# Manufacturing and the real exchange rate: natural resource rents matter when measuring misalignments

Mohamed E Chaffai, Patrick Plane

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## To cite this version:

Mohamed E Chaffai, Patrick Plane. Manufacturing and the real exchange rate: natural resource rents matter when measuring misalignments. Applied Economics, 2024, 10.1080/00036846.2024.2387859. hal-04845262

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Submitted on 18 Dec 2024

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# Manufacturing and the real exchange rate: natural resource rents matter when measuring misalignments

Mohamed E. Chaffai,
University of Sfax,
Tunisia

Patrick Plane*
CERDI-CNRS, Université
Clermont Auvergne, France

May, 2024

Declarations of interest: none

## Abstract

We analyze the relationship between the share of manufacturing in GDP and real exchange rate misalignments based on the purchasing power parity criterion (PPP) and a sample of 102 developing and transition economies (2003-2019). In a departure from usual practice, we subtract natural resource rents from GDP in order to correct misalignments for the productivity bias. A dynamic threshold panel model is used and we separate out the impact of undervaluation and overvaluation components in the same regression. Overvaluation has a negative linear effect, while undervaluation stimulates the manufacturing sector in a non-linear way. Above an 18% threshold, the marginal effect of undervaluation diminishes.

**Keywords:** Real exchange rate, misalignment, threshold model, overvaluation, undervaluation, manufacturing sector

**JEL Codes:** F63, O14, O50, O25.

<footnote>* Corresponding author. Email: patrick.plane@uca.fr. Postal address: 26 Avenue Léon-Blum, 6300 Clermont-Ferrand.</footnote>
# I. Introduction

Premature deindustrialization is a major concern for many aspiring emerging countries (Rodrik, 2016, Cadot et al., 2016). First, employing a fast-growing workforce is a challenge, and jobs that improve living standards are in manufacturing and some related tertiary activities (de Vries et al., 2015). Second, in the international catching-up process, manufacturing is a source of absolute convergence, whereas convergence in other sectors is predetermined by the national economic and institutional environment (Rodrik, 2013). Third, the manufacturing sector disseminates technical progress more easily through backward and forward interactions (McMillan et al., 2014; Rodrik, 2009). Last but not least, the income elasticity of demand is higher for manufacturing than for primary products (Prebisch, 1950). These arguments justify focusing on this deindustrialization movement, and more specifically on the role that exchange rates can play in breaking it in developing and transition economies.

Real exchange rate adjustments provide an incentive to stimulate the production of tradable goods, and can be an effective policy instrument for the realignment of the manufacturing/GDP ratio with that posted by high-income countries during their own development process. Recent literature suggests that while overvaluation penalizes the production of tradables, a deliberately undervalued exchange rate could improve welfare and prospects for structural change. This latter prediction applies in particular to developing and transition economies suffering from both market failures and institutional weaknesses. (Rodrik, 1986, 2008; Guzman et al., 2018). However, there is no broad consensus regarding the effectiveness of this variable, and no clear-cut result regarding the presence of an asymmetry and/or linearity effect of misalignments in the explanation of economic performance.

We explore the role played by real exchange rate misalignments in the share of manufacturing value-added in GDP by testing asymmetric and/or non-linear impacts. An unbalanced panel of 102 countries is considered over the 2003-2019 period (see Appendix 1). Our contribution to the literature
is twofold: in the way we model the share of manufacturing and in the measurement of real exchange
rate misalignments. First, the non-linearity of misalignments is investigated using an estimation method
based on an extension of the dynamic panel threshold model developed by Kremer et al. (2013). Our
methodological approach is original in terms of the possibility of having separate currency
undervaluation and overvaluation thresholds in the same regression. We also adapt the Hansen (1999)
non-standard bootstrapping test to investigate the presence of non-linearities and their impact.
Threshold effects are then estimated in anticipation of the detection of non-monotonic phenomena for
the components of real exchange rate misalignment. Second, we take the absolute purchasing power
parity (PPP) criterion to calculate misalignments taking into account the misleading impact of natural
resource rents from oil, natural gas, coal, mineral and forest products. Rents give rise to the
overestimation of average domestic productivity as proxied by per capita GDP. This overestimation
distorts the measurement of misalignments adjusted for the Balassa Samuelson productivity bias and
thereby incentives to produce manufactured goods.

Our estimation of dynamic panel threshold regressions determines the influence of
misalignment components, i.e. the impact of undervaluation (*Under*) and overvaluation (*Over*) on the
ratio of manufacturing value-added to GDP. The effect of these respective variables is controlled for a
set of covariates. The empirical results confirm the presence of asymmetries. *Over* is a major obstacle
to an increase in the share of manufacturing while *Under* is a driver, but in a non-linear way. Above
the 18% threshold, the marginal coefficient of *Under* is almost halved. Results prove robust to an
alternative definition of exchange rate misalignments in which we measure heterogeneity in tradable
price changes, i.e. variations in the terms of trade. The threshold and coefficient of the *Under* variable
change only marginally in the explanation of the share of manufacturing. In a second robustness check,
we revert to the standard measurement of misalignments with natural resource rents retained in the
level of GDP. The threshold for the *Under* variable then rises to 33%, and the coefficient becomes
curiously negative above that. We interpret this result as a justification for eliminating rents from GDP.
The rest of the paper is organized as follows. Section 2 discusses the evolution of the share of manufacturing over the development process and the effects that can be expected from real exchange rate misalignments. These misalignments are derived from the ratio of the purchasing power parity (PPP) conversion factor to the official exchange rate. The ratio is adjusted for the international productivity bias. Section 3 discusses how misalignments are calculated and the dynamic threshold panel models that we specify with a threshold for the *Under* and *Over* variables in the same regression. Section 4 comments on our empirical results. Section 5 presents some robustness checks. Section 6 summarizes and draws some conclusions for economic policy.

## II. Determinants of the share of manufacturing and the role of exchange rate misalignments

Empirical studies focusing on explaining the share of manufacturing in GDP are still few and far between, although most countries have undergone a deindustrialization process. Africa as a whole illustrates this shift. In 2010, the share of manufacturing in GDP was around 10% compared with 15% in 1975 (Rodrik, 2014), a far cry from what Ellis (2005) described as the sector's "golden age" (1960-1975). Developments were just as marked in Asia and Latin America, in contrast to Lewis's (1954) predictive normative scheme. This phenomenon affected both value-added and manufacturing employment (McMillan and Rodrik, 2011). China and India's impressive, sustained economic growth constituted a major competitive shock that impacted on the development of this sectoral contraction. Developing and transition economies also suffered from their own structural adjustment policies in a context of global trade liberalization. Changes in the real exchange rate were generally insufficient to offset the impact of lower tariffs and trade barriers and the weakness of public institutions. In other words, the domestic production context made it hard to implement the Schumpeterian paradigm of
"creative destruction". Job losses in the manufacturing sector drove shifts to low-productivity undertakings in agriculture and informal services (De Vries. G, M. Timmer and K. de Vries, 2013).

In our literature review of the potential determinants of the share of manufacturing, we obviously pay particular attention to the expected role of currency misalignments. In his seminal article, Rodrik (2008) finds for a set of predetermined ranges of misalignment rates that the marginal effects of undervaluation and overvaluation are similar and stable with respectively a positive and negative impact on the considered economic performance indicators. Over and above this review of the variable of interest, we focus on structural and economic policy factors that may together help explain the share of manufacturing.

Currency misalignment and manufactured goods

What does the applied literature tell us about exchange rate misalignments? The econometric focus is generally on their effect on GDP or export growth. The misalignment criterion we refer to here is the result of deviations from the principle of purchasing power parity (PPP), which can be considered to reflect competitiveness via relative unit production costs.

Whatever the performance variable considered, there is a fairly broad consensus that overvaluation reduces collective well-being, even though moderate rates may have minor negative impacts (Easterly, 2000; Razin and Collins, 1997). Rapetti (2016) and the literature on Dutch disease highlight this phenomenon of crowding out manufactured goods, particularly those subject to increasing returns to scale and learning-by-doing externalities (e.g. Baldwin and Krugman, 1989; Van Wijnbergen, 1984). A high rate of overvaluation sends out the wrong price signals. It generally restricts convertibility, and creates currency shortages and a parallel foreign exchange market. Companies anticipating uncertain access to imported intermediate goods have to build up abnormal inventories, which increases production costs. Unlike the work on overvaluation, which is fairly consensual save
with respect to the real economic cost of low rates, the relevance of discretionary undervaluation is
more debated.

Starting with the positive arguments, in an environment of strong international competition,
appropriate incentives are needed to ensure that the manufacturing sector drives structural
transformation. An active exchange rate policy can contribute to this. Rodrik (2008) points up the
advantages of an undervalued currency. To the extent that the variation in the relative internal price is
sustainable, it supports the production of tradables by reducing the market failures and institutional
weaknesses that disproportionately affect them. Undervaluation is tantamount to subsidizing the value-
added of tradables. It instantly improves their profitability and, unlike public subsidies, the
effectiveness of this support is not conditional on the availability of public funds (Rodrik, 2016). Méon
and Sekkat (2008) highlight the relationship between institutional weaknesses and currency
undervaluation by pointing out that sophisticated goods are more intensive in contractual arrangements
than primary goods, and more exposed to the problem of enforcement and ensuing costly legal
procedures. Undervaluation mitigates these abnormal costs, which are akin to an implicit tax on the
production of manufactured goods.

