differences Parallel trend assumption (visual evidence) Figure 6: Time trend for quantity. 17 / 30

18 Results Results (1/3): ln(quantity) Table 2: Regression results for ln(quantity) (1) (2) (3) (4) RE RE FE FE Treat i Post t ** ** ** *** (0.0991) (0.0988) (0.0991) (0.0691) Covariates no no no yes Individual FE no no yes yes Time FE no yes yes yes Constant *** *** *** ** (0.2724) (0.2820) (0.0650) ( ) Observations No. of labels R-squared Note: *** p<0.01, ** p<0.05, * p<0.1; clustered robust standard errors (individual) in parentheses; RE=random effect (individual); FE=fixed effect (individual). 18 / 30

19 Results Results (2/3): ln(price) Table 3: Regression results for ln(price) (1) (2) Treat i Post t * ** (0.0277) (0.0135) Covariates no yes Constant *** *** (0.0157) (8.7366) Observations No. of labels R-squared Note: *** p<0.01, ** p<0.05, * p<0.1; clustered robust standard errors (individual) in parentheses. 19 / 30

20 Results Results (3/3): ln(income) Table 4: Regression results for ln(income) (1) (2) FE FE Treat i Post t ** *** (0.0839) (0.0656) Covariates no yes Constant *** (0.0577) ( ) Observations No. of labels R-squared Note: *** p<0.01, ** p<0.05, * p<0.1; clustered robust standard errors (individual) in parentheses. 20 / 30

21 Robustness checks Robustness checks (1/4): ln(quantity) Table 5: Placebo pre-post treatment regressions (quantity) (1) (1) Pre-Treatment Post-Treatment Treat i Post t (0.0883) ( ) Constant * ( ) ( ) Observations No. of labels R-squared Note: *** p<0.01, ** p<0.05, * p<0.1; clustered robust standard errors (individual) in parentheses. 21 / 30

22 Robustness checks Robustness checks (2/4): ln(quantity) Table 6: Placebo control regions regression (quantity) (1) (2) (3) (4) (5) Placebo Region Valais Vaud Geneva G-CH Ticino Treat i Post t (0.0497) (0.0833) (0.0927) (0.0714) (0.0746) Constant ** * * * ** ( ) ( ) ( ) ( ) ( ) Observations No. of labels R-squared Note: *** p<0.01, ** p<0.05, * p<0.1; clustered robust standard errors (individual) in parentheses. 22 / 30

23 Robustness checks Robustness checks (3/4): ln(quantity) Table 7: Regressions for different configurations of the control group (quantity) (1) (2) (3) (4) (5) Taken Out Valais Vaud Geneva G-CH Ticino Treat i Post t *** *** *** *** *** (0.0712) (0.0720) ( ) (0.0708) (0.0704) Constant *** * * * ( ) ( ) ( ) ( ) ( ) Observations No. of labels R-squared Note: *** p<0.01, ** p<0.05, * p<0.1; clustered robust standard errors (individual) in parentheses. 23 / 30

24 Robustness checks Robustness checks (4/4): ln(quantity) Table 8: Regressions by color type (quantity) (1) (2) (3) Treat i Post t *** *** *** (0.0826) (0.0765) (0.0870) Color Treated White All White Color Control All Red & Rosé Red & Rosé Constant ( ) ( ) ( ) Observations No. of labels R-squared Note: *** p<0.01, ** p<0.05, * p<0.1; clustered robust standard errors (individual) in parentheses. 24 / 30

25 Robustness checks DID regression with leads and lags Autor (2003) analyses the effect of increased employment protection on the firm s use of temporary help workers, including both leads and lags in a DID regression: ln(q i,t ) = β t= q m β3(treat t i δ t ) i,t + β3(treat t i δ t ) i,t +S i,tθ+c i +ɛ i,t (4) t=0 We allow β 3 (coefficient of interest) to vary across time: β t 3 We set t = 0 the starting of the treatment (cut-off date) δ t could be monthly, trimester, semester or year dummies q leads 1 t= q βt 3 (Treat i δ t ) i,t : analysis of pre-trends characteristics m lags m t=0 βt 3 (Treat i δ t ) i,t : analysis of treatment effect changes (if any) over time after treatment 25 / 30

