Innovative Analysis of Global Economic Performance Using the Penn World Table
RISHA BHATT
2024-12-13
The analysis of productivity and Gross Domestic Product (GDP) is central to understanding the economic performance of countries. Productivity, which measures output per worker, is directly linked to GDP growth and national economic development. High productivity leads to better resource utilization, increased output, and improved standards of living. Conversely, low productivity can hinder economic growth and contribute to stagnation. This study explores global productivity trends using data from the Penn World Table, highlighting the factors that drive high productivity and providing policy recommendations to improve economic performance in regions with lower metrics.
Objective
This analysis explores economic performance through: 1. Identifying key economic trends. 2. Evaluating GDP, productivity, and labor indicators. 3. Highlighting disparities across regions and income groups. 4. Utilizing unique visualizations to present impactful insights.
Dataset Selection
The Penn World Table 10.0 dataset provides detailed information on global economic metrics. For this analysis, we focus on: - Real GDP (rgdpna) - Total Factor Productivity (rtfpna) - Labor input (emp, avh) - Population (pop)
pwt <- read_excel("C:/Users/Risbha/Desktop/RISHA/stats by Dr. rahul/pwt100 (1).xlsx",
sheet = "Data")
head(pwt)## # A tibble: 6 × 52
## countrycode country currency_unit year rgdpe rgdpo pop emp avh hc
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ABW Aruba Aruban Guilder 1950 NA NA NA NA NA NA
## 2 ABW Aruba Aruban Guilder 1951 NA NA NA NA NA NA
## 3 ABW Aruba Aruban Guilder 1952 NA NA NA NA NA NA
## 4 ABW Aruba Aruban Guilder 1953 NA NA NA NA NA NA
## 5 ABW Aruba Aruban Guilder 1954 NA NA NA NA NA NA
## 6 ABW Aruba Aruban Guilder 1955 NA NA NA NA NA NA
## # ℹ 42 more variables: ccon <dbl>, cda <dbl>, cgdpe <dbl>, cgdpo <dbl>,
## # cn <dbl>, ck <dbl>, ctfp <dbl>, cwtfp <dbl>, rgdpna <dbl>, rconna <dbl>,
## # rdana <dbl>, rnna <dbl>, rkna <dbl>, rtfpna <dbl>, rwtfpna <dbl>,
## # labsh <dbl>, irr <dbl>, delta <dbl>, xr <dbl>, pl_con <dbl>, pl_da <dbl>,
## # pl_gdpo <dbl>, i_cig <chr>, i_xm <chr>, i_xr <chr>, i_outlier <chr>,
## # i_irr <chr>, cor_exp <dbl>, statcap <dbl>, csh_c <dbl>, csh_i <dbl>,
## # csh_g <dbl>, csh_x <dbl>, csh_m <dbl>, csh_r <dbl>, pl_c <dbl>, …Filter Data for Recent Year (2019)
We filter for the most recent data (2019) and create additional calculated metrics: 1. GDP per Capita: Real GDP divided by population. 2. Productivity per Worker: TFP divided by employment.
pwt_2019 <- pwt %>%
filter(year == 2019) %>%
select(country, countrycode, rgdpna, emp, avh, rtfpna, pop) %>%
mutate(
gdp_per_capita = rgdpna / pop,
productivity_per_worker = rtfpna / emp
)
head(pwt_2019)## # A tibble: 6 × 9
## country countrycode rgdpna emp avh rtfpna pop gdp_per_capita
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Aruba ABW 3.07e3 0.0476 NA NA 0.106 28865.
