This assignment is for ETC5521 Assignment 1 by Team emu comprising of Xiaoyu Tian and Yin Shan Ho.

Introduction and motivation

This report is to explore the Big Mac indices derived from the Big Mac prices around the world, and how the indices is associated with a country’s purchasing power. Since going for a MC Donald’s meal is really what people often do in their daily lives and the Big Mac is the most popular universal menu items, it is interesting to see that the price of the Big Mac burger can actually be used as an benchmark to predict the economic related events that matters for our daily lives. Hence, our team is motivated to further explore this topic. The focus of this report will be on how the price of the big mac has changed over time and how the big mac indices might predict the foreign exchange rate, as well as indicating the purchasing power for that country.

Questions based on the data:

  1. How much has the Big Mac prices changed over time around the world?

  2. Compare and contrast the Big Mac Index with the official exchange rate, and how did the index reflect the value of the currency?

  3. How has the global GDP changed over time and how does the purchasing power affected based on the changes?

Data description

The data used in this report is about the Big Mac Index. The Big Mac Index is calculated simply by dividing the local price of a Big Mac in one country by the local price of a Big Mac in another country in their respective currencies. When compared with the official exchange rate, the Big Mac Index reflects which currencies are under or overvalued. This data set is provided by the Economists and the source data are from several places. The Big Mac prices are directly from the reporting of McDonald’s around the world; exchange rates are from Thomson Reuters; GDP data are from the IMF World Economist Outlook reports. Detailed structure of the data, including variables and their types are listed in the data dictionary as follows.

Data dictionary
No.  Variable Description Type Source
1 date Date of observation Date
2 iso_a3 Three-character ISO 3166-1 country code Character
3 currency_code Three-character ISO 4217 currency code Character
4 name Country name Character
5 local_price Price of a Big Mac in the local currency Numeric McDonalds; The Economist
6 dollar_ex Local currency units per dollar Numeric Thomson Reuters
7 dollar_price Price of a Big Mac in dollars Numeric
8 USD_raw Raw index, relative to the US dollar Numeric
9 EUR_raw Raw index, relative to the Euro Numeric
10 GBP_raw Raw index, relative to the British pound Numeric
11 JPY_raw Raw index, relative to the Japanese yen Numeric
12 CNY_raw Raw index, relative to the Chinese yuan Numeric
13 GDP_dollar GDP per person, in dollars Numeric IMF
14 adj_price GDP-adjusted price of a Big Mac, in dollars Numeric
15 USD_adjusted Adjusted index, relative to the US dollar Numeric
16 EUR_adjusted Adjusted index, relative to the Euro Numeric
17 GBP_adjusted Adjusted index, relative to the British pound Numeric
18 JPY_adjusted Adjusted index, relative to the Japanese yen Numeric
19 CNY_adjusted Adjusted index, relative to the Chinese yuan Numeric

Data Wrangling

##  [1] "Argentina"            "Australia"            "Brazil"              
##  [4] "Canada"               "Switzerland"          "Chile"               
##  [7] "China"                "Czech Republic"       "Denmark"             
## [10] "Euro area"            "Britain"              "Hong Kong"           
## [13] "Hungary"              "Indonesia"            "Israel"              
## [16] "Japan"                "South Korea"          "Mexico"              
## [19] "Malaysia"             "New Zealand"          "Poland"              
## [22] "Russia"               "Singapore"            "Sweden"              
## [25] "Thailand"             "Taiwan"               "United States"       
## [28] "South Africa"         "Philippines"          "Norway"              
## [31] "Peru"                 "Turkey"               "Egypt"               
## [34] "Colombia"             "Costa Rica"           "Sri Lanka"           
## [37] "Pakistan"             "Saudi Arabia"         "Ukraine"             
## [40] "Uruguay"              "UAE"                  "India"               
## [43] "Vietnam"              "United Arab Emirates" "Azerbaijan"          
## [46] "Bahrain"              "Guatemala"            "Honduras"            
## [49] "Croatia"              "Jordan"               "Kuwait"              
## [52] "Lebanon"              "Moldova"              "Nicaragua"           
## [55] "Oman"                 "Qatar"                "Romania"
##  [1] "ARG" "AUS" "BRA" "CAN" "CHE" "CHL" "CHN" "CZE" "DNK" "EUZ" "GBR" "HKG"
## [13] "HUN" "IDN" "ISR" "JPN" "KOR" "MEX" "MYS" "NZL" "POL" "RUS" "SGP" "SWE"
## [25] "THA" "TWN" "USA" "ZAF" "PHL" "NOR" "PER" "TUR" "EGY" "COL" "CRI" "LKA"
## [37] "PAK" "SAU" "UKR" "URY" "ARE" "IND" "VNM" "AZE" "BHR" "GTM" "HND" "HRV"
## [49] "JOR" "KWT" "LBN" "MDA" "NIC" "OMN" "QAT" "ROU"

