The Economics and Finance Letters
2023 Vol. 10, No. 3, pp. 205-215
ISSNI: 2312-430X
ISSN(p): 2312-6310
DOI: 10.18488/29.v10i3.3429© 2023 Conscientia Beam. All Rights Reserved.

An empirical analysis of premature deindustrialization in latecomer developing countries

Hiroyuki
Taguchi1+
Ryo Kosegawa2

Saitama University, 255 Shimo-Okubo, Sakuraku, Saitama, 338-8570,
Japan.
1,2

Email: htaguchi@mail.saitama-u.ac.jp
Email: kosegawa.r.314@ms.saitama-u.ac.jp

1
2(+ Corresponding author)

ABSTRACT
Article History
Received: 26 April 2023
Revised: 2 June 2023
Accepted: 31 July 2023
Published: 18 August 2023

Keywords
Fixed effect model
Global value chains
Globalization
Latecomer developing countries
Panel data
Premature deindustrialization.

JEL Classification:
F10; O14.

The purpose of this study is to investigate the phenomenon of prematuredeindustrialization among latecomer economies in developing countries across theglobe. This study applies a fixed effect model based on panel data as a methodology.
The main findings and conclusions of this study are as follows: First, the fixed effectmodel based on panel data, which was used as a baseline analysis for looking at the link
between manufacturing and income using the latecomer index, found that developing
economies that joined the global economy after 1990 were deindustrializing too
quickly. Second, from a geographical perspective, the acceleration of prematuredeindustrialization was confirmed in Latin America and some areas of Africa. Third, thequantile regression, which was used to test how stable the results of the fixed effectmodel estimation were, found that developing economies were deindustrializing toosoon. Finally, alternative estimations demonstrated that partaking in global value
chains (GVC) facilitated industrialization, whereas natural resource abundance
prevented it. In terms of practical implications, GVC participation can be a good way
for latecomer in developing economies to avoid premature deindustrialization.
Resource-rich economies could keep the Dutch disease effect from speeding up
premature deindustrialization by putting their resource revenues to productive uses,
like building infrastructure.

Contribution/Originality: This study adds to the existing research by finding early deindustrialization indeveloping countries that joined the global economy after 1990. It does this by using the fixed effect model based on
panel data with a latecomer index, a quantile regression, and alternative estimations that take participation in global
value chains into account.

1. INTRODUCTION
Literature such as Dasgupta and Singh (2007) and Rodrik (2016) defines premature deindustrialization as an
economic phenomenon in which latecomer economies shift into service economies without having experienced afull-fledged industrialization process. Dasgupta and Singh (2007) initially coined the term “prematuredeindustrialization.” The study's primary emphasis was on employment rather than output, asserting that thedecrease in manufacturing employment should not be automatically regarded as a negative occurrence. They
highlighted that India and East Asian nations have experienced economic growth through the adoption of205© 2023 Conscientia Beam. All Rights Reserved.