Price distortions favor the production of non-tradables. Assuming that demand in this sector is
only weakly price-elastic, the absence of international competition enables costs to be passed on to
local consumers and profit margins to be maintained. Since it is impossible to remedy every single
market failure, undervaluation is a second-best solution for correcting these distortions. This obviously
implies the absence of retaliation by the international community, i.e. a beggar-thy-neighbor policy
comparable to that which prevailed in the 1930s. A competitive real exchange rate can be seen as a
component of horizontal industrial policies, that is to say an instrument creating no distortion among
tradables. It is not subject to the lobbying activities that are a shortcoming of public choice in vertical
industrial policy design. This problem is well known when supported companies or industries are too
narrowly targeted (cherry-picking), sometimes to defend private interests that conflict with collective
well-being.

Rodrik's seminal article (2008) considers a panel of 11 five-year periods for 188 countries over the 1950-2004 period. Undervaluation has a positive impact on both economic growth and the share of industry in GDP. A 50% undervaluation is estimated to raise this share by 1.2 percentage points. There is no statistical evidence for some undervaluation thresholds, which means that linearity is not rejected and that *Under* stimulates structural transformation just as much as *Over* slows it down. Berg and Miao (2010) share this conclusion and reject neither the linearity hypothesis nor the invariant coefficient for *Under* and *Over*. Elbadawi et al. (2012) use panel data for 83 countries over the 1980-2004 period. They show that a one standard deviation change in misalignment leads to a loss of economic growth of around 1.1 percentage points. They also point out that *Over* negatively affects structural change, and potentially the share of manufacturing, while *Under* facilitates it. Rapetti et al. (2012) take a similar line, showing that a competitive exchange rate effectively stimulates structural change by increasing the profitability of labor-intensive tradables, including in manufacturing. Their results support Rodrik's findings, with a positive effect of undervaluation remaining for countries with per capita GDP between $9,000 and $15,000. Guzman et al. (2018) also argue that a stable, competitive real exchange rate encourages the production of tradables, discoveries and learning processes in the uncertain environment of fixed and sunk costs associated with the production of manufactured goods. Active use of the real exchange rate instrument limits the need for public financial support. This is an important consideration for most developing countries where the opportunity cost of public funds is high. It also protects against the potential drawbacks of discretionary industrial policies in situations where bureaucrats are ill-informed or corrupt.

The abovementioned arguments come up against more critical literature on undervaluation and its
economic consequences. In Subramanian et al. (2007), *Under* is found to be much less important than
overvaluation. The analyses conducted by Galvarriato and Williamson (2008) are not totally conclusive
for Latin American countries in the post-1870 period. They show that an incentive price ratio for tradables supported industrialization in Brazil and Mexico, but failed to achieve this objective in Argentina, Chile and Colombia. Levy-Yeyati et al. (2013) and Glüzmann et al. (2012) emphasize the income redistribution effect of undervaluation from poor to rich households and from wage earners to companies. The authors take up the contractionary devaluation hypothesis for a closed economy (Díaz Alejandro, 1981). Income redistribution can have the effect of reducing the level of aggregate demand. In this case, manufacturing output may fall if the drop in local demand is not offset by exports. Schröder (2013) also argues that undervaluation based on relative price criteria would not boost economic performance. The best real exchange rate management strategy would be to maintain the rate at its equilibrium level. This brings the author into line with neoclassical recommendations and the requirements of international cooperation. Couharde and Sallenave (2013) are less categorical in their empirical analysis of a sample of developed and emerging countries over the 1980–2009 period. Undervaluation has an incentive effect on tradables, but this positive effect is not without its limits. The panel smooth transition regressions they use show that above a threshold estimated at 25.9%, undervaluation would have contractionary effects on domestic production. The conclusion then conflicts with linearity hypothesis posited by Rodrik (2008), and confirms in part the doubts of the new structuralist school regarding a policy that would support sharp currency depreciation.

Structural determinants of the share of manufacturing

Some of the most influential development economics pioneers have documented normal long-
term changes in the sectoral components of GDP. In the two-sector economy studied by Lewis (1954),
inter-sectoral labor mobility is driven by productivity differentials. Low productivity in agriculture
drives internal mobility by the working population to manufacturing activities where capital intensity
is higher. Supply side changes are accompanied by changes in the composition of domestic demand.
Higher income levels encourage consumption of manufactured goods processed in urban areas. At an
advanced level of development, a new intersectoral shift takes place, this time raising the share of
services. Therefore, the relation over time between the share of manufacturing and per capita GDP takes the form of an inverted U-shape curve. Based on these long-run stylized facts, Chenery and Syrquin (1975) hypothesize this quadratic relation and round out their econometric specification with a set of structural determinants reflecting the economic impact of physical geography. In so doing, they draw attention to population size. Large domestic markets increase productive efficiency by increasing the intensity of competition. They also provide technological advantages by means of economies of scale that reduce the effect of the indivisibilities of investments and sunk costs in both production and trade.

Chenery and Syrquin (1975) were the first authors to focus on the economic impact of these factors in an international cross-section study. They assume that, once structural and geographical determinants have been taken into account, deviations from the regression line, i.e. regression residuals for the manufacturing industry share, reflect the impact of political variables. Today, such a methodological approach is obviously open to criticism. Over and above the potential endogeneity of some variables, which is not considered, the cross-sectional nature of the estimation means that there is no omission of specific national factors. In other words, regression residuals can be arbitrarily assigned to economic policy variables. A more adequate method of econometric estimation is obviously to specify the model with all the relevant regressors, preferably in a panel data analysis. The empirical estimates proposed later in this article are based on this approach.

## Additional policy covariates of the share of manufacturing

The quality of institutions conditions human interactions and transaction costs. Weak institutions put a brake on structural change (North, 1991). Effective rules, on the other hand, encourage trust, which is the “lubricant of a social system” (Arrow, 1974). Acemoglu and Robinson (2012) point up that leaders and elites create “extractive institutions” which undermine the production of tradables, including in the manufacturing sector, which requires higher levels of investment, with depreciation schedules extending over longer periods than agricultural activities. Costs lower profitability in an
environment of competitive pressure. The authors argue that institutional quality accounts for as much
as 75% of the variation in income levels around the world (Acemoglu et al., 2019). A business-friendly
environment is created by a combination of political stability and inclusiveness, the rule of law and
absence of violence, and a transparent and accountable government committed to rolling back public
sector corruption. Good governance reduces transaction costs and uncertainty, and therefore the risks
associated with capitalist investments with a long-term profitability profile, as is the case for
manufactured production. The World Bank's Worldwide Governance Indicators (WGI) provide a set
of six measures reflecting the quality of public governance. A composite index based on a principal
component analysis (PCA) is considered in the estimation of our empirical model to manage the
existing multicollinearity between individual components.

The impact of Foreign Direct Investment (FDI) is hard to determine accurately because it is conditional on the investment sector (Ocampo and Stiglitz, 2008). In natural-resource-rich countries, FDI in this sector may hamper the production of manufactured goods, in keeping with the Dutch disease theory and the natural resource curse. On the other hand, FDI in manufacturing would be expected to bolster the sector due to foreign firms’ technological and organizational expertise, with potential spin-offs for domestic firms. To avoid the ambiguity of the effect, rather than considering the ratio of total FDI flows to GDP, we focus on FDI in the manufacturing sector as provided by the *Financial Times'* *FDI Markets* database.

### III. Misalignments adjusted for rents and the dynamic system GMM estimator with threshold effects

*Natural resource rents and their effect*

A high percentage of natural resource rents in GDP influences the share of manufacturing in a number of ways. Firstly, if the raw materials associated with these rents are not processed locally, they
automatically inflate the level of GDP, but reduce the share of manufacturing industries. Natural resource rents represent more than 20% of GDP in a dozen countries in our sample (Appendix 4). Secondly, the domestic distribution of these rents also matters and plays out in different ways. When the economy's permanent income level is high because of them, then wages and the prices of non-tradables are also high. These high levels reduce the competitiveness of non-traditional goods and generally crowd out manufacturing goods. An alternative scenario is when natural resources rents fuel domestic undervaluation by subsidizing non-tradables. This phenomenon can be seen in developing countries exporting oil and gas.