26 Robustness checks Estimated shock effect over time Figure 7: Estimated shock effect over time (semester dummies). 26 / 30

27 Conclusion Conclusion Unique natural experiment: hail weather shock in the wine region Three Lakes in 2013 with panel data monthly quantity and price for different types of Swiss wines AOC in the retail market (Nielsen). Strong visual evidence of the parallel trend assumption, before the weather shock, between the treated group and the control group. Statistically significant ATE effects on Three Lakes wines in terms of consumption (-22.8%) and price (+2.8%) several robustness checks confirm the validity and stability of the results. This study could give a contribution to predict and forecast wine outcomes in support of appropriate economic policy decisions when a supply shock occurs in this specific market. Insurance (Suisse grêle), stock management (climate reserve), cantonal and federal subsidies, implication for the wine industry and producers. Internal validity (causal effect through DID framework) versus external validity (similar weather shocks in other wine regions or other agricultural commodities). 27 / 30

28 Conclusion Thank you for your attention! (a) Lavaux (Vaud) (b) Valais (c) Boccalino of Merlot (Ticino) (d) Château d Auvernier (Neuchâtel) 28 / 30

29 Conclusion References Angrist, J. D. and J.-S. Pischke (2008): Mostly Harmless Econometrics: An Empiricist s Companion, Princeton University Press, 52, Ashenfelter, O., S. Ciccarella, and H. J. Shatz (2007): French Wine and the U.S. Boycott of 2003: Does Politics Really Affect Commerce? NBER Working Papers 13258, National Bureau of Economic Research, Inc. Ashenfelter, O. C. (1978): Estimating the Effect of Training Programs on Earnings, The Review of Economics and Statistics, 60, Autor, D. H. (2003): Outsourcing at Will: The Contribution of Unjust Dismissal Doctrine to the Growth of Employment Outsourcing, Journal of Labor Economics, 21, Card, D. and A. B. Krueger (1994): Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania, American Economic Review, 84, Egger, P., M. Larch, M. Pfaffermayr, and H. Winner (2006): The impact of endogenous tax treaties on foreign direct investment: theory and evidence, Canadian Journal of Economics, 39, FOAG (2016): Année viticole 2015, Federal Office for Agriculture, Statistiques vitivinicoles. OSMV (2016): Observatoire suisse du marché des vins, Rapport N05, Année 2015, Changins Haute école en viticulture et oenologie, Swiss wine promotion. SWP (2015): Swiss Wine Promotion,. 29 / 30

30 Conclusion Definition of the Average Treatment Effect (ATE) E{Y T =t i D i = d} = β 0 + β 1 d + β 2 t + β 3 (dt) + ɛ i,t (5) { 0 if control group d = 1 if treated group (Three Lakes) { 0 if before-treatment period (01/ /2014) t = 1 if post-treatment period (04/ /2015) β 0 = E{Y 0 i D i = 0} β 1 = E{Y 0 i D i = 1} β 0 = E{Y 0 i D i = 1} E{Y 0 i D i = 0} β 2 = E{Y 1 i D i = 0} β 0 = E{Y 1 i D i = 0} E{Y 0 i D i = 0} β 3 = E{Yi 1 D i = 1} β 0 β 1 β 2 = E{Yi 1 D i = 1} E{Yi 0 D i = 0} E{Yi 0 D i = 1} + E{Yi 0 D i = 0} E{Yi 1 D i = 0} + E{Yi 0 D i = 0} = E{Yi 1 D i = 1} E{Yi 0 D i = 1} (E{Yi 1 D i = 0} E{Yi 0 D i = 0}) 30 / 30