After reviewing the dataset, we found there are 56 unique country identifying code, but 57 country name. We discovered that the United Arab Emirates is recorded as both “UAE” and “United Arab Emirates” in the column ‘name’. Thus, we need to check whether there are duplicated data.

##          date iso_a3 currency_code                 name local_price dollar_ex
## 22 2018-01-01    ARE           AED                  UAE       14.00   3.67300
## 23 2018-07-01    ARE           AED United Arab Emirates       14.00   3.67315
## 24 2019-01-01    ARE           AED United Arab Emirates       14.00   3.67315
## 25 2019-07-09    ARE           AED United Arab Emirates       14.75   3.67315
## 26 2020-01-14    ARE           AED United Arab Emirates       14.75   3.67315
## 27 2020-07-01    ARE           AED United Arab Emirates       14.75   3.67295
##    dollar_price  usd_raw  eur_raw  gbp_raw jpy_raw cny_raw gdp_dollar adj_price
## 22     3.811598 -0.27811 -0.21179 -0.13629 0.11063 0.20177         NA        NA
## 23     3.811442 -0.30827 -0.19506 -0.09928 0.08724 0.23119         NA        NA
## 24     3.811442 -0.31695 -0.17891 -0.06422 0.05978 0.24915         NA        NA
## 25     4.015627 -0.30041 -0.12174 -0.02089 0.11990 0.31648         NA        NA
## 26     4.015627 -0.29178 -0.12362 -0.08947 0.13302 0.28593         NA        NA
## 27     4.015846 -0.29670 -0.16094 -0.06113 0.10461 0.29608         NA        NA
##    usd_adjusted eur_adjusted gbp_adjusted jpy_adjusted cny_adjusted
## 22           NA           NA           NA           NA           NA
## 23           NA           NA           NA           NA           NA
## 24           NA           NA           NA           NA           NA
## 25           NA           NA           NA           NA           NA
## 26           NA           NA           NA           NA           NA
## 27           NA           NA           NA           NA           NA

By filtering the name by the two country name, we found that there is no duplication in the dataset. It’s the problem with the change in the recorded name of that country. Hence, we changed the name of “UAE” to its full name “United Arab Emirates”. However, by reviewing the dataset we realised that there are Nas in the dataset, and hence we conducted a check on the percentage of missing values in the dataset.

We can see that most of variables have 0 missing value, but for the GDP_dollar, the data before 2011 are all missing, and hence all the adjusted price that derived from the GDP is missing as well, and hence the following analysis on the GDP and adjusted price will be mainly focus on the year from 2011 to 2020.

Analysis and findings

How much has the Big Mac prices changed over time around the world?

Overview of the nominal price change by map

The map above shows the price change of the Big Mac in US Dollar overtime. The darker color represents higher price while the lighter means lower. Based on the map, it is found that the price of the Big Mac is comparatively higher in Northern European countries, American continents as well as some of the Middle East areas. The details are shown in the table below. Also, it is interesting to see that the colours in the area of North America and Europe has dramatically became darker after 2008 and 2012, while the areas in Asia almost remain unchanged. This could be due to the Global Financial Crisis and the European Debt Crisis which were well-known in the year of 2008 and 2012.

Detailed Data

The plot above is the average percentage on the nominal price of Big Mac in US Dollar. According to the table, there are great increases of the Big Mac nominal price in the high income regions especially in Northern European countries like Norway, Sweden, Denmark and Switzerland. It indicates that the price of Big Mac is going rather high in these areas which is possible due to the higher price level in the high income countries.
However, it is found that there was an average decrease on the nominal price of Big Mac price in South East Asian middle income countries like Thailand, Vietnam, Sri Lanka and Indonesia which are having negative growth of the price.
On the other hand, it is interestingly found that there are negative growth on the price in High income regions like Hong Kong SAR and Chile. As the price level in Hong Kong kept on increasing in the recent decades, the decrease on the nominal price of the Big Mac is very surprising.