The Economics and Finance Letters, 2023, 10(3): 205-215

information technology and knowledge-based innovation, while Latin American and African countries have faced
detrimental consequences due to deindustrialization resulting from import substitution strategies.
Rodrik (2016) pointed out that premature deindustrialization refers to the early shrinking of manufacturing interms of not only employment but also output in developing countries. This author built a simple two-sector
theoretical model with manufacturing and non-manufacturing sectors and showed that developing countries thatare price-takers in the world markets for manufacturing and who lack a strong comparative advantage inmanufacturing tend to be net importers of manufactured products, thereby leading to premature deindustrialization.
The author also provided the following empirical evidence: Late industrializers experience lower peak levels of
industrialization than early industrializers at lower income levels, and countries in Latin America and sub-SaharanAfrica have unquestionably suffered from premature deindustrialization, whereas Asian countries, as a group withcomparative advantages in manufacturing, have managed to avoid it.
Since the seminal work of Rodrik (2016), numerous empirical studies have been conducted to identify theexistence of premature deindustrialization in multiple and specific countries, including the following: Sato andKuwamori (2019) in Organization for Economic Co-operation and Development non-OECD countries; Nayyar,
Cruz, and Zhu (2021) in lower-income developing countries; Daymard (2020) in Latin American and Africancountries; Caldentey and Vernengo (2021) in Latin American countries; Ssozi and Howard (2018) in Sub-SaharanAfrican countries; Taguchi and Tsukada (2022) in Asian latecomer economies; Lee (2020) in Malaysia; and Hamid
and Khan (2015) in Pakistan.
Most of these previous empirical studies have concentrated on the comparison of industrialization peaks
between forerunner and latecomer economies, reporting that lower peaks with lower incomes in latecomers indicate
premature deindustrialization. However, while latecomers face a high probability of falling into prematuredeindustrialization, not all latecomers necessarily reach their industrialization peaks. In this context, Taguchi andTsukada (2022) focused on Asian latecomers in developing economies and adopted the “latecomer index” forexamining the positions of the manufacturing-income nexus. The latecomer index facilitates the identification of
downward shifts in latecomers’ manufacturing-income nexus, regardless of the existence of an industrialization
peak. Even for a latecomer that has not reached its peak, its downward shift suggests an upcoming peak-out at alower manufacturing share in a lower income stage, implying a symptom of premature deindustrialization.
By using the latecomer index, specifically the extension suggested by Taguchi and Tsukada (2022), this study
seeks to identify the existence of premature deindustrialization in all developing countries (110 countries)
worldwide from 1980 to 2020. The study is structured as follows: First, we estimate a fixed effect model in thepanel setting as a baseline analysis to examine the manufacturing-income nexus with the latecomer index. Second,
we examine the regional heterogeneity of premature deindustrialization by incorporating the cross-terms of thelatecomer index and regional dummies in the fixed effect model. Third, we check the robustness of the fixed effectmodel estimation results using quantile regression, which is an alternative approach for allowing the complete
conditional distribution of dependent variables over different years and countries. Fourth, to propose policy
directions for mitigating and avoiding premature deindustrialization, we conduct alternative estimations
considering country participation in global value chains (GVCs) and natural resource abundance. Finally, wesummarize the study and conclude the paper.

2. BASELINE ESTIMATIONThis section presents a baseline estimation using the fixed effect model in the panel setting. Regarding thespecification, we use the equation with the inverted U-shaped manufacturing-income nexus that Rodrik (2016)
proposed, which accounts for the impact of demographic and income trends with their quadratic terms. But thisstudy changes the Rodrik specification by using the latecomer index (Taguchi & Tsukada, 2022), which shows how

206© 2023 Conscientia Beam. All Rights Reserved.

The Economics and Finance Letters, 2023, 10(3): 205-215

the relationship between manufacturing and income changes in a latecomer economy and proves thatdeindustrialization happened too soon.
The latecomer index indicates the degree of development lateness, computed as the ratio of the gross domestic
product (GDP) per capita of a latecomer economy in a particular year relative to that of a benchmark economy inthat year. China is chosen as the benchmark economy because it has become a global manufacturing center, as
described in prior research (Sung, 2007), and a top runner in manufacturing-output ratios among developing
economies. In Figure 1, the latecomer index in year t is shown by the GDP per capita of economy A (Xat) divided
by that of China (Xct). If the index (Xat / Xct) is linked to a lower manufacturing-output ratio, economy A’s
manufacturing-output curve is positioned downward from China’s curve, as shown in Figure 1.

Figure 1. Analytical framework of premature deindustrialization.

This implies the existence of premature deindustrialization in the latecomer economy because the downward
position of the manufacturing-income curve (vs. China’s curve) suggests a peak-out or an upcoming peak-out at alower manufacturing-output ratio in a lower income stage. The equation for the baseline estimation is as follows.

(1)
The definition and data source of the variables mar, pop, ypc, and lac (gvc and nrr in Section 5) are described inTable 1. The subscripts i and t denote countries (110 developing economies) and years (1980–2020), respectively.
d90 shows the time dummy for 1990–2020, and fi denotes a time-invariant, country-specific fixed effect. ε is a
residual error term. γ0…4 and φ0…2 are the estimated coefficients. “ln” represents a logarithmic form. The
explanatory variables in Equation 1 are lagged by one year for the purpose of helping avoid reverse causality due tothe endogenous interactions between the dependent and independent variables. The logarithmic forms of pop andypc are used to avoid scaling issues based on population size and real GDP per capita. The descriptive statistics forthe variables, including those for the estimation in Section 5, are displayed in Tables 2. This study builds a set ofpanel data for 110 sample countries for the period 1980–2020.