As Eifert et al. (2003) point out, just as political traditions determine the use of oil revenues, revenues themselves determine the political economy of oil-exporting countries. Elites in resource-rich countries often invest less in infrastructure and directly productive activities. They strive to appropriate the rents and distribute part of them to the population in order to preserve the stability of their political power (Van der Ploeg, 2011). One way of implementing this strategy is to subsidize current consumption. Such a policy lowers the retail price of the consumer goods that account for a significant proportion of urban household budgets: foodstuffs, gas or light fuel-oil, public transport and social housing. A lower general price level can raise the share of the manufacturing industry. Yet, the flipside of this is the “resource curse”. Many oil-, gas- and mineral-rich countries are authoritarian and domestically conflict-prone. Officials are reluctant to promote transparency with respect to the large revenues collected, and economic elites are rent-seeking, investing less in the sector exposed to international competition. Resource rents thus become a curse. Distorted domestic prices and weak institutions discourage risky investment in tradables, primarily export-oriented manufacturing. As a result, cronyism and the political economy rule out any incentive to invest in activities that promote a long-term, sustainable structural change in the productive base (Schwab and Werker, 2018).

It is easy to control for these effects on the share of the manufacturing industry by introducing an explanatory variable reflecting the percentage of resource rents in GDP. All other things being equal,
a higher percentage of rents automatically lowers the share of the manufacturing industry. However, there is also a more indirect impact through productivity levels. Resource rents increase permanent per capita GDP, which no longer reflects the real productivity of non-traditional tradables, those exposed to international competition.

We derive yearly real exchange rate (RER) misalignments from the absolute PPP criterion corrected for the Balassa (1964)-Samuelson (1964) effect. Before adjusting for productivity level, natural resource rents (NRR) are subtracted from GDP (Per capita GDP<sub>NRR</sub>) (2). For a country i, the consolidated share of rents ($\theta_i$) is taken from the World Bank's WDIs (3 and 4). The international convergence of productivity in tradables drives the long-run convergence of the price of non-tradables. We calculate misalignments from a long-run relationship (5) by considering a larger sample of countries than ours covering all per capita development levels. We define the RER as the ratio of the PPP conversion factor (i.e. the dollar exchange rate that enables the same amount of goods to be purchased in a given country as in the United States) to the official exchange rate for the US dollar (ER). This RER is then regressed on the per capita GDP level. In regression (5), $f_t$ is a year fixed effect and $\alpha$ the constant. Misalignments, hereinafter $MIS_{NRR}$ (6) are captured by the regression residuals of (5), where $u_{it} < 0$ refers to the percentage of *Under* and $u_{it} > 0$ to *Over*.

For country i at time t, $MIS_{NRR}$ are calculated as follows:

$$RER_{it} = \frac{PPP_{it}}{ER_{it}} \qquad (1)$$

$$(Per capita GDP_{NRR}) = (1 - \theta_i) Per capita GDP \qquad (2)$$

$$\theta_i = \sum_{k=1}^{5} [(P_k - CM_{ik})\mu_{ik}] \qquad (3)$$

$$\text{with } \mu_{ik} = Q_{ik}/GDP_{it} \qquad (4)$$

$\theta_i \in [0, 1]$ is the difference between the world price for product k ($P_k$) and the related unit cost of production ($CM_{ik}$) weighted by the relative importance of product k in total rents ($\mu_{ik}$).
$$ \log (RER)_{it} = \alpha + \beta \log (Per\ capita\ GDP_{NRR})_{it} + f_t + u_{it} \quad (5) $$

$$ MIS_{NRRit} = \log (RER)_{it} - \log (\overline{RER})_{it} \quad (6) $$

## The Dynamic SGMM estimator and threshold effects

First, we test for the presence of an asymmetry in misalignment by hypothesizing that *Under* and *Over* may have different impacts on the share of manufacturing in GDP, hereinafter referred to as *Manufsh*<fnref n="1" />

$$ Manufsh_{it} = \alpha + \rho Manufsh_{it-1} + \eta_1 Under_{it} + \eta_2 Over_{it} + \delta_1 X_{1it} + \delta_2 X_{2it} + \mu_i + \tau_t + v_{it} \quad (7) $$

In Eq. (7), X is a vector encompassing endogenous ($X_1$) and exogenous ($X_2$) controls. $\mu_i$ and $\tau_t$ represent regional and time fixed effects, respectively, and $v_{it}$ is the usual random error term. We estimate the equation using the dynamic system GMM estimator (Blundell and Bond,1998). We identify endogenous variables using the Hausman-Wu test. Second, we hypothesize that the impact of *Under* and *Over* may be non-linear using a Hansen-(2000)-type estimator that prevents arbitrariness in the choice of ad hoc thresholds. In Eq. (8), we rewrite Eq. (7) with two threshold variables and two threshold parameters:

<footnote n="1">¹ *Under* is negative. A higher level of undervaluation results in a decrease in the ratio when $\eta_1 < 0$, or an increase if $\eta_1 > 0$. To facilitate the interpretation of coefficient $\eta_1$, the absolute value of the misalignment is considered. When undervaluation increases in absolute terms, a positive sign of $\eta_1$ means that undervaluation bolsters the ratio while the effect is adverse when $\eta_1 < 0$.</footnote>
$$ 
\begin{aligned}
\text{Manufsh}_{it} = & \alpha + \rho \text{Manufsh}_{i,t-1} + \eta_{10} I(\text{Under}_{it} > \gamma_1) + \eta_{1L} \text{Under}_{it} I(\text{Under}_{it} \le \gamma_1) \\
& + \eta_{1H} \text{Under}_{it} I(\text{Under}_{it} > \gamma_1) + \eta_{20} I(\text{Over}_{it} > \gamma_2) + \eta_{2L} \text{Over}_{it} I(\text{Over}_{it} \le \gamma_2) \\
& + \eta_{2H} \text{Over}_{it} I(\text{Over}_{it} > \gamma_2) + \delta'_{1} X_{1it} + \delta'_{2} X_{2it} + \mu_i + \tau_t + v_{it}
\end{aligned}
\quad (8) $$

I (.) is the indicator function. It takes the value 1 if the argument in parenthesis holds and 0 otherwise. Parameter $γ_1$ indicates that when *Under* is below threshold $γ_1$, its impact on the share of manufacturing activities is $η_{1L}$. The marginal effect is $η_{1H}$ when undervaluation is above that threshold. Both threshold parameter $γ_2$ and the impact of *Over* are interpreted similarly. *Over* and *Under* are both threshold variables and regime-dependent variables. The dynamic panel threshold model includes specific regime dummies for intercepts $I(\text{Under}_{it} > \gamma_1)$ and $I(\text{Over}_{it} > \gamma_2)$. Omitting these regime dummies may introduce a bias for both the parameter estimates and the thresholds (Bick, 2010). To estimate Eq. (8), we extend static panel threshold model developed by Hansen (1999) by adapting the dynamic panel data specification proposed by Kremer et al. (2013).<fnref n="2" /> Dang et al. (2012, 2014), Asimakopoulos and Karavias (2016), and Law et al. (2021) use a similar estimation procedure. Our dynamic model differs from that proposed by Kremer et al. (2013) in two ways. First, we account for two endogenous threshold variables (*Under*, *Over*). Second, the two threshold variables are also endogenous. Before estimating Eq. (8), let's start by considering the particular case where the two threshold levels are known, $γ'_1$ and $γ'_2$. In this case, Eq. (8) belongs to the standard dynamic panel data models that are estimated using the GMM type estimators including the GMM system (Blundell and Bond, 1998).

In a first step, the endogenous threshold variables are regressed on their instruments, i.e. the lagged values of *Under* and *Over* plus the set of exogenous variables (matrix $X_2$). Predicted values are

<footnote n="2">² These authors extend the GMM estimation technique to the dynamic panel threshold models. They also consider endogeneity on a subset of explanatory regressors, but not threshold variables. Seo and Shin (2016) consider the endogeneity of the threshold variable. In these two studies, there is only one threshold variable.</footnote>
then calculated (Under, Over). A grid search procedure is used taken from an adaptation of the methodology proposed by Hansen (1999). We run system-GMM (SGMM) estimations and calculate the resulting sum of squared residuals $S(\gamma_1, \gamma_2)$ for each value of $\gamma'_1$s and $\gamma'_2$s in the subset of the support of each threshold variable Under and Over.<fnref n="3" /> The selected thresholds $\hat{\gamma} = (\hat{\gamma}_1, \hat{\gamma}_2)$ are those providing the smallest sum of squared residuals $S(\gamma_1, \gamma_2)$. Once estimated, $\hat{\gamma}_1, \hat{\gamma}_2$, are substituted for $\gamma_1, \gamma_2$ in Eq. (8), which is estimated by the SGMM estimator. We also use two cut-off points, at 10% and 90%, to prevent extreme values from determining the thresholds (Hansen, 1999). To maintain the uncorrelatedness of the errors, we use the forward orthogonal observation transformation as suggested by Arellano and Bover (1995). Kremer et al. (2013), Osei and Kim (2020), and Law et al. (2021) also estimate their dynamic panel threshold models similarly to ensure that the error terms are not correlated.

