1. An Unconventional Empirical Analysis of the Relationship between Poverty and Income Inequality for Turkey Sadullah Çelik and Deniz Şatıroğlu 2 nd Annual Conference of the Society for Economic Measurement OECD Center, Paris, France 22-24 July 2015

2. I. Motivation II. Possible Discussions III. Literature Survey IV. Methodology and Data V. Empirical Findings VI. Concluding Remarks

3. I. Motivation  Poverty is one of the most compelling problems for societies.  Similarly, income inequality is also a fundamental issue both in terms of social and economic aspects.  These two concepts are linked to each other with one of the most important economic measures, that is inflation.

4.  Inflation has 1) direct effects on income distribution through distortion and 2) indirect effects on poverty associated with the purchasing power observed through the household’s consumption patterns. Therefore, the motivation of this study is to test the relationship between poverty and income inequality by employing survey data results instead of the well-known proxies. The aim is to examine whether survey results are capable of explaining the dynamics of the relationship.

5. II. Possible Discussions  In order to obtain a robust empirical relationship, is it possible to use income or wage data?  NO, only available for Turkey at low frequency.  Are the households poor/middle income/rich? Do they invest in stock market instead of consuming?  NO, Financial markets in Turkey are rather shallow and thus very speculative for small investors.

6.  Are the households precautionary savers?  May be, but consumption behavior is still the main determinant for poverty.  Is there any possible data which could be strongly related with poverty and income inequality? YES! Direct measurements (surveys and high frequency data) can be employed.

7. Official Poverty Rates and Income Share in Turkey

8. Gini Index for Turkey

9. III. Literature Review  Kuznets (1955)  Adelman and Morris (1973)  Paukert (1973)  Sen (1981)  Anand and Kanbur (1984)  Kakwani (1986)  Fields (1989)  Morley and Alvarez (1991)  Ravallion and Datt (1991)  Cardosa (1992)

10.  Rowntree (1998)  Whelan et. al. (2000)  Naschold (2002)  Saunders (2004)  Wade (2004)  Albanesi (2006)  Mingione (2008)  Ravallion and Chen (2009)  Çelik and Başdaş (2010)

11. Food Prices  Hull (2009)  Ivanic and Martin (2011)  Walsh and Jiangyan (2012)  Headey (2014) Turkey  Aran et. al. (2010)

12. IV. Methodology and Data  Time Domain Granger Causality  Geweke Test of Linear Feedback By using a Fourier transformation to VAR (p) model for x and y series, the Geweke’s measure of linear feedback from y to x at frequency ω is defined as :

13.  Frequency Domain Granger Causality

14.  Wavelet Comovement The wavelet-based measure  allows one to quantify the comovement in the time-frequency space  assess over which periods of time and frequencies, the comovement is higher through the correlation coefficient (Rua, 2010).  Basically, it plays a role as a contemporaneous correlation coefficient around each moment in time and for each frequency.

15. Consumption reflects a household’s ability to meet basic needs. the Retail Sector Confidence Survey (of TEPAV) is taken as a proxy for consumption. the Consumer Price Index (of TUIK) as a proxy for income inequality. Indirect Measurement Direct Measurement Poverty: The Headcount Index or Poverty Rates (complete poverty, food poverty and relative poverty). Income inequality: GINI Index or Theil Index

16.  The data set is monthly and runs through January 2011 – February 2014.  Seasonally adjusted data sets both for the CPI and the RSCS without sectoral categories dispersion.  The sector categories of the retail sector confidence survey are picked in accordance with the main expenditure groups of CPI (5 sectors, 6 survey questions). DEMETRA seasonal adjustment software

18. RSCS Questions 1. In what direction was the status of your business within the past 3 months? (increased, the same, decreased) (Past) 2. What do you think about your existing stock level? (above the average, normal, below the average) (Current) 3. In what directions do you think that your orders from the suppliers will change for the next 3 months? (will increase, will be the same, will decrease) (Future) 4. In which directions do you think that your sales are going to be for the next 3 months? (will increase, will be the same, will decrease) (Future) 5. In which direction is the number of employees going to move for the next 3 months? (will increase, will remain the same, will decrease) (Future) 6. What do you think your selling price is going to be within the next 3 months? (will increase, no idea, will decrease) (Future)

19. So, Seasonally adjusted CPI of TUİK is used for income inequality, CSA and Seasonally adjusted RSCI of TEPAV is used for poverty, TSA. These two are the main indices. We also have sub-indices derived from categories and questions.

20. Categorical Variables (T for TEPAV and C for CPI with next word for sectors) TFOOD and CFOOD for ‘food and beverages’, TTEX and CTEX for ‘textile and shoes’, TTRANS and CTRANS for ‘transportation’, TFURN and CFURN for ‘furnishing and household equipment’ and TMISC and CMISC for ‘miscellaneous goods and services’

21. V. Empirical Findings  Time Domain Granger Causality for CSA and TSA

22.  Geweke Test of Linear Feedback

30.  Frequency Domain Granger Causality

38.  Wavelet Comovement

46. VI. Conclusion A new perspective into the poverty and income inequality relationship Proxies used instead of one single analysis Methods employed also are unconventional Consumption as a proxy for poverty explains most of the variation in ‘food and beverages’ (Not just past but also future consumption expectations of sellers) Poverty causes income inequality in ‘food and beverages’ and ‘transportation’ Income inequality causes differentiating patterns of consumption in retail sectors

47. Observations of sellers about consumption levels (based on both past and future) and the change in consumer price of the sectors via CPI have strong oscillations and statistically significant correlations in ‘textile and shoes’ and ‘miscellaneous goods and services’ at several frequencies along the time line. In summary, the findings highlight the distortion of income, 1) inflation channel elicits a change in the consumption level at several frequency levels 2) as well as expectations showing strong causality 3) speculation of poverty causes income inequality that obtains its validity through some important sectors

48. Thank you for your patience!