207© 2023 Conscientia Beam. All Rights Reserved.

The Economics and Finance Letters, 2023, 10(3): 205-215
Table 1. List of variables, definitions, and sources.

Var.
Dependent variablemar

Description

Sources

Manufacturing in US dollars at constant prices (2015), percentage
of gross domestic product (GDP)UNCTAD
Stat

Explanatory variables
pop
Population in thousands, logarithmic term, one-year lagged
GDP in US dollars at constant prices (2015) per capita, logarithmic
ypc
term, one-year lagged
Latecomer index: a ratio of the GDP per capita of an economy tolacthat of a benchmark country (China) in a certain year, one-year
laggedGlobal value chains (GVC) indicator: GVC values divided by gross
gvc
export values, one-year lagged
nrr
Total natural resource rents, percentage of GDP, one-year lagged

UNCTADStat

UNCTAD-Eora
World bank

Note: UNCTAD Stat: https://unctadstat.unctad.org/EN/
UNCTAD-Eora: UNCTAD-Eora Global Value Chain Database (https://worldmrio.com/unctadgvc/).
World Bank: World Bank Open Data (https://data.worldbank.org/).
Table 2. Descriptive statistics.

Variables
Dependent variablemar
Explanatory variables
pop
ypc
lac
gvc
nrr

Obs.

MedianStd. dev.

Min.

Max.

4,510

12.590

7.227

0.010

70.790

4,510
4,510
4,510
2,581
3,248

9.014
7.546
0.850
0.434
4.071

2.165
1.068
3.166
0.109
10.696

2.079
4.564
0.010
0.180
0.000

14.17010.03552.9800.942
67.890To ensure a more thorough description of Equation 1, the following notes on its specifications are required:
The latecomer index (lac) is the most critical variable for identifying premature deindustrialization. A significant
positive value of φ, which refers to the linkage between a country’s delayed development and its lower
manufacturing-output ratio and represents the downward shift of the country’s manufacturing-income curve, cansubstantiate the occurrence of premature deindustrialization. Premature deindustrialization is explicitly triggered
by globalization trends in manufacturing markets, as Rodrik (2016) argued (see the Introduction). This cited author
regarded the post-1990 period as the period in which globalization gained momentum. Thus, the equation contains
the cross-term lac and the time dummy for 1990–2020 (d90).
Concerning the control variables for the trends in population size (pop) and real GDP per capita (ypc), theinverted U-shaped relationships between the manufacturing-output ratio (mar) and these control variables are
identified in the case that γ1, γ3 > 0 and γ2, γ4 < 0 are significant. This study includes the time-invariant countryspecific fixed effect (fi) as it considers the existence of exogenous country-specific factors (e.g., geography,
endowments, and history) that vary among sample countries and correlate with mar. Adopting the fixed effectmodel contributes to mitigating the endogeneity problem by absorbing unobserved heterogeneity among countries.
For the subsequent estimation, the stationary property of the constructed panel data is examined by applying
panel unit root tests: the Levin, Lin, and Chu test (Levin, Lin, & Chu, 2002) as a common unit root test; the FisherADF and Fisher-PP tests (Choi, 2001; Maddala & Wu, 1999); and the Im, Pesaran, and Shin (2003) as individual
unit root tests. We conduct these tests following the null hypothesis that a series of panel data in levels has a unit
root by inserting the “individual intercept” and “individual intercept and trend” in the test equations. Table 3
demonstrates that the Levin, Lin, and Chu test rejects the null hypothesis of a unit root at the significance level for
all variables in both test equations. The individual unit root tests do not necessarily reject the null hypothesis in allcases. However, the Fisher-PP test, with the individual intercept and trend, rejects the null hypothesis at the208© 2023 Conscientia Beam. All Rights Reserved.