## 2 Angola AGO 2.22e5 16.6 NA 0.938 31.8 6980.
## 3 Anguilla AIA 2.23e2 NA NA NA 0.0149 15028.
## 4 Albania ALB 3.72e4 1.08 NA NA 2.88 12914.
## 5 United Arab Em… ARE 6.48e5 5.81 NA NA 9.77 66320.
## 6 Argentina ARG 9.76e5 20.6 1609. 0.932 44.8 21785.
## # ℹ 1 more variable: productivity_per_worker <dbl>Table Output
Top 10 Countries by GDP Per Capita
top_gdp_capita <- pwt_2019 %>%
arrange(desc(gdp_per_capita)) %>%
head(10)
knitr::kable(top_gdp_capita, caption = "Top 10 Countries by GDP Per Capita (2019)")| country | countrycode | rgdpna | emp | avh | rtfpna | pop | gdp_per_capita | productivity_per_worker |
|---|---|---|---|---|---|---|---|---|
| Qatar | QAT | 304382.500 | 2.0839512 | NA | 0.8787640 | 2.832067 | 107477.15 | 0.4216816 |
| Ireland | IRL | 472685.656 | 2.2604477 | 1771.978 | 0.9719849 | 4.882495 | 96812.32 | 0.4299966 |
| China, Macao SAR | MAC | 58838.316 | 0.3878000 | NA | 0.9461432 | 0.640445 | 91870.99 | 2.4397708 |
| Luxembourg | LUX | 56563.883 | 0.4606628 | 1505.559 | 0.9795948 | 0.615729 | 91864.90 | 2.1264897 |
| Singapore | SGP | 484399.906 | 3.7596037 | 2330.166 | 0.9529986 | 5.804337 | 83454.82 | 0.2534838 |
| Switzerland | CHE | 648257.250 | 5.0112047 | 1556.883 | 1.0200546 | 8.591365 | 75454.51 | 0.2035548 |
| Cayman Islands | CYM | 4589.535 | 0.0450873 | NA | NA | 0.064948 | 70664.76 | NA |
| Norway | NOR | 377715.344 | 2.8536618 | 1384.073 | 0.9771795 | 5.378857 | 70222.23 | 0.3424300 |
| United Arab Emirates | ARE | 647986.250 | 5.8088341 | NA | NA | 9.770529 | 66320.49 | NA |
| Brunei Darussalam | BRN | 28401.256 | 0.2223580 | NA | NA | 0.433285 | 65548.67 | NA |
Regions with Highest Productivity Per Worker
top_productivity <- pwt_2019 %>%
arrange(desc(productivity_per_worker)) %>%
head(10)
knitr::kable(top_productivity, caption = "Top 10 Countries by Productivity Per Worker (2019)")| country | countrycode | rgdpna | emp | avh | rtfpna | pop | gdp_per_capita | productivity_per_worker |
|---|---|---|---|---|---|---|---|---|
| Barbados | BRB | 3355.104 | 0.1322586 | NA | 1.0112780 | 0.287025 | 11689.240 | 7.646216 |
| Iceland | ISL | 17635.359 | 0.1923381 | 1454.495 | 1.0406487 | 0.339031 | 52016.952 | 5.410518 |
| Malta | MLT | 17294.842 | 0.2201905 | 1915.417 | 1.0408167 | 0.440372 | 39273.255 | 4.726892 |
| Fiji | FJI | 12043.993 | 0.3082475 | NA | 1.0029286 | 0.889953 | 13533.291 | 3.253647 |
| Eswatini | SWZ | 9667.104 | 0.3125304 | NA | 0.9604759 | 1.148130 | 8419.868 | 3.073224 |
| Cyprus | CYP | 28697.797 | 0.3665672 | 1805.240 | 1.0218076 | 0.868495 | 33043.134 | 2.787504 |
| China, Macao SAR | MAC | 58838.316 | 0.3878000 | NA | 0.9461432 | 0.640445 | 91870.990 | 2.439771 |
| Luxembourg | LUX | 56563.883 | 0.4606628 | 1505.559 | 0.9795948 | 0.615729 | 91864.900 | 2.126490 |
| Mauritius | MUS | 30252.941 | 0.5828115 | NA | 1.0195796 | 1.269668 | 23827.443 | 1.749416 |
| Estonia | EST | 44788.539 | 0.6729755 | 1797.215 | 1.0644550 | 1.325648 | 33786.148 | 1.581714 |
Data Visualization
1. GDP Per Capita Distribution
ggplot(pwt_2019, aes(x = gdp_per_capita)) +
geom_histogram(fill = "steelblue", bins = 30, alpha = 0.8) +
scale_x_continuous(labels = scales::comma) +
labs(
title = "Distribution of GDP Per Capita (2019)",
x = "GDP Per Capita",
y = "Number of Countries"
) +
theme_fivethirtyeight()
2. Top 10 Countries by Productivity Per Worker
ggplot(top_productivity, aes(x = reorder(country, productivity_per_worker), y = productivity_per_worker, fill = country)) +
geom_bar(stat = "identity", show.legend = FALSE) +
coord_flip() +
labs(
title = "Top 10 Countries by Productivity Per Worker (2019)",
x = "Country",
y = "Productivity Per Worker"
) +
theme_minimal()
Findings: Analysis of Productivity Data
The analysis of GDP per capita and productivity per worker across the top-performing countries reveals significant trends:
Top Performers by GDP Per Capita:
The countries with the highest GDP per capita are Qatar, Ireland, and China, Macao SAR. Qatar leads with a GDP per capita of $107,477, followed by Ireland at $96,812, and China, Macao SAR at $91,871. These nations have thriving economies supported by investments in infrastructure, strong financial sectors, and skilled workforces. Notably, China, Macao SAR represents an interesting case where a relatively small region outperforms larger nations in GDP per capita, demonstrating that smaller economies with strategic investments can achieve high economic performance.
