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Recent improvements in cross-country taxation data

This blog post relates to our entry on taxation, specifically the section discussing data quality.
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Having access to reliable cross-country data on taxation is important because it helps us understand and contextualize the changing landscape of public policy around the world. And yet, while the importance of high-quality data on taxation that is comparable across time and countries is widely recognised, there are substantial deficiencies in the estimates published by traditional sources. In this blog post I want to highlight the efforts of the International Centre for Tax and Development (ICTD) to produce a new dataset that tries to address some of these deficiencies, and that has perhaps not received all the attention it deserves.

Today, the most widely used source of cross-country data on taxation is arguably the IMF Government Finance Statistics (IMF-GFS). Unfortunately, despite IMF efforts, the estimates published in this source are problematic for a number of reasons.

Perhaps the most important limitation of IMF-GFS estimates is coverage: there is extensive missing data, with large spatial and temporal gaps. Yet coverage is not the only limitation. The IMF-GFS estimates are inconsistent with estimates published in other mainstream sources such as the World Bank World Development Indicators (WB-WDI), both because of differences in the methodology used to construct the variables, and because of inconsistencies in the way countries collect and report underlying revenue data.

The following chart, from Prichard (2016), shows an example of data discrepancies in tax revenues for Ghana. The different series correspond to different sources: the blue line denotes estimates using IMF Article IV reports, the orange line denotes estimates from the IMF-GFS, the yellow line denotes data from IMF Country Reports, and the green line denotes estimates from the World Bank World Development Indicators.

As can be seen, research findings and policy conclusions would be quite different depending on the source that one uses to characterize the evolution of tax revenues in Ghana.

Alternative measures of Tax revenue (% GDP), Ghana, 1980-2010 – Prichard (2016)

ghanadata_prichard2016

Considering the above, the International Centre for Tax and Development (ICTD) recently started producing a depurated dataset that combines data from multiple sources (including the OECD, the IMF-GFS and the WB-WDI among others), applying consistency checks and flagging potential problems that may arise with the interpretation of estimates. This is a very valuable resource for research and a clear improvement over the individual underlying sources.

The following visualization maps cross-country estimates of total tax revenues as a share of GDP from the new ICTD Government Revenue Dataset (ICTD-GRD). You can find many other visualizations using this data in our entry on taxation.

One of the key advantages of the ICTD-GRD is that it stipulates a clear hierarchy for underlying sources, country by country. Generally speaking, the ICTD-GRD prioritises sources with (i) longer time series, and (ii) higher levels of disaggregation of sub-categories of revenues, such as social contributions. Yet each case is analysed independently, so the underlying sources are hand-picked, country by country, year by year. This yields fewer missing observations and more consistent estimates across countries and time.

Another important advantage of the ICTD-GRD is that it flags estimates that seem problematic. These flags include remarks such as ‘Data not credible, ‘Data is of Questionable Analytical Comparability’, and ‘Cannot Exclude Resource Revenue from Sub-Components of Total Tax Revenue’. This is evidently convenient for anyone interested in doing econometric analysis: researchers can report results both with and without the flagged data.

Of course, there remains much to be done regarding cross-country data quality in the field of taxation. While the transparent process of manual data cleaning in the ICTD-GRD is clearly a step forward, it is by no means a definitive solution. Misreporting by countries makes it difficult to unambiguously rank sources.

You can read more about how the ICTD-GRD was constructed in Prichard et al. (2014). And you can read more about how this new dataset has been used to re-examine major research questions – such as the relationships between tax and aid, elections, economic growth, and democratization – in Prichard (2016).

How global inequality changed from 2003 to 2013 and what we can expect for the future

This is part of our entry on global income inequality.
We have just updated our entry on income inequality that mostly looks at the distribution within countries.

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The following visualisation shows global income inequality in 2003 and 2013. The data shown here were carefully prepared by Tomas Hellebrandt and Paolo Mauro from the Bank of England and the IMF.

You see the yearly disposable income for all world citizens in both 2003 and 2013. On the x-axis you see the position of an individual in the global distribution of incomes and on the logarithmic y-axis you see the annual disposable income at that position.

The increase in prosperity – and decrease of poverty – is substantial. The income cut-off of the poorest 10% has increased from 260 international-$ to 480 international-% and the median income has almost doubled from 1,100 international-$ to 2,010. Global mean income in 2013 is 5,375 international-$.

The share of the world population living in extreme poverty fell from 25% (in 2002) to 11% in 2013.

The global income distribution in 2003 and 2013

global-inc-distribution-2003-and-2013

Global income inequality is still very high and will stay very high for a long time

The visualisation below presents the same data in the same way except that the y-axis is now not logarithmic but linear. In this perspective the still very high level of global inequality is shown even more clearly.