The Economics and Finance Letters, 2023, 10(3): 205-215

significant level for all variables. Thus, there seems to be no serious problem with the existence of unit roots in thepanel data, and it allows us to utilize the panel data in levels for subsequent estimations.
Table 3. Panel unit root tests results.

Tests
Mar
Individual intercept
Levin, Lin & Chu-2.867 ***Fisher ADF
289.2 ***
Fisher PP
274.9 ***Im, Pesaran & Shin-0.387
Individual intercept and trend
Levin, Lin & Chu-4.584 ***Fisher ADF
306.6 ***
Fisher PP
282.2 ***Im, Pesaran & Shin
-1.773 **

Pop

Ypc

Lac

Gvc

Nrr

-7.684 ***426.7 ***
1,394.4 ***-1.641 *

1.411
192.3
159.4
6.101

-43.088 ***
1,676.9 ***2,523.4 ***-35.982 ***-11.020 ***195.4
328.4 ***
-1.922 **-14.223 ***499.9 ***
509.3 ***
-12.464 ***-6.183 ***394.9 ***
312.7 ***
-0.606-5.285 ***279.9 ***
248.8 *
-0.124-26.609 ***840.5 ***
1,732.4 ***-14.841 ***-11.281 ***153.3
224.5 **
3.863

-12.727 ***635.3 ***
698.6 ***
-8.244 ***Note: *, **, and *** denote the rejection of the null hypothesis at the 90%, 95%, and 99% levels of significance, respectively.

Table 4. Baseline estimation results.

Estimationln popln (pop)2
ln ypcln (ypc)2a4.598 ***
(30.682)-0.349 ***(-29.876)
18.948 ***(34.269)-1.154 ***(-32.431)b
4.092 ***
(27.740)-0.323 ***(-28.646)
18.428 ***(33.574)-1.117 ***(-31.184)-0.012 **(-2.039)

3,682

3,819

c
3.996 ***
(25.791)-0.318 ***(-28.189)
18.218 ***(33.791)-1.102 ***(-31.607)-0.003(-0.492)
0.071 ***
(4.416)
3,885

Yes
110
4,510

Yes
110
4,510

Yes
110
4,510

lac
lac * d09
Turning point of(USD)
Country fixed effectNo. of countries
No. of observations

ypc

Note: * ** and *** denote the rejection of the null hypothesis at the 90%, 95% and 99% levels ofsignificance, respectively. T-statistics are shown in parentheses.

Table 4 reports the results of the baseline estimation. Across all estimation results from columns (a) to (c)(including those in Tables 5, 6, and 7 in columns d–m), γ1, γ3 > 0 and γ2, γ4 < 0 hold significantly, demonstrating an
inverted U-shaped relationship between a country’s manufacturing-output ratio and its control variables(population size and real GDP per capita). The turning points, computed using –γ3/2γ4 in Equation 1, fell within
the reasonable ranges of real GDP per capita, namely between 3,682 and 3,885 USD. The main research focus inthis study was, however, the position of a country’s manufacturing-income curve, not its shape.
Estimation results in column (b) show that lac had a negative coefficient, while those in column (c) show that lac
had an insignificant coefficient and the cross-term, lac*d90, had a significant positive coefficient. This positivecoefficient represents the downward position of the latecomer’s manufacturing-income curve during the post-1990
period, suggesting that globalization in manufacturing markets has caused the premature deindustrialization oflatecomers. This result is consistent with those of the study in Rodrik (2016).

3. REGIONAL ESTIMATION
By incorporating the regional dummies and latecomer index as cross-terms into the fixed effect model, we also
examine how premature deindustrialization affects various regions. The model is specified as follows:209© 2023 Conscientia Beam. All Rights Reserved.