To sum up, our estimator combines the SGMM estimator and the threshold model developed by Hansen (1999). Once estimated, a number of hypotheses are considered regarding the model's coefficients for the two regime-dependent variables. Specifically, the null hypotheses, which can be tested for Eq. (8) are:

(i) No threshold effect on currency misalignment, Under and Over: $\eta_{10} = 0, \eta_{1L} = \eta_{1H}, \eta_{20} = 0, \eta_{2L} = \eta_{2H}$

(ii) A single threshold effect for Under only: $\eta_{20} = 0, \eta_{2L} = \eta_{2H}$

(iii) A single threshold effect for Over only: $\eta_{10} = 0, \eta_{1L} = \eta_{1H}$

<footnote n="3">³ Estimation results may be sensitive to the number of instruments, which might exceed the number of countries and cause overfitting (see Roodman, 2009). To reduce the number of instruments, the collapse option was used whenever the GMM estimator was employed. The Windmeijer small sample correction was not used as it is not clear whether it extends to non-linear GMM as in our case.</footnote>
These linear assumptions are tested by conducting a bootstrap-type test. Hansen (1999) proposes a procedure to simulate the asymptotic distribution of the test. In keeping with Seo and Shin (2016) and Dang et al. (2012, 2014), we adapt this procedure for our dynamic panel threshold regression and the three hypothesis tests using the following four steps. The null hypothesis is rewritten in its matrix form where R is a matrix of linear restrictions and θ the parameters to be tested:

$$H_0: R\theta = 0 \qquad (9)$$

**Step 1:** Based on the data sample, let $\hat{\theta}(\hat{\gamma})$ denote the GMM parameter estimates of Eq. (8) under the alternative and the corresponding residuals, $\hat{\varepsilon}_{it}(\hat{\gamma})$. Calculate the Wald test statistics:<fnref n="4" />

$$\Phi(\hat{\gamma}) = [R\hat{\theta}(\hat{\gamma})]' [RVar(\hat{\theta}(\hat{\gamma}))R']^{-1} [R\hat{\theta}(\hat{\gamma})] \qquad (10)$$

**Step 2:** Select a random draw in the residual's distribution $\hat{\varepsilon}_{it}(\hat{\gamma})$, and for each j-th bootstrapped residual (sampling with replacement) denoted $\hat{\varepsilon}_{it}^{(j)}$, use these errors to generate a bootstrapped sample under H0. For example, for test (j) the corresponding equation used under H0 is:

$$Manufsh^{(j)}_{it} = \hat{\alpha} + \hat{\rho} Manufsh^{(j)}_{i,t-1} + \hat{\eta}_1 Under_{it} + \hat{\eta}_2 Over_{it} + \hat{\delta}'_1 X_{1it} + \hat{\delta}'_2 X_{2it} + \hat{\varepsilon}_{it}^{(j)}, \quad j=1,\dots,B \quad (11)$$

The estimated coefficients $\hat{\theta} = (\hat{\alpha}, \hat{\rho}, \hat{\eta}_1, \hat{\eta}_2, \hat{\delta}'_1, \hat{\delta}'_2)$ are those obtained from Step 1. Given the nature of the dynamic model, we treat in each replication j the initial conditions as given by $Manufsh^{(j)}_{i1} = Manufsh_{i1}$ and $Manufsh^{(j)}_{i2} = Manufsh_{i2}$ (Dang et al., 2012). Finally, B=1000 is the total number of replications.

<footnote n="4">⁴ Hansen (1999) proposes a likelihood ratio test requiring the estimation of the model under both the null and the alternative. Seo and Shin (2016) use a Wald test calling for the estimation of the model under the alternative only. This last test is more appealing in our case since the GMM-system estimation method is time consuming. The Wald test is therefore more appropriate.</footnote>
**Step 3:** Using the bootstrapped sample generated in Step 2, estimate the model under the alternative and calculate the Wald statistics, denoted $\Phi(\hat{y})^{(i)}$, using Eq. (10).

**Step 4:** Repeat steps 2-4 B times and calculate the bootstrap p-value by the frequency that the simulated statistic $\Phi(\hat{y})^{(i)}$ exceeds the actual one $\Phi(y)$.

# IV. Econometric analysis and comments

## *The empirical sample*

We cover the 2003-2019 period. Although statistical data is available up to 2021, the last two years were severely disrupted by the Covid-19 pandemic and its repercussions. In 2020 and 2021, the lockdowns of almost half the world's population impacted on GDP and its sector breakdown. Manufactured goods were particularly hard hit, as global value chains often use imported intermediate inputs. The other two sectors were less affected. Primary production is dominated by goods largely consumed locally and by non-containerized exports such as gas and oil. In the context of a major transport and logistics shock, low- and middle-income countries – those with the lowest volumes of international trade in manufactured goods – were also hardest hit (Plane, 2021). The tertiary sector in developing economies consists mainly of non-tradable goods. Public sector production helped stabilize GDP, as civil servants generally continued to receive their salaries during lockdown. As a result, the Covid-19 pandemic and the transport and logistics shocks affected primarily manufacturing. Moreover, as most countries absorbed these shocks by means of changes to the real exchange rate, the introduction of years 2020 and 2021 would bias the estimation of the normal relationship between the share of manufacturing and levels of misalignment.

The empirical sample comprises 102 developing countries and countries in transition to a market economy in 2003. Although they were all supposed to be targeting industrialization, some were in a process of "early deindustrialization", shifting to services while the share of manufacturing
activities remained very low. The list of countries considered was conditioned not only by the availability of statistical data, but also by their size. Countries with a population of less than 1.5 million in 2003 were not included. Most of them were primary or service economies. As trade openness only partially offsets the disadvantages of small population size, most of these countries had no ambition to develop their manufacturing sector. In the history of economic development, it is a fact that very few of them have succeeded in industrializing.

## Regression results and comments

We derive $MIS_{NRR}$ from eq (12), which is run over the 2003-2019 period. The coefficient of $Per capita GDP_{NRR}$ is in line with the empirical literature (Rodrik, 2008; Elbadawi, 2012), significant at the 99% confidence level. A 10% increase in per capita income gives rise to a “normal” long-term appreciation in the RER of about 2.2%. Robust standard errors are in parentheses for a sample of 175 countries at all levels of development, which means we reach far beyond our empirical sample of 102 developing countries (see Appendix 1).<fnref n="5" />

$$ \log (RER)_{it} = -2.702 + 0.222 \log (Per capita GDP_{NRR})_{it} \quad (12) \\ (0.03) \quad (0.004) \quad \text{***} $$

$R^2 = 0.505$, Observations = 4393, Countries = 175, Year fixed effects = Yes.

Let's start by testing the exogeneity of the explanatory variables. Population size aside, all of the covariates of Manufsh can be suspected of endogeneity. The Durbin Hausman Wu (DHW) test confirms this for both $MIS_{NRR}$ and the FDI ratio (Table 1).

<footnote n="5">⁵ *** p<0.01, ** p<0.05, * p<0.1.</footnote>
<table><caption>Table 1. The Durbin-Wu-Hausman, DHW test for endogeneity (2003-2019).</caption><thead><tr><th rowspan="2">Pair of variables</th><th colspan="2">Quality of Instruments</th><th rowspan="2">P-value<br>of DHW tests</th><th rowspan="2">Conclusion</th></tr><tr><th>Fisher Tests</th><th></th></tr></thead><tbody><tr><td>Manufsh vs. MIS<sub>NRR</sub></td><td>F(11, 1374) = 92.18***</td><td>0.000</td><td></td><td>Endogenous</td></tr><tr><td>Manufsh vs. FDI</td><td>F(11, 1374) = 4.58***</td><td>0.000</td><td></td><td>Endogenous</td></tr><tr><td>Manufsh vs. Institutions</td><td>F(11, 1374) = 68.87***</td><td>0.421</td><td></td><td>Exogenous</td></tr><tr><td>Manufsh vs. ln per capita GDP</td><td>F(11, 1374) = 335.45***</td><td>0.417</td><td></td><td>Exogenous</td></tr></tbody></table>

Mis NRR: Exchange rate misalignments calculated as a 3-year moving average after adjusting Per capita GDP for the impact of natural resource rents (NRR). Institutions is the composite index of WGI components. It is calculated with the first axis of the Principal Component Analysis.

We hypothesize distinct thresholds for *Under* and *Over* in the same regression. Linearity is tested by the Wald bootstrap test, which rejects the hypothesis of no threshold, and suggests a threshold effect for *Under* only (Table 2). The Table 3 regressions are based on these results.

