ARTICLE PRESENTATION Engel’s law in China: Some new evidence by Menggen Chen Furkan Demir 2018300204 Aim of the Paper • Investigating the applicability of Engel’s law in China with; • A cross sectional data in 2016 covering the 31 provincial regions in the country Engel’s Law • “The proportion of food in total consumption expenditure is negatively associated with household income.” • It was defined after the German statistician Ernst Engel from his findings in 1857 based on a survey of worker families in Belgium. Engel’s Coefficient • It has been an important international indicator to measure household living standards. • It can be used to; • Show living standards by revealing the consumption structure • In different countries or regions in specific periods of time (a cross-section) • Reflect the dynamics of consumption structure and the change in standards (a time-series) Engel’s Coefficient • Orshansky(1969) found that families that have an Engel’s coefficient above 0.3, are usually considered poor. • In the 1970’s, the Food and Agriculture Organization(FAO) of the UN used this coefficient to standardize poverty measurement for countries and regions. • Above 59% - poor, 50%-59% - adequate food, 40%-50% - relatively well-off, 30%-40% - well-off, below 30% - the richest Problems with the Coefficient • Differences in consumer goods prices in different regions, residents’ customs, social welfare allowances, and choices of food caused problems in “comparability transversely or vertically”. Methodology • Engel curve is found to be useful tool for measuring elasticity of income for commodities, described as: • The amount of change of the consumption proportion for a good when income increases or decreases 1%. • After Ernst Engel, many scholars conducted studies on the law. Methodology 1. Linear Engel Curve Method • Linear correlation • Ogburn(1919) and Allen and Bowley(1935) used as a basic model. • However, conclusions demonstrated that the estimating error was large related to consumer tastes from different regions not being homogenous. 2. Working-Leser Model • Some researchers linearly related food consumption to ln(y). • Working(1943) used • Leser(1963) proved that this model worked better than others. Methodology 3. Rank-three model •. 4. Method of nonlinear system • Many scholars used Kernel estimation to measure Engel curve. Methodology • Since food prices in China have high volatility, this study intended to incorporate food price and other control variables into the model. Methodology • There are two kinds of factors that affect the relationship between Engel’s coefficient and income: • Economic • Natural • To represent the economic factors, the author used the indicators below. • Growth rate of the gross domestic product • The ratio of the urban/rural population • GDP(Indust) • Control variables (land, temp, transport etc.) Food Price Measurement • “CPI and PPP both belong to the indicators of price measurement.” • The effect of varying food prices on Engel’s coefficient was overlooked before. • In the paper, author measured the subnational PPPs of food in China with both weighted and nonweighted methods. • The seperation is made according to the basic-heading level. • Basic-heading level: “A group of similar well-defined goods or services” • “It is at the level of the basic heading that expenditures are defined, products selected, prices collected, prices edited, and PPPs first calculated and averaged.” Food Price Measurement – Used Methods 1. Below the basic-heading level • CPD method 2. Above the basic-heading level • GEKS method • GK method • WCPD method Data 1. Engel’s coefficient 2. Income per capita • Urban residents’ disposable income per capita 3. Food price and weight 4. Consumption expenditure per capita 5. Other control variables Empirical Study – Preliminary Analysis Gansu had the lowest income per capita, 25.693 yuan Beijing had the lowest Engel’s coefficient, 21.10% Shanghai had the highest income per capita, 57.691 yuan Tibet had the highest Engel’s coefficient, 44.89% AVERAGE Income per capita – 32.290 yuan Engel’s coefficient – 29.83% Empirical Study – Preliminary Analysis “The correlation coefficient between Engel’s coefficient and residents’ income is -0.33 and exhibits a weak negative correlation.” Measurement of Food Price • Due to geographical complexities and high costs of transportation, food PPP in Tibet and Gansu is very high. • The correlation between Engel’s coefficient and food PPPs is 0.60, indicating a strong positive relationship. • As a result, when trying to reach a conclusion between income level and Engel’s coefficient, a strong emphasis should be made upon food prices. Results of the Augmented Models • After taking food PPPs into account, the significance of coefficient estimates goes up. • The difference in food prices among regions influences the coefficient significantly. (Tibet) • After incorporating other control variables, the inverse relationship between income does not change. However, the significance of food prices dilute a bit when other variables are considered. • Annual average temperature and transportation have a strong positive effect on EC, while the social security of the citizens effect the opposite way. • The augmented rank-three model is slightly better than other models. A Further Study • “When Ernst Engel proposed Engel's law, food consumption included usually necessary goods for life. To date, food consumption has been increasingly abundant, and the structure of apenditures on food consumption has varied greatly. With the rapid development of more than 40 years after the Reform and Opening, China has solved the problems of food. The residents’ consumption structure is in the process of being upgraded, and, in recent years, demand has been growing quickly for advanced and highquality foods such as green food, leisure food, and nutritious food. In economics, goods can be divided into necessities of life and luxury goods, based on the concept of elasticity. So we can calculate an indicator, the income elasticity of food (IEF). According to the income elasticity of different types, food can be grouped into life necessities and life nonnecessities, and the income elasticity of the former must be less than that of the latter. “ Life Necessities and Nonnecessities IEC and SEC Findings from the Further Study • For both IEC and SEC, there is an inverse relationship with the Engel’s coefficient. That provides strong support for the applicability of the law in the country of China. • IEC is more intensely effected by the income level. • The models that include price variables had shown relatively stronger evidence for Engel’s law. • If the food price is high in some region, both necessities and nonnecessities, Engel’s coefficient is also high. Conclusion • The author claimed that the study revealed Engel’s law to be applicable in China. • There is a strong negative correlation between income level and the coefficient, which is even stronger when regional food PPPs are taken into account. • Stronger relationship with IEC than SEC. • Regional consumption and family structures should be further investigated. References • Chen, M. (2022). Engel’s law in China: Some new evidence. Review of Development Economics, 26, 1640-1662 End of this presentation. Thank you!