The Economics and Finance Letters, 2023, 10(3): 205-215(2)Equation 2 adds an additional cross-term that includes the regional dummy, darea, to Equation 1. A significant
positive value of φ3 represents the additional region-specific effect of premature deindustrialization underglobalization in that region. The regional dummy comprises four types, as described herein: dummies for Africancountries (dafri), East African countries (dafri_e), Asian countries (dasia), and Latin American countries (dlame).
Country classification is shown i Appendix 1.1
Table 5 presents the results of the regional estimations. When looking at the cross-terms with regionaldummies, the coefficient of the African dummy in column (d) is significantly negative, which cancels out the positiveeffect of premature deindustrialization on the world as a whole. However, the coefficient of the East African dummyin column (e) is significantly positive, accelerating the worldwide effect of premature deindustrialization. The
coefficient of the Asian dummy, in column (f), is positive and weakly significant, and that of the Latin American
dummy, in column (g), is positive and highly significant. The findings on the acceleration of prematuredeindustrialization in Latin America and some areas of Africa are in line with those of the studies by Dasgupta andSingh (2007), Rodrik (2016), Daymard (2020), Caldentey and Vernengo (2021), and Ssozi and Howard (2018).
Meanwhile, the weak acceleration of premature deindustrialization in Asia seems to reflect the heterogeneity of thecountries in the region, with Taguchi and Tsukada (2022) having previously argued that the risk of prematuredeindustrialization is larger for South Asian countries than for Southeast Asian countries.
Table 5. Estimation results with regional dummies.

Estimationln popln (pop)2
ln ypcln (ypc)2
lac
lac * d09
dafri * lac * d90

d3.977 ***
(25.879)-0.316 ***(-28.019)
18.228 ***(35.959)-1.104 ***(-34.225)-0.002(-0.320)
0.093 ***
(5.062)-0.083 ***(-0.083)

dafri_e * lac * d90e4.015 ***
(25.199)-0.318 ***(-24.233)
18.192 ***(33.706)-1.101 ***(-31.429)-0.004(-0.579)
0.059 ***
(3.982)f
3.928 ***
(21.499)-0.314 ***(-29.190)
18.339 ***(33.575)-1.112 ***(-31.315)-0.003(-0.484)0.049 ***
(3.997)0.804 ***
(4.909)dasia * lac * d90

0.189 *
(1.835)dlame * lac * d90
Country fixed effectNo. of countries
No. of observationsg4.051 ***
(25.884)-0.321 ***(-29.223)
18.298 ***(35.202)-1.107 ***(-32.992)-0.007(-1.058)
0.021(0.786)

Yes
110
4,510

Yes
110
4,510

Yes
110
4,510

0.092 ***
(2.901)
Yes
110
4,510

Note: *** and * denote rejection of the null hypothesis at the 99% and 90% levels of significance, respectively, in the coefficients. T-statistics areshown in parentheses.

1

The estimation for Oceanian countries is excluded because the samples are quite limited.

210© 2023 Conscientia Beam. All Rights Reserved.

The Economics and Finance Letters, 2023, 10(3): 205-215

4. QUANTILE REGRESSION
In this section, we report the estimation using quantile regression, which serves to check the robustness of thefindings using the fixed effect model. Most regression models are concerned with analyzing the conditional ‘mean’of a dependent variable. Meanwhile, the quantile regression, originally proposed by Koenker and Bassett (1978),models the quantile of the dependent variable given a set of conditioning variables by describing how the median(or quantile) of the response variable is affected by regressor variables. This method is robust because its approachis less sensitive to outliers and heteroscedastic residuals, so it does not require a strong distribution assumption(e.g., (Buchinsky, 1998; Chang, Wen, Dong, & Hao, 2018)). Equation 3 presents the quantile regression as follows:(3)
The quantiles are set at three levels: ζ = 25th, 50th, and 75th. Table 6 presents the estimation outcomes for eachquantile. In the 25th quantile shown in column (h), the coefficients of lac and lac*d90 are insignificant. Regarding the50th and 75th quantiles demonstrated in columns (i) and (j), they are significantly positive, with those of the crossterm accelerating the positiveness. This suggests that, in the countries in our sample, a progressed stage ofindustrialization allows for premature deindustrialization to be evidently identified, while an earlier stage ofindustrialization makes deindustrialization less obvious. Thus, the quantile regression model confirms the existenceof premature deindustrialization in the sampled economies.
Table 6. Quantile regression results.

