Income per-capita across-countries: stories of catching-up, stagnation, and laggardness

ABSTRACT

The convergence/divergence debate and plausible explanations to the process of catching-up remain highly controversial research areas in growth economics. Recently, these issues have been the subject of questionable predictions with regard future prospect for backward countries, raising concerns about the right direction of macroeconomic policy. To explore these issues, a sample of 131 countries is studied over the 1950s–2010s to identify those that have managed to catching-up, remain stagnant, or keep lagging further behind. Time-distance to the frontier and productivity decompositions, based on non-parametric methods, suggest that some countries have successfully completed already the caching-up, and it would take between 27 and 194 years for others to do so in the most optimistic scenario. But many others would never do. Policy implications drawn from the comparative analysis suggests the need to strengthen local innovation in order to increase the ability to catching-up alongside widespread reliance on technology diffusion from abroad.

KEYWORDS:

• Economic development and economic growth
• innovation
• technology diffusion
• convergence

Acknowledgments

The author greatly acknowledges comments received from the editor of this journal and two anonymous referees on a previous version of the paper. This research has also benefited from the comments and extensive discussion with Thomas Ziesemer and Bart Verspagen at Maastricht University and the United Nations University – Maastricht Economic and Social Research Institute on Innovation and Technology, UNU-MERIT. Usual Disclaimers apply.

Disclosure statement

No potential conflict of interest was reported by the author.

Supplemental material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15140326.2024.2339701

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Notes

¹ See https://www.rug.nl/ggdc/productivity/pwt. S. Johnson et al. (2013) show that specific results vary using alternative datasets. Patel et al. (2021), indicate that while PWT tends to favour divergence in earlier decades it does not invalidate convergence patterns in most recent data.

² I use PWT V.10.0 output-side real GDP at chained PPPs (in mil. 2017US$)–rgdpo). Rich oil-producing countries, non-OECD high-income countries with relative income per-capita larger than 75% of the frontier at the origin, and countries with data over less than two decades, are excluded.

³ I consider i) a cluster of 35 countries with data over the 1950s-2010s; ii) a cluster of 49 countries with data over 1960s-2010s; and iii) a cluster of 23 countries with data over 1970s-2010s.

⁴ Decade averages are calculated as long as there is data for at least 8 years.

⁵ https://www.oecd.org. Using this benchmark rather than a single country (like the U.S.) has the advantage that the OECD represents a wider variety of capitalism and institutional practices (See Hall & Soskice, 2004).

⁶ See the Appendix.

⁷ Most Low Income Countries (LICs) and many countries characterized as having weak institutions and poor governance, so-called fragile states (Frags), classify as LGCs. Likewise, all of the so-called New Industrialized Asian Economies-NICs (Hong Kong, Singapore, Taiwan, South Korea), and most of the High Income Countries that are not part of the OECD (HInOECDs) classify as CUCS. To prevent a misleading influence on long-run trajectories, in Figure 2 these specific country cases are depicted apart.

⁸ A gap of 1 pp for a country that has half the FRC’s income indicates that catch-up will take 70 years; but with a gap of 0.1 pp it will take 700 years.

⁹ Countries as Burundi or Algeria would need more that 300 years to catching-up. But Central African Republic, Djibouti, and Congo would not be able to catching-up at all. As a rule of thumb, countries falling behind far beyond 300 years are plotted at −300 (these countries are even behind the starting gate of modern economic growth initiated with the onset of the industrial revolution – see Lucas, 2000).

¹⁰ Interestingly, the distance to the frontier, and indeed the decline in relative income of the three Latin American countries in the latter group between the 1950s and the 2010s (Colombia from 38% to 28%, Costa Rica from 46% to 33%, and Mexico from 59% to 38%) is in strong contrast with the optimism on their economic performance in other country classifications. For instance, these countries belong to the World Bank UMICs: Mexico since 1990, Costa Rica since 2000, and Colombia since 2008, and all of them are among the latest members of the OECDs: Mexico 1994, Colombia 2020, and Costa Rica 2021).

¹¹ Alternatively, a “conditional convergence” test can be run based on the fact that countries belong to distinct convergence clubs.

¹² Calculations are available from the author.

¹³ See Dougherty and Jorgenson (1996), and Jorgenson and Vu (2005) for counter arguments.

¹⁴ $\mathit{\alpha }=1/3$ is a standard value in growth literature. Alternatively, one may use country specific and time varying statistics on factors shares. However, Gollin (2002), and Pritchett (2000a), provide solid arguments to be cautious on the reliability of official statistics in this regard.

¹⁵ I use the following data from PWT V.10.0: Output-side real GDP (cgdpo) and capital stock (cn) at current PPPs in millions of 2017US$, number of persons engaged (emp) and the human capital index (h). The smaller sample size is because some countries in the original sample of 131 countries lack the data on factor inputs that is needed in this section.

¹⁶ The Z-mean test is runned for every pair of country classifications, e.g., for the FRCs and STCs, the null hypothesis is.

$\mathit{Ho}:{\mu }_{\mathit{FRCs}}-{\mu }_{\mathit{STCs}}=0$

which is distributed at the 99% as follows:

$(\mathit{\mu },{\sigma }^2,\mathit{N}{)}_{\mathit{FRCs}}=(0.76,0.01,50)\mathit{and}(\mathit{\mu },{\sigma }^2,\mathit{N}{)}_{\mathit{STCs}}=(0.64,0.01,50)$

Thus, $(0.76,0.01,50{)}_{\mathit{FRCs}}$-$(0.64,0.01,50{)}_{\mathit{STCs}}$ = −6.55 (P $<|\mathit{z}|$ = 2.92E–11).

¹⁷ Fare et al. (1994) associate changes in efficiency to catching-up and technical changes to innovation instead. The different use of the terms is because they think of “innovation” as technology produced at the frontier, and “catching-up” as local efforts to reach the frontier.

¹⁸ I have forced the model to use only FRCs as the benchmark. But this approach leads to minor variation in the results.

¹⁹ In this case, the linear program must be calculated for each country and time period, e.g., if there are N countries and T time periods a total of N$\times$(3T–2) programs need to be calculated.

²⁰ In spite that PWT V.10 report statistics for ctfp using USA as the benchmark and country specific factor shares, I rely on the average across FRCs as the benchmark and constant factor shares.

²¹ Notice that when the focus is on the overall mean of the conventional measure of productivity, $\overline{\Delta \mathit{ctfp}}$, the CUCs are even more successful than the FRCs.

Additional information

Funding

The work was supported by the Universiteit Maastricht.