<table><caption>Table 2. Tests of threshold effects on misalignment components (2003-2019), (MIS NRR is corrected for per capita GDP excluding natural resource rents).</caption><thead><tr><th>Null assumption</th><th>Restrictions</th><th>P-value</th></tr></thead><tbody><tr><td>No threshold effects on <i>Under</i></td><td>η<sub>10</sub> = 0, η<sub>1L</sub> = η<sub>1H</sub></td><td>0.078</td></tr><tr><td>and <i>Over</i></td><td>η<sub>20</sub> = 0, η<sub>2L</sub> = η<sub>2H</sub></td><td></td></tr><tr><td>Threshold effect on <i>Under</i> only</td><td>η<sub>10</sub> = 0, η<sub>1L</sub> = η<sub>1H</sub></td><td>0.026</td></tr><tr><td>Threshold effect on <i>Over</i> only</td><td>η<sub>20</sub> = 0, η<sub>2L</sub> = η<sub>2H</sub></td><td>0.780</td></tr></tbody></table>

Note: P-value is the rejection probability for the bootstrapped Wald test, the number of replications B=1000.
<table><caption>Table 3. Dynamic panel threshold regressions and exchange rate misalignment components (2003-2019 period).</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Threshold</td><td>ŷ<sub>1</sub> = 0.18</td><td>ŷ<sub>1</sub> = 0.18</td><td>ŷ<sub>1</sub> = 0.18</td><td>ŷ<sub>1</sub> = 0.18</td></tr><tr><td>L. Manufsh</td><td>0.925***<br>(0.013)</td><td>0.918***<br>(0.013)</td><td>0.919***<br>(0.013)</td><td>0.909***<br>(0.013)</td></tr><tr><td>Over</td><td>-1.793***<br>(0.378)</td><td>-1.747***<br>(0.371)</td><td>-1.734***<br>(0.377)</td><td>-1.685***<br>(0.365)</td></tr><tr><td>I(Under > γ<sub>1</sub>)</td><td>0.089<br>(0.096)</td><td>0.108<br>(0.095)</td><td>0.140<br>(0.094)</td><td>0.148<br>(0.095)</td></tr><tr><td>UnderI(Under ≤ γ<sub>1</sub>)</td><td>1.548**<br>(0.756)</td><td>1.585**<br>(0.741)</td><td>1.542**<br>(0.772)</td><td>1.540**<br>(0.759)</td></tr><tr><td>UnderI(Under > γ<sub>1</sub>)</td><td>1.173***<br>(0.293)</td><td>1.135***<br>(0.312)</td><td>1.126***<br>(0.310)</td><td>1.059***<br>(0.309)</td></tr><tr><td>FDI</td><td>-0.011<br>(0.014)</td><td>-0.008<br>(0.014)</td><td>-0.010<br>(0.015)</td><td>-0.009<br>(0.015)</td></tr><tr><td>Institutions(PCA)</td><td>0.047**<br>(0.020)</td><td>0.039*<br>(0.021)</td><td>0.017<br>(0.023)</td><td>0.027<br>(0.024)</td></tr><tr><td>Rents 10%</td><td></td><td>-0.096<br>(0.084)</td><td></td><td></td></tr><tr><td>Rents 15%</td><td></td><td></td><td>-0.297***<br>(0.099)</td><td></td></tr><tr><td>Rents 20%</td><td></td><td></td><td></td><td>-0.321***<br>(0.089)</td></tr><tr><td>LnGDPPC</td><td>-0.442<br>(0.359)</td><td>-0.488<br>(0.332)</td><td>-0.714**<br>(0.329)</td><td>-0.499<br>(0.374)</td></tr><tr><td>LnGDPPC<sup>2</sup></td><td>0.017<br>(0.022)</td><td>0.022<br>(0.020)</td><td>0.038*<br>(0.020)</td><td>0.025<br>(0.022)</td></tr><tr><td>LnPOP</td><td>0.065*<br>(0.035)</td><td>0.073**<br>(0.034)</td><td>0.057*<br>(0.034)</td><td>0.077**<br>(0.035)</td></tr><tr><td>Africa</td><td>-0.057<br>(0.098)</td><td>-0.042<br>(0.108)</td><td>0.010<br>(0.108)</td><td>-0.031<br>(0.111)</td></tr><tr><td>Asia</td><td>-0.219**<br>(0.087)</td><td>-0.207**<br>(0.094)</td><td>-0.140<br>(0.099)</td><td>-0.126<br>(0.102)</td></tr><tr><td>Latin America</td><td>-0.075<br>(0.081)</td><td>-0.080<br>(0.082)</td><td>-0.073<br>(0.083)</td><td>-0.105<br>(0.086)</td></tr><tr><td>Constant</td><td>2.191<br>(1.725)</td><td>2.222<br>(1.605)</td><td>3.203**<br>(1.592)</td><td>2.088<br>(1.811)</td></tr><tr><td>No. of observations</td><td>1,270</td><td>1,270</td><td>1,270</td><td>1,270</td></tr><tr><td>Number of countries</td><td>102</td><td>102</td><td>102</td><td>102</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td># Instruments</td><td>85</td><td>86</td><td>86</td><td>86</td></tr><tr><td>Ar(1)_Pvalue</td><td>0.001</td><td>0.002</td><td>0.002</td><td>0.002</td></tr><tr><td>Ar(2)_Pvalue</td><td>0.938</td><td>0.928</td><td>0.933</td><td>0.930</td></tr><tr><td>Hansen_P-value</td><td>0.329</td><td>0.324</td><td>0.281</td><td>0.279</td></tr></tbody></table>

Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. FDI, and mis components (Over, Under) are endogenous.
The estimation procedure we use for the Table 3 regressions combines the extension of the Kremer et al. (2013) dynamic panel threshold model and the System Generalized Method of Moments with internal and external instruments. Regional dummies and year fixed effects capture unobservable heterogeneity. The reference region is East European countries. The regressions differ only in terms of the dummy variable reflecting the percentage of natural resource rents in GDP. The Roodman (2009) rule of thumb applies. The number of instruments is smaller than the number of countries to capture the efficiency/bias trade-off in finite samples. Although there is no clearly defined p-value threshold for the over-identification restrictions test, our standard Hansen statistic is well above the threshold of concern (Vliet, 2020).

Let's briefly discuss the impact of covariates before moving on to more substantive comments regarding *Over* and *Under*. For these two variables of interest, a three-year moving average is used to prevent exchange rate volatility blurring the effect on the manufacturing/GDP ratio. The coefficient of the lagged dependent shows that the model is subject to a strong inertia phenomenon. The quadratic relation of Per capita GDP is rejected. The composition of the sample, which excludes high-income countries where a substitution effect over time is expected to occur between the share of manufacturing and services, contributes to this result. The ratio of FDI in the manufacturing sector is never statistically significant, unlike the quality of institutions as measured by the World Governance Indicators. Note, however, that the institutions coefficient and its statistical significance weaken when the specification includes the percentage of natural resource rents. The higher the percentage of the rents, the lower the quality of the institutions. This negative correlation bears out the “resource curse” hypothesis. “Extractive political institutions” place power in the hands of a few and generate extractive economic institutions with unfair regulations and high barriers to market entry. (Acemoglu and Robinson, 2012). “Vested interests” prevent producers from reaping the economic benefits of their personal and material investments.
Our results are therefore consistent with the idea that institutional quality is a driver of the long-term development and share of the manufacturing industry. Acemoglu and Robinson (2019) posit that full democratization would induce a 20% increase in per capita GDP over 30 years, estimated by Rodrik (2016) at 0.6% per year. Depending on whether we consider the instantaneous or long-term effect, a one-standard-deviation variation in WGI increases the share of the manufacturing industry by 0.1 to 1.3 percentage points (regressions 1 and 2, Table 3). It would be interesting to disentangle the impact of this aggregate WGI, to identify the respective role of liberal democracy and market economy. Jha and Zhuang (2014) argue that low-income countries should strive for more effective government, a better quality of regulation and rule of law, adequate provision of essential public services and tighter control of corruption. At this level of development, the functioning of the market economy is important while the political regime also matters at higher income levels when the citizen participation process is more effective. As mentioned earlier, collinearity among components is a major issue for a detailed breakdown. The dummy variables reflecting the percentage of rents present the automatic negative effect we assumed earlier for values of at least 15% and 20%. On average, natural resource rents of at least 15% reduce the share of manufacturing by around three-tenths of a percentage point of GDP.

Let's now take a closer look at the misalignment ($MIS_{NRR}$) components. To adjust PPP for the Balassa-Samuelson effect, remember that these misalignments are calculated for a larger sample of 175 countries to cover all levels of development. Across our sample of 102 developing and transition countries, the average $MIS_{NRR}$ displays an undervaluation of 9%. This distribution breaks down into an average undervaluation of 22% and an overvaluation of 16% (Appendix 2). We obtain the expected negative effect for *Over*. Based on a variation of one standard deviation, the short-run effect is limited to a loss of 0.1 percentage point in Table 3, but 3.3 (Regression 2) to 3.6 (Regression 1) for the long-run effect. There are 418 country-year-observations in this overvaluation case. With respect to *Under*, the coefficients are fairly stable whatever the regression. Below the 18% threshold, the impact is some 0.25 and 3.3 percentage points respectively for the short- and long-term effects, falling to 0.2 and 2.3
above this threshold. For the 852 observations concerned by *Under* (67%), other things being equal, the effect proves positive and lower in magnitude than for *Over*. We therefore agree with the argument that overvaluation clearly needs to be corrected, but reject neoclassical mantras or "Victorian virtues" (Krugman, 1995). Maintaining the PPP exchange rate at its equilibrium level is not the most efficient strategy for stimulating structural change in developing and transition economies. Our empirical results differ from Rodrik (2008) in two respects. Firstly, the impact of *Under* is not found to be the same as that of *Over*, and secondly, the marginal effect of *Under* is not monotonic. It decreases moderately, and increasingly so, as the percentage of rents in economies increases. This has the side-effect of rendering the institutions coefficient statistically non-significant.<fnref n="6" />