Estimation
Quantile levels
ln popln (pop)2
ln ypcln (ypc)2
lac
lac * d09
No. of countries
No. of observationsh25th
1.552 ***
(13.045)-0.015 **(-2.160)
14.413 ***(11.789)-0.906 ***(-11.090)-0.022(-0.266)
0.116(0.812
110
4,510

i
50th
3.492 ***
(28.946)-0.128 ***(-16.228)
17.502 ***(20.757)-1.086 ***(-17.341)
0.169 ***
(6.327)0.253 ***
(3.999)
110
4,510

j
75th
3.616 ***
(18.527)-0.125 ***(-10.521)
20.490 ***(17.269)-1.306 ***(-16.360)
0.155 ***
(4.662)0.207 ***
(2.805)110
4,510

Note: *, ** and *** denote the rejection of the null hypothesis at the 90%, 95% and 99% levels ofsignificance, respectively. T-statistics are shown in parentheses.

5. ALTERNATIVE ESTIMATIONS FOR PROPOSING POLICY DIRECTIONS
Lastly, this section shows the results of the alternative estimations, which took into account participation in
GVCs and the availability of natural resources. These results can be used to suggest policy directions to slow down
or stop premature deindustrialization.
GVCs have dominated global economic activities over the past few decades, and their production networks
have typically revolved around manufacturing activities (Kimura, 2006; Kimura, Takahashi, & Hayakawa, 2007).
GVCs facilitate specialization in production processes among countries and relieve a single country fromperforming all processes of production, thereby enhancing efficiency and productivity and promoting the diffusionof technology along the chains (World Bank, 2020). Thus, the absence of GVC participation leads to sluggish
manufacturing.
211© 2023 Conscientia Beam. All Rights Reserved.

The Economics and Finance Letters, 2023, 10(3): 205-215

Another dimension of deindustrialization issues is the nexus with Dutch disease in resource-rich economies.
The disease was coined by the Economist in a November 1977 issue and was inspired by the deindustrialization
process related to natural gas discoveries by the Netherlands in the late 1950s. Corden and Neary (1982) provided
the theoretical basis for this phenomenon, and many quantitative studies have verified the existence of Dutch
Disease in resource-rich economies (e.g., Sachs and Warner (2001)). The alternative estimation model is as follows:(4)
Equation 4 replaces the latecomer index (lac) in Equation 1 with the GVC indicator (gvc) and natural resource
rents (nrr; the data description, statistics, and properties are shown in Tables 1–3). Columns (k) to (m) in Table 7
show the estimation results, wherein the coefficient of gvc, η1, is significantly positive and that of nrr, η2, issignificantly negative. This suggests that GVC participation facilitates industrialization, whereas natural resource
abundance prevents it.
Table 7. Estimation results while considering GVC participation and natural resource rent.

Estimationln popln (pop)2
ln ypcln (ypc)2
gvc
dnrr
Country fixed effectNo. of Countries
No. of Observations

k-0.458(-1.107)-0.128 ***(-5.749)
20.360 ***(27.905)-1.275 ***(-23.635)
1.361 ***
(3.330)
Yes
89
2,581

l
5.355 ***
(24.660)-0.392 ***(-31.206)
20.854 ***(35.631)-1.266 ***(-33.613)-0.026 ***(-5.045)
Yes
108
4,179

m
0.890 *
(1.914)-0.212 ***(-8.393)23.831 ***(24.926)-1.504 ***(-21.662)
2.765 ***
(6.789)-0.025 ***(-4.038)
Yes
88
2,481

Note: * and ***denotes rejection of the null hypothesis at the 90% and 99% level of significance. Tstatistics are shown in parentheses.

These outcomes point towards the following policy implications for mitigating and avoiding the prematuredeindustrialization verified in Sections 2–4. First, GVC participation can be a viable policy for mitigating prematuredeindustrialization in latecomer developing economies. Numerous reports by international organizations (United
Nations Conference on Trade and Development, 2013; World Bank., 2020) have recommended countries develop
GVC participation strategies, such as strategies related to infrastructure and human resource development,
institutional improvements, and policy frameworks to create industrial clusters and networks. Second, for resourcerich economies, the Dutch disease effect may accelerate premature deindustrialization. To offset the disease effect,
resource revenues should be mobilized for productive uses, such as infrastructure development, to activate
manufacturing activities (e.g., (Coutinho, 2011; Sachs, 2007)).