Compare and contrast the Big Mac Index with the official exchange rate, and how did the index reflect the value of the currency?

Trend of Big Mac Index

The plot above shows the trend of the Big mac Index of different countries. As the Big Mac Index reflects the currency value, we can easily indicate whether the currency is overvalued or undervalued for the time from the index. Based on the plot, the green bars indicate that the currency is undervalued whereas the red indicate overvalued. The Blue lines are the trend of the movement on currencies’ exchange rate based on US Dollar. It is found that Brazilian Real and Colombian Pesos are highly overvalued. Meanwhile, Malaysian Ringgit and New Taiwan Dollar are found highly undervalued.
There are some special findings that some of the currencies of high income regions with high nominal price of the Big Mac are actually undervalued. For instance, the Northern Europeans.
On the other hand, from the previous section, it was found that, the nominal price of the Big Mac had a negative growth in Chile, whereas the Chilean Peso is found overvalued here.

How has the global GDP changed over time and how does the purchasing power affected based on the changes?

The dumbbell chart shows the price of the Big Mac in each countries when adjusted by GDP from 2011 to 2020. It is interesting to see that there is a significant adjusted price decrease in Northern European, such as Norway, Switzerland, etc., where as discussed in the previous section that Norway has actually the highest nominal price increase over time. This might indicate a deflation in the Northern European countries. In contrast, we can see that the real price of a big mac in Asian countries remains stable over time. While all the developed countries has a higher adjusted price in burger, the price in the developing countries is significantly lower. This might suggest that poorer countries have lower labor costs than richer countries, and thus has less affected by the GDP factors.

Detailed data

The bar chart above reflects the changes in the purchasing power over time for each countries. This proves that the European, North American Countries, and Australia had the strong purchasing power over time. Also, it is interesting to see that they follows the similar trends, where reached the peak purchasing power around 2016. As for the others, we see medium purchasing power and slow growth in East Asia and Singapore, while a low and unchanged purchasing power in South American and South East Asian.

Average number of burger affordable based on GDP per capita
Country Avg burger affordable
Norway 13770.4887
Switzerland 13429.8220
Australia 11505.6906
Denmark 11437.0405
United States 11114.8033
Sweden 11040.1661
Singapore 11024.6640
Canada 10262.7998
Britain 9425.8602
Hong Kong 9314.9018
Japan 9275.6037
New Zealand 9091.4452
Euro area 9004.8729
Israel 8688.0170
South Korea 7075.7004
Taiwan 6193.3482
Saudi Arabia 6102.9605
Czech Republic 5690.7391
Chile 4424.9527
Hungary 4311.3168
Poland 4222.9877
Argentina 3898.8201
Russia 3669.1754
Brazil 3423.2199
Turkey 3354.6757
Malaysia 3268.8577
Mexico 3219.1317
China 2436.3619
Colombia 2331.2310
South Africa 2291.4798
Peru 2176.7325
Thailand 2052.4777
Indonesia 1278.0808
Egypt 1108.0914
Philippines 983.0686
India 614.8603
Pakistan 503.0952

By calculating the average amount of burgers affordable for each countries, we can see that European and North American countries remains the top of the lists. While Pakistan’s GDP could only afford 503 Big Macs on average, Norway can buy 13770 Big Macs, which is 27 times higher. This might indicate the huge purchasing power between developed countries and developing countries.

Conclusion

In conclusion, it is found that the price of the Big Mac is comparatively higher in the high income region. Also, the price in those regions has dramatically increased after 2008 and 2012, while the prices in Asia are low and almost remain unchanged, and some of them even having negative growth. However, when taking the GDP factors into account, we see that there were negative growth in the real price in these high income countries, while unchanged growth in low income countries. This suggests that higher developed countries tends to have higher purchasing powers and their currencies are more likely to be undervalued.

References

  1. EU. (April 23, 2021). Which countries use the euro. Retrieved August 16, 2021, from https://europa.eu/european-union/about-eu/euro/which-countries-use-euro_en

  2. The Economist. The Big Mac index. Retrieved August 26, 2021, from https://www.economist.com/big-mac-index

  3. World Bank. World Bank Country and Lending Groups. Retrieved August 26, 2021, from https://datahelpdesk.worldbank.org/knowledgebase/articles/906519