## V. Robustness checks

We propose two robustness checks based on alternative measures of misalignments.<fnref n="7" /> First, we check whether our empirical conclusions remain valid when the real exchange rate is normalized by the country's productivity level and the external terms of trade. So far, we have assumed that there is only one tradable good. We relax this restrictive assumption by taking into account the price ratio between exports and imports (TOT). This price ratio influences the manufacturing sector's

<footnote n="6">⁶ On page 379, Rodrik (2008) mentions that unlike Álvaro Aguirre and César Calderón, and Ofair Razin and Susan Collins, little evidence is found of non-linearity in the relationship between undervaluation and economic growth.</footnote>

<footnote n="7">⁷ The use of an alternative econometric technique to the GMM method proves difficult in the context of a dynamic model with endogenous variables of interest that are potentially non-linear. An alternative is to specify the model with a polynomial functional form, for example a quadratic relationship between *Under/Over* and the share of manufacturing. In this case the functional form a priori predetermines the threshold. Table 3 regressions have been rerun considering squared terms of misalignment components and the GMM estimator. *Under* displays an inverted U-shaped impact while the impact proves monotonous and negative for *Over*. These results are fairly close to those obtained in Tables 3 and 4. They are available on request from the authors.</footnote>
competitiveness through various channels. In a second robustness test, we return to the RER adjusted
only for the most standard Balassa Samuelson effect, i.e. natural resource rents maintained in GDP.

Misalignments relaxing the assumption of a single tradable

TOTs are exogenous and represent a potential channel for transmitting a competitiveness shock. Given that the composition of a country's exports differs from that of its imports, the price of tradables is likely to change. A permanent increase in this ratio translates into an appreciation of the RER. If primary exported products are the source of this increase, it pushes up the consumption level and may have a negative impact on manufactured tradables. Indeed, assuming that the income effect dominates the substitution effect, the relative price of non-tradables rises. Another transmission channel is import trade liberalization. A reduction in the level of customs duties or an easing of import quotas leads to a fall in the price of imports and a subsequent rise in the terms of trade. This increase is likely to have a negative effect on the competitiveness of domestic manufactured goods by lowering nominal protection. However, the effect may be beneficial if trade liberalization applies to imported capital and intermediate goods. Reducing tariffs therefore illustrates the Schumpeterian paradigm of "creative destruction" with concrete implications for the manufacturing industry. The augmented RER regression with TOTs is run on the same sample as before and new misalignment levels are obtained. The regression coefficient of TOTs is positive and the per capita GDP coefficient maintains its previous level (13).

$$
\begin{equation}
\begin{split}
\log (\text{RER})_{it} = {}& -2.814 + 0.212 \log (\text{Per capita GDP}_{\text{NRR}})_{it} + 0.0359 \log (\text{TOT})_{it} \quad (13) \\
& \quad (0.095) \ \cdots \ (0.004) \ \cdots \qquad (0.0185) \ \cdots
\end{split}
\end{equation}
$$

R² = 0.518, Observations = 3887, Countries = 175, Year fixed effects = Yes.

Let's now analyze the relationship between these new misalignments and the dependent variable (Table 4). Hansen's test for over-identifying restrictions is satisfactory and econometric results
remain remarkably stable. *Over* keeps its negative impact and linearity is not rejected, whereas it is for *Under* with a threshold effect still maintained at 18%. Again, the coefficient of *Under* is slightly lower than that of *Over* and the marginal effect diminishes above the threshold. Furthermore, note that the percentage of rents remains negatively related to the share of manufacturing, and that their impact increases as before with their contribution to GDP.
<table><caption>Table 4. Dynamic panel threshold regressions and exchange rate misalignment components (Misalignments normalized by per capita GDP without rents and TOT, 2003-2019 period).</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Thresholds</td><td>ŷ<sub>1</sub> = 0.18</td><td>ŷ<sub>1</sub> = 0.18</td><td>ŷ<sub>1</sub> = 0.18</td><td>ŷ<sub>1</sub> = 0.18</td></tr><tr><td>L. Manufsh</td><td>0.935***<br>(0.012)</td><td>0.926***<br>(0.013)</td><td>0.929***<br>(0.012)</td><td>0.923***<br>(0.012)</td></tr><tr><td>Over</td><td>-1.833***<br>(0.424)</td><td>-1.785***<br>(0.413)</td><td>-1.767***<br>(0.417)</td><td>-1.727***<br>(0.407)</td></tr><tr><td>I(Under > γ<sub>1</sub>)</td><td>0.044<br>(0.095)</td><td>0.060<br>(0.093)</td><td>0.084<br>(0.095)</td><td>0.089<br>(0.096)</td></tr><tr><td>UnderI(Under ≤ γ<sub>1</sub>)</td><td>1.580*<br>(0.850)</td><td>1.625*<br>(0.835)</td><td>1.611*<br>(0.851)</td><td>1.608*<br>(0.837)</td></tr><tr><td>UnderI(Under > γ<sub>1</sub>)</td><td>1.384***<br>(0.362)</td><td>1.339***<br>(0.382)</td><td>1.351***<br>(0.375)</td><td>1.301***<br>(0.376)</td></tr><tr><td>FDI</td><td>-0.018<br>(0.014)</td><td>-0.013<br>(0.014)</td><td>-0.014<br>(0.015)</td><td>-0.014<br>(0.015)</td></tr><tr><td>Institutions(PCA)</td><td>0.036*<br>(0.019)</td><td>0.035*<br>(0.020)</td><td>0.014<br>(0.022)</td><td>0.021<br>(0.022)</td></tr><tr><td>Rents 10%</td><td></td><td>-0.052<br>(0.084)</td><td></td><td></td></tr><tr><td>Rents 15%</td><td></td><td></td><td>-0.229**<br>(0.098)</td><td></td></tr><tr><td>Rents 20%</td><td></td><td></td><td></td><td>-0.237***<br>(0.084)</td></tr><tr><td>LnGDPPC</td><td>-0.523<br>(0.365)</td><td>-0.485<br>(0.349)</td><td>-0.669*<br>(0.344)</td><td>-0.548<br>(0.370)</td></tr><tr><td>LnGDPPC<sup>2</sup></td><td>0.022<br>(0.022)</td><td>0.021<br>(0.021)</td><td>0.034<br>(0.021)</td><td>0.027<br>(0.022)</td></tr><tr><td>LnPOP</td><td>0.052<br>(0.036)</td><td>0.063*<br>(0.036)</td><td>0.048<br>(0.035)</td><td>0.057<br>(0.038)</td></tr><tr><td>Africa</td><td>0.061<br>(0.103)</td><td>0.053<br>(0.112)</td><td>0.105<br>(0.110)</td><td>0.076<br>(0.112)</td></tr><tr><td>Asia</td><td>-0.190**<br>(0.084)</td><td>-0.179*<br>(0.094)</td><td>-0.125<br>(0.097)</td><td>-0.114<br>(0.097)</td></tr><tr><td>Latin America</td><td>0.005<br>(0.091)</td><td>-0.008<br>(0.092)</td><td>0.005<br>(0.093)</td><td>-0.009<br>(0.097)</td></tr><tr><td>Constant</td><td>2.535<br>(1.725)</td><td>2.230<br>(1.643)</td><td>3.036*<br>(1.623)</td><td>2.442<br>(1.770)</td></tr><tr><td>No. of observations</td><td>1,270</td><td>1,270</td><td>1,270</td><td>1,270</td></tr><tr><td>Number of countries</td><td>102</td><td>102</td><td>102</td><td>102</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td># Instruments</td><td>79</td><td>80</td><td>80</td><td>80</td></tr><tr><td>Ar(1)_Pvalue</td><td>0.001</td><td>0.001</td><td>0.001</td><td>0.001</td></tr><tr><td>Ar(2)_Pvalue</td><td>0.961</td><td>0.947</td><td>0.954</td><td>0.950</td></tr><tr><td>Hansen_P-value</td><td>0.250</td><td>0.250</td><td>0.223</td><td>0.218</td></tr></tbody></table>

Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. FDI, Under, Over and PCA are endogenous variables.
Misalignments with no correction of GDP for natural resource rents

The second robustness check is the classic PPP criterion adjusted for the productivity bias with natural resource rents maintained in GDPs (14). Empirical estimates are presented in Table 5.

$$ \log (\text{RER})_{it} = -2.714 + 0.220 \log (\text{Per capita GDP})_{it} \quad (14) \\ (0.032) \quad \text{***} \quad (0.004) \quad \text{***} $$