6. CONCLUDING REMARKSThis study examined whether latecomer developing countries worldwide have experienced prematuredeindustrialization. The main findings of this study are as follows: First, the fixed effect model in the panel setting,as a baseline analysis for examining the manufacturing-income nexus using the latecomer index, identified theexistence of premature deindustrialization in latecomer developing economies under globalization in the post-1990
212© 2023 Conscientia Beam. All Rights Reserved.

The Economics and Finance Letters, 2023, 10(3): 205-215

period. Second, from a geographical perspective, the acceleration of premature deindustrialization was confirmed in
Latin America and some areas of Africa. Third, the quantile regression, used for checking the robustness of the
fixed effect model estimation findings, also supported the existence of premature deindustrialization. Finally,
alternative estimations showed that GVC participation facilitated industrialization, whereas natural resource
abundance prevented it.
The policy implications of this study are that GVC participation can be a viable policy to mitigate prematuredeindustrialization in latecomer developing economies. Furthermore, resource-rich economies should mobilize theirresource revenues for productive uses, such as allocating them to infrastructure development, in order to prevent
the Dutch disease effect from accelerating premature deindustrialization.
A limitation of this study is the lack of detailed research on individual countries. By doing detailed case studies
to look at the complexity of early deindustrialization mechanisms and how policies work in different countries, itwould be possible to come up with country-specific and more concrete recommendations and prescriptions forpreventing and stopping early deindustrialization.
Funding: This study received no specific financial support.
Institutional Review Board Statement: Not applicable.
Transparency: The authors state that the manuscript is honest, truthful, and transparent, that no key
aspects of the investigation have been omitted, and that any differences from the study as planned have beenclarified. This study followed all writing ethics.
Data Availability Statement: The corresponding author can provide the supporting data of this study
upon a reasonable request.
Competing Interests: The authors declare that they have no competing interests.
Authors’ Contributions: Both authors contributed equally to the conception and design of the study. Bothauthors have read and agreed to the published version of the manuscript.

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Appendix 1. Country classification (110 countries and 4 areas).

AfricaAlgeria
AngolaBenin
Botswana
Burkina Faso
Burundi *Cabo Verde
Cameroon
Central African
Chad
Comoros *Congo
Congo, Dem. Rep.
Côte d'Ivoire
Djibouti *Egypt
Equatorial Guinea
Eswatini

Latin America
Argentina
BelizeBolivia
BrazilColombia
Costa Rica
Cuba
Dominica
Dominican Republic
EcuadorEl Salvador
GrenadaGuatemala
GuyanaHaiti
Honduras
Jamaica
MexicoAsia
Afghanistan
Bangladesh
BhutanCambodia
China
India
Indonesia
Iran
Iraq
JordanKorea, Dem.
Lao People's Dem. Rep.
Lebanon
Malaysia
Maldives
Mongolia
Myanmar
Nepal

OceaniaFiji
Nauru
Papua New Guinea
Samoa
Solomon Islands
Tonga
TuvaluVanuatu214© 2023 Conscientia Beam. All Rights Reserved.

The Economics and Finance Letters, 2023, 10(3): 205-215

Ethiopia *Gabon
GambiaGhana
GuineaGuinea-Bissau
Kenya *
LesothoLiberia
Libya
Madagascar *Malawi *Mali
Mauritania
Mauritius *MoroccoMozambique *Namibia
Niger
Nigeria
Rwanda *Sao Tome and Principe
SenegalSierra Leone
Somalia *South Africa
Tanzania *Togo
Tunisia
Uganda *Zambia *Zimbabwe *Nicaragua
PanamaParaguay
Peru
Saint Lucia
Saint Vincent and the Grenadines
Suriname
Venezuela

Pakistan
Philippines
Sri Lanka
State of Palestine
Syrian Arab Republic
ThailandTurkeyViet Nam

Note: * Represents East Africa.
Sources: UNCTAD Stat.

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