The previous and the following visualisation show how very high global income inequality still is: The cut-off to the richest 10% of the world in 2013 was 14,500 int-$; the cut-off for the poorest 10% was 480 int-$. The ratio is 30.2.

While global inequality is still very high we are now living in a period of falling global inequality: In 2003 this ratio was 37.6. The Gini coefficient has also fallen from 68.7 to 64.9 over the ten year period.

Taking the historical experience as a guide for what is possible in the future we have to conclude that global inequality will remain high for a long time. To see this we can ask how long it will take for those with incomes at the poorest 10% cutoff to achieve the current incomes of the richest 10% cutoff which is 14,500 international-$. This income level is roughly the level of GDP per capita above which the poverty headcount gets close to 0% for most countries (see here).

How long does it take for incomes to grow from 480$ to 14,500$?

2% growth 172.1 years
4% growth 86.9 years
6% growth 58.5 years
8% growth 44.3 years
10% growth 35.8 years

Even under a very optimistic scenario it will take several decades for the poor to reach the income level of the global top 10%.

2% is roughly the growth rate that the richest countries of today experienced over the last decades (see here). We have seen that poorer countries can achieve faster growth, but we have not seen growth rates of more than 6% over a time frame as long as necessary to reach the level of the global 10% in such a short time. If the past is a good guide for the future the world will very likely be highly unequal for a long time.

The global income distribution in 2003 and 2013

global-inc-distribution-2003-and-2013-linear-scale

New entries on Our World In Data during summer 2016

We have been a bit quiet on the blog, but that doesn’t mean that we did not keep working on Our World In Data.
We worked on a series of updates and added new entries to our web publication. Here are the links to 7 of our latest additions:

  • We have two completely new meta entries: One for health and for education. They both present the most important aspect on these topics and give a first overview so that you can then go and study and read about the particular aspects in more depth in the specific entries.
  • On the same topics we did quite a lot of work to present the empirical research on how these public goods are financed. You find these in the entry on financing health and on financing education.
  • We have also updated and added a series of specific entries: Three that I think are particularly interesting are the entries on trust, literacy, and international trade.

 

 

 

Trust and GDP

[The visualization below and much more on this topic can be found in our entry on Trust]

 

In a much cited article, Arrow (1972) says that “Virtually every commercial transaction has within itself an element of trust, certainly any transaction conducted over a period of time.”

The extent to which trust is linked to economic development has been the subject of many academic papers in the economics literature on growth (see Guiso et al. 2006, Algan and Cahuc 2010, and the references therein). A common way to get a first-order approximation of this relationship is to estimate the correlations between trust and GDP per capita. The visualization below provides evidence of this correlation, by plotting trust estimates from the World Value Survey against GDP per capita. Each dot on this scatter-plot corresponds to a different country. You can learn more about measures of national income in our entry on GDP data.

As it can be seen, there is a very strong positive relationship. Most academic studies find that this relationship remains after controlling for further characteristics. And similar results can also be obtained by looking at other measures of economic outcomes. Looking at outcomes across individuals, Guiso et al. (2006), for instance, report that trust has a positive and statistically significant correlation with the probability of becoming an entrepreneur, even after controlling for education, age and individual income. Their results also hold if religious affiliation of the respondents’ ancestors is used as a proxy for trust – they thus argue that, since ancestors’ religion correlates with respondents’ trust attitudes, this instrumental variable approach can be taken as evidence that the estimated relationship goes in the suggested direction (i.e. that trust leads to entrepreneurship, rather than the other way around).

Other studies using instrumental variables have also found similarly large effects. Algan and Cahuc (2010) predict that, according to their estimates, African countries would have a five-fold increase in GDP per capita if they had the same level of inherited social attitudes as Sweden, after controlling for lagged GDP per capita, contemporaneous political environment and time-invariant country characteristics.

Algan and Cahuc (2010) show that inherited trust of descendants of US immigrants is significantly influenced by the country of origin and the timing of arrival of their forebears. This is their instrumental variable: the inherited trust of descendants of US immigrants is used as a time-varying measure of inherited trust in the country of origin. This approach allows the authors to control for country fixed effects and interpret the effect of trust on growth causally. You can read a summary of their findings and approach in a voxeu.org article written by the researchers.

Nunn and Wantchekon (2011) provide evidence to explain mistrust in Africa: they show that current differences in trust levels within Africa can be traced back to the transatlantic and Indian Ocean slave trades. More specifically, they show that individuals whose ancestors were heavily raided during the slave trade are less trusting today – and using a variety of different econometric strategies, they claim that this relationship is causal.

Trust vs. GDP per capita, 2014 (or latest available data)

Trust-vs-GDP-per-capita