R² = 0.477, Observations = 4398, Countries = 175, Year fixed effects = Yes.
<table><caption>Table 5. Panel threshold regressions: standard misalignment measurement
(Standard misalignments, normalized by per capita GDP, 2003-2019 period).</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Thresholds</td><td>ŷ<sub>1</sub> = 0.33</td><td>ŷ<sub>1</sub> = 0.33</td><td>ŷ<sub>1</sub> = 0.33</td><td>ŷ<sub>1</sub> = 0.33</td></tr><tr><td>L. Manufsh</td><td>0.932***<br>(0.014)</td><td>0.906***<br>(0.017)</td><td>0.909***<br>(0.016)</td><td>0.902***<br>(0.017)</td></tr><tr><td>Over</td><td>-1.598***<br>(0.392)</td><td>-1.628***<br>(0.405)</td><td>-1.633***<br>(0.417)</td><td>-1.469***<br>(0.394)</td></tr><tr><td>I(Under > γ<sub>1</sub>)</td><td>0.839***<br>(0.182)</td><td>0.943***<br>(0.181)</td><td>0.984***<br>(0.184)</td><td>0.965***<br>(0.180)</td></tr><tr><td>UnderI(Under ≤ γ<sub>1</sub>)</td><td>0.868**<br>(0.413)</td><td>1.052**<br>(0.432)</td><td>1.121***<br>(0.423)</td><td>1.228***<br>(0.431)</td></tr><tr><td>UnderI(Under > γ<sub>1</sub>)</td><td>-0.671*<br>(0.344)</td><td>-0.788**<br>(0.351)</td><td>-0.764**<br>(0.360)</td><td>-0.660*<br>(0.339)</td></tr><tr><td>FDI</td><td>-0.007<br>(0.013)</td><td>0.001<br>(0.013)</td><td>-0.003<br>(0.013)</td><td>-0.001<br>(0.014)</td></tr><tr><td>Institutions(PCA)</td><td>0.042**<br>(0.019)</td><td>0.027<br>(0.019)</td><td>0.008<br>(0.023)</td><td>0.013<br>(0.025)</td></tr><tr><td>Rents 10%</td><td></td><td>-0.265***<br>(0.098)</td><td></td><td></td></tr><tr><td>Rents 15%</td><td></td><td></td><td>-0.448***<br>(0.108)</td><td></td></tr><tr><td>Rents 20%</td><td></td><td></td><td></td><td>-0.473***<br>(0.095)</td></tr><tr><td>LnGDPPC</td><td>-0.434<br>(0.318)</td><td>-0.523<br>(0.339)</td><td>-0.672**<br>(0.329)</td><td>-0.465<br>(0.365)</td></tr><tr><td>LnGDPPC<sup>2</sup></td><td>0.016<br>(0.019)</td><td>0.026<br>(0.020)</td><td>0.037*<br>(0.020)</td><td>0.025<br>(0.021)</td></tr><tr><td>LnPOP</td><td>0.058*<br>(0.030)</td><td>0.094***<br>(0.032)</td><td>0.075**<br>(0.032)</td><td>0.085**<br>(0.034)</td></tr><tr><td>Africa</td><td>-0.132<br>(0.092)</td><td>-0.061<br>(0.113)</td><td>-0.030<br>(0.109)</td><td>-0.065<br>(0.112)</td></tr><tr><td>Asia</td><td>-0.228**<br>(0.100)</td><td>-0.201*<br>(0.109)</td><td>-0.124<br>(0.113)</td><td>-0.116<br>(0.118)</td></tr><tr><td>Latin America</td><td>-0.036<br>(0.085)</td><td>-0.043<br>(0.099)</td><td>-0.037<br>(0.097)</td><td>-0.041<br>(0.104)</td></tr><tr><td>Constant</td><td>2.301<br>(1.402)</td><td>2.097<br>(1.468)</td><td>2.755*<br>(1.442)</td><td>1.764<br>(1.642)</td></tr><tr><td>No. of observations</td><td>1,271</td><td>1,271</td><td>1,271</td><td>1,271</td></tr><tr><td>Number of countries</td><td>102</td><td>102</td><td>102</td><td>102</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td># Instruments</td><td>79</td><td>80</td><td>80</td><td>80</td></tr><tr><td>Ar(1)_Pvalue</td><td>0.002</td><td>0.002</td><td>0.002</td><td>0.002</td></tr><tr><td>Ar(2)_Pvalue</td><td>0.850</td><td>0.830</td><td>0.840</td><td>0.835</td></tr><tr><td>Hansen_P-value</td><td>0.155</td><td>0.146</td><td>0.152</td><td>0.116</td></tr></tbody></table>

Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. FDI, Over, Under are endogenous.
Once again, the Wald bootstrap test returns a non-linearity effect for *Under* only (Appendix 3).

*Over*'s coefficient is slightly lower than in previous regressions, but remains negative. The coefficient of *Under* still reveals a positive incentive for manufacturing, but becomes negative above the 33% threshold, which is nearly twice as high as the previous threshold (18%). A number of reasons can be put forward to explain this humped relationship, which justifies removing natural resource rents from GDP.

First, for low- and middle-income countries, contractionary effects are likely to occur at high undervaluation rates. Take the case of a sharp real currency devaluation. In keeping with structuralist views, domestic demand falls instantaneously with the stabilization effect (Diaz Alejandro, 1963, 1981). Given the low quality of products and the cost of redirecting flows to new, unknown foreign markets, it is unlikely that increased exports will quickly offset the loss of domestic demand. Second, some countries above the 33% threshold have a floating or dirty floating exchange rate system. With a high level of international capital mobility, this exchange rate regime is subject to high volatility (Rapetti, 2019) and creates episodes of "overshooting" (Dornbusch, 1976). Financial assets adjust instantly to excessive growth in the money supply, so that capital outflows sharply depreciate the external value of the currency. As domestic absorption declines and production slowly shifts to exports, the share of manufacturing is bound to decline despite temporarily high rates of undervaluation. Episodes of overshooting fuel inflation and create a "fear of floating", a major concern for the production of tradables (Calvo and Reinhart, 2002). The volatility of misalignments becomes a source of uncertainty, and the smoothing procedure we use – a three-year moving average – only partially corrects for this phenomenon. All in all, high levels of temporary undervaluation may therefore be correlated with circumstances that discourage manufacturing activity. The third and final explanation supports our methodological approach. Many resource-rich countries are above the 33% threshold of the *Under* variable (Appendix 4). We hypothesize that the negative sign of *Under* is due mainly to model misspecification when rents are not removed from GDP. Partly for mechanical reasons, the
correlation between *Under* and the share of manufacturing industry then becomes negative. Firstly, as rents are part of GDP, other things being equal, they pull this share down. Secondly, per capita GDP with rents does not reflect productivity in the production of non-traditional tradables. Natural resource rents offer the possibility of high prices for non-tradables. If these do not rise accordingly, significant rates of undervaluation occur. Thirdly, these rates of undervaluation are amplified if rents are used to subsidize domestic products that drive down the consumer price index, as is the case of many oil- and gas-producing countries.

Table 6 and Appendix 4 consolidate the 237 annual observations of *Under* distributed above the 33% threshold. They concern 39 countries out of the 102 in the sample (38.2%) and account for 18.6% out of the 1,271 country-year observations. Table 6 breaks down these observations into five classes. The first two columns where the share of manufacturing industry is less than 15% account for 56.5%. Within these two categories, natural resource rents account on average for about 20% or more of GDP. Oil- and gas-producing countries are the countries most often above the *Under* threshold. In these two classes, they represent 71% (96/134) of observations and six of them account for 78.1% of the related country-year episodes (75/96). We provide the country-year observations above the 33% threshold in brackets for these six countries: Saudi Arabia (15), UAE (14), Qatar (13), Iran (12), Oman (11) and Kuwait (10) (See Appendix 4).

<table><caption>Table 6. Characteristics of annual observations above the *Under* threshold of 33% (237 observations over the 2003-2019 period).</caption><thead><tr><th>Manufacturing value-added/GDP (%)</th><th>0-10</th><th>10-15</th><th>15-20</th><th>20-25</th><th>+25</th></tr></thead><tbody><tr><td>Average share of manufacturing (% GDP)</td><td>7.4</td><td>12.1</td><td>17.1</td><td>22.1</td><td>31.5</td></tr><tr><td>Average natural resource rents (% GDP)</td><td>29.1</td><td>17.1</td><td>5.7</td><td>4.5</td><td>11.6</td></tr><tr><td>Average undervaluation level above the threshold (%)</td><td>-46.4</td><td>-47.4</td><td>-45.4</td><td>-38.9</td><td>-41.7</td></tr><tr><td>Country-observations above 33% (out of the 237 observations)</td><td>59</td><td>75</td><td>48</td><td>30</td><td>25</td></tr><tr><td>Average number of years above the threshold (out of 17 years)</td><td>9.6</td><td>7.8</td><td>9</td><td>10</td><td>9.8</td></tr></tbody></table>

Note: Appendix 4, Table 5 for more details on the countries above the threshold.
The classic Dutch disease argument – deindustrialization is the consequence of price distortions that penalize tradable goods – is irrelevant if rents are used to subsidize consumer goods, and lead to currency undervaluation. Administered prices can go hand in hand with cronyism and weak institutions, negatively affecting investment and the production of non-traditional goods. In Azerbaijan for example, the *manat* was 60% undervalued for several years. The average share of manufacturing remained at 5%, with natural resource rents accounting for 31% of GDP. Coady et al. (2006) assess that the distribution of these rents via consumer good subsidies stood at 12.7% of GDP in 2005. Iran is another example with 12 years above the 33% threshold. Up to 2010, the population benefited from subsidies for most basic consumer goods, including administered electricity prices of 1 to 2 US cents per kilowatt-hour, among the lowest in the world, but with frequent power cuts that affected manufacturing activities in particular. Short-sighted strategies maintained persistent dependence on oil activities. Current consumption took precedence over investment and the long-term diversification that conditions the development process. To summarize this second robustness test, we find that the relationship between *Under* and the share of manufacturing is not correctly identified when rents are retained in GDP. Ignoring rents when calculating the misalignment overestimates the threshold for *Under*.