Top Performers by Productivity Per Worker:
When examining productivity per worker, countries like Barbados, Iceland, and Malta show the highest productivity rates. Barbados, with a striking productivity per worker value of 7.646, leads the list, followed by Iceland (5.410) and Malta (4.727). These countries demonstrate that smaller economies with strategic investments in technology, skilled labor, and efficient business practices can achieve remarkable productivity levels.
Observations:
While GDP per capita is often an indicator of economic wealth, productivity per worker provides a clearer view of economic efficiency. For instance, Luxembourg and China, Macao SAR appear in both top lists, demonstrating that small, high-income nations with advanced technologies and favorable business climates can achieve both high GDP per capita and high productivity. On the other hand, countries like Mauritius and Eswatini show lower productivity per worker, indicating a need for investment in human capital, innovation, and technology.
Factors Influencing Productivity:
Key drivers behind high productivity include a skilled workforce, technological innovation, effective governance, and investments in infrastructure. Countries like Iceland and Luxembourg have successfully leveraged these factors, contributing to their strong productivity levels. In contrast, nations with lower productivity scores face challenges such as limited access to technology, education, and unstable political environments.
Relationship Between GDP per Capita and Productivity per Worker
Findings: Analysis of Productivity Data
In our analysis of GDP per capita and productivity per worker across top-performing countries, we observe the following:
- Correlation Between GDP per Capita and Productivity per
Worker:
- A correlation coefficient of 0.08 between GDP per capita and productivity per worker indicates a very weak positive relationship. This means that, in this dataset, countries with higher GDP per capita do not necessarily have higher productivity per worker.
- The weak correlation suggests that while both indicators may reflect overall economic well-being, GDP per capita is not a strong predictor of productivity per worker. Other factors, such as investments in technology, education, and infrastructure, could play a larger role in determining productivity levels.
- Visual Representation:
- The scatter plot further illustrates this weak correlation, showing no clear upward or downward trend between the two variables. Countries such as Qatar (high GDP per capita) and Barbados (high productivity per worker) show that a high income does not always correlate with high efficiency or output per worker.
- Implications for Policy:
- Since the correlation is weak, policymakers should not assume that high GDP directly translates into high productivity. Investments in workforce skills, technology, and infrastructure should be prioritized to boost productivity, rather than solely focusing on increasing GDP.
Policy Recommendations
| Policy Area | Evidence | Action |
|---|---|---|
| Invest in Education and Workforce Training | Countries like Iceland and Barbados emphasize education and workforce development, focusing on technical and vocational skills. | Expand technical education and vocational training programs aligned with market needs, similar to South Korea’s focus on technical education leading to productivity gains. |
| Promote Technological Innovation and Digital Transformation | High-productivity countries such as Luxembourg and China, Macao SAR, embrace technology and innovation as key drivers of economic growth. | Incentivize research and development (R&D) and create favorable conditions for startups and tech companies, following Israel’s investment in its tech sector to improve productivity. |
| Improve Infrastructure and Connectivity | Luxembourg and Qatar have high productivity and GDP per capita due to advanced infrastructure facilitating trade, business, and the flow of goods/services. | Invest in transportation, communication, and digital infrastructure to streamline business operations and reduce costs, as seen in China’s infrastructure development. |
| Foster SMEs and Encourage Entrepreneurship | Countries like Mauritius demonstrate the positive impact of fostering small and medium-sized enterprises (SMEs) on productivity and job creation. | Offer incentives like tax breaks, access to finance, and regulatory support to encourage entrepreneurship, similar to India’s support for MSMEs that boosts productivity. |
Conclusion
The analysis of GDP per capita and productivity per worker underscores the importance of both economic wealth and efficiency in driving sustainable growth. While countries like Qatar, Ireland, and China, Macao SAR stand out for their high GDP per capita, countries like Barbados and Iceland demonstrate the potential of smaller nations to achieve high productivity. The intersection of these two metrics highlights the need for nations to invest in education, innovation, infrastructure, and entrepreneurship. By adopting policies that prioritize these areas, nations with lower productivity can catch up and foster a more robust and competitive economy. Through such comprehensive policy interventions, countries can enhance their global standing and economic growth in a sustainable manner.