## VI. Conclusion

An increase in the share of manufacturing is key to the development process. In settings of market failures and institutional weaknesses, real exchange rate incentives can be conducive to the emergence of manufactured goods. We explore this role for a large sample of developing and transition countries by estimating dynamic threshold panel models. We modify the Kremer et al. (2013) model to test asymmetries and non-linearities for each of the two misalignment components in the same regression. Absolute purchasing power parity is used as the equilibrium real exchange rate criterion. When we adjust this criterion for the Balassa-Samuelson effect, resource rents are removed from GDP. By eliminating rents, we obtain a better approximation of the level of productivity in non-traditional
tradable goods, and therefore the true level of exchange rate misalignment with respect to these goods. Overvaluation has a linear negative impact on the share of manufacturing, while undervaluation increases it, albeit non-linearly and with a smaller marginal impact above the 18% threshold. These results are controlled for a set of covariates and prove robust when we include the effect of the external terms of trade as an additional determinant of RER to per capita GDP. Ignoring the effect of rents is therefore found to be a source of misleading interpretation of the marginal effect of *Under* above the threshold.

The empirical implications of this study concern exchange rate policy first and, more indirectly, the management of natural resource rents in oil- and gas-producing countries. Results are partly in line with the literature and provide four main lessons. Firstly, overvaluation is a problem in a competitive environment, particularly for unsophisticated tradables, which is the case for most manufactured goods in developing economies. Secondly, undervaluation has a non-linear and lesser impact on the dependent variable than overvaluation. It can be an offsetting factor for institutional and market failures. Thirdly, the marginal impact is still positive and of significant magnitude above the 18% threshold. Fourthly, it is common to associate oil and gas resource rents with high domestic prices and income levels that lead to currency overvaluation. Price distortions then crowd out manufactured tradables. This classic Dutch disease argument ignores situations where rents are used to subsidize current consumption, leading to high percentages of undervaluation.

Cases where this happens are not marginal in developing and transition economies. Although rents drive undervaluation, they also favor extractive economic and political institutions, highlighting the phenomenon of the resource curse. When no correction is made to GDP for the percentage of rents, high rates of undervaluation are associated with a low share of manufacturing. Although political economy arguments make this strategy unlikely, rents would obviously be more effective if managed from an intertemporal perspective combining both savings and investment in basic infrastructure (Collier et al., 2010). This strategy, which would be consistent with the long-term development of the
manufacturing sector, is unlikely to be adopted when the utility function of governments is short-
termist, dominated by the ambition to hold on to power by buying clientelistic social peace.
<table><caption>Appendix 1. List of 102 countries for the empirical analysis and data source.</caption><thead><tr><th></th><th>Africa<br>(30)</th><th>Asia<br>(34)</th><th>Eastern Europe<br>(17)</th><th>Latin America<br>(21)</th></tr></thead><tbody><tr><td>Algeria</td><td>South Africa</td><td>Armenia</td><td>Singapore</td><td>Argentina</td></tr><tr><td>Angola</td><td>Tanzania</td><td>Azerbaijan</td><td>South Korea</td><td>Bolivia</td></tr><tr><td>Botswana</td><td>Togo</td><td>Bangladesh</td><td>Siri Lanka</td><td>Brazil</td></tr><tr><td>Burkina Faso</td><td>Tunisia</td><td>Cambodia</td><td>Tajikistan</td><td>Chile</td></tr><tr><td>Cameroon</td><td>Uganda</td><td>China</td><td>Thailand</td><td>Colombia</td></tr><tr><td>Congo</td><td>Zambia</td><td>Georgia</td><td>Turkey</td><td>Costa Rica</td></tr><tr><td>Côte d'Ivoire</td><td>Zimbabwe</td><td>India</td><td>Turkmenistan</td><td>Dominican Rep</td></tr><tr><td>Djibouti</td><td></td><td>Indonesia</td><td>UAE</td><td>Ecuador</td></tr><tr><td>Egypt</td><td></td><td>Iran</td><td>Uzbekistan</td><td>El Salvador</td></tr><tr><td>Ethiopia</td><td></td><td>Jordan</td><td>Vietnam</td><td>Guatemala</td></tr><tr><td>Ghana</td><td></td><td>Kazakhstan</td><td></td><td>Guyana</td></tr><tr><td>Guinea</td><td></td><td>Kyrgyzstan</td><td></td><td>Honduras</td></tr><tr><td>Kenya</td><td></td><td>Kuwait</td><td></td><td>Jamaica</td></tr><tr><td>Madagascar</td><td></td><td>Laos</td><td></td><td>Mexico</td></tr><tr><td>Malawi</td><td></td><td>Lebanon</td><td></td><td>Nicaragua</td></tr><tr><td>Mali</td><td></td><td>Malaysia</td><td></td><td>Panama</td></tr><tr><td>Morocco</td><td></td><td>Mongolia</td><td></td><td>Paraguay</td></tr><tr><td>Mozambique</td><td></td><td>Oman</td><td></td><td>Peru</td></tr><tr><td>Namibia</td><td></td><td>Nepal</td><td></td><td>Trinidad and Tobago</td></tr><tr><td>Nigeria</td><td></td><td>Pakistan</td><td></td><td>Uruguay</td></tr><tr><td>Rep of Congo</td><td></td><td>Philippines</td><td></td><td>Venezuela</td></tr><tr><td>Rwanda</td><td></td><td>Qatar</td><td></td><td></td></tr><tr><td>Senegal</td><td></td><td>Russia</td><td></td><td></td></tr><tr><td></td><td></td><td>Saudi Arabia</td><td></td><td></td></tr></tbody></table>

List of variables and sources.

Manufsh: value-added of manufacturing (percent of GDP); RER: Real exchange rate; ER nominal bilateral exchange rate of the domestic currency for 1 US dollar; PPP: Purchasing Power Parity conversion factor; Mis: Misalignment of the exchange rate visa-vis the US dollar; POP: population; GDPPC: per capita GDP; Rents 10%: share of natural resource rents in GDP equal to or less than 10%. Similar definition for 15% (Rents 15%) and 20% (Rents 20%); PC1 first component of the Principal Component Analysis for the six economic and political institution items, respectively Government Effectiveness, Rule of Law, Regulatory Quality, Voice and Accountability, Political Stability and Absence of Violence. Source: the source for all the above variables is the World Bank's World Development Indicators. FDI: share of the foreign direct investment in manufacturing in GDP. Source: fDi Market, Financial Times. Africa: Dummy variable for African countries = 1 and 0 otherwise. A similar definition applies for Asia, Europe and Latin America.
<table><caption>Appendix 2. Summary statistics for variables and sources.</caption><thead><tr><th>Variable</th><th>Mean</th><th>Std. Dev.</th><th>min</th><th>max</th><th>N</th></tr></thead><tbody><tr><td>Share of manufacturing (% GDP)</td><td>13.68</td><td>5.93</td><td>1.64</td><td>49.88</td><td>1270</td></tr><tr><td>Misalignments (Table 3) (1)</td><td>-.09</td><td>.24</td><td>-.77</td><td>.79</td><td>1270</td></tr><tr><td>Under</td><td>.22</td><td>.16</td><td>0.0</td><td>.77</td><td>852</td></tr><tr><td>Over</td><td>.16</td><td>.15</td><td>.00</td><td>.79</td><td>418</td></tr><tr><td>Misalignments (Table 4)</td><td>-.10</td><td>.24</td><td>-.77</td><td>.82</td><td>1270</td></tr><tr><td>Under</td><td>.22</td><td>.16</td><td>.00</td><td>.77</td><td>865</td></tr><tr><td>Over</td><td>.18</td><td>.16</td><td>.00</td><td>.82</td><td>405</td></tr><tr><td>Misalignments (Table 5)</td><td>-.10</td><td>.24</td><td>-.83</td><td>.73</td><td>1271</td></tr><tr><td>Under</td><td>.23</td><td>.17</td><td>.00</td><td>.83</td><td>835</td></tr><tr><td>Over</td><td>.15</td><td>.14</td><td>.00</td><td>.73</td><td>436</td></tr><tr><td>Share of FDI in GDP (%)</td><td>1.48</td><td>3.2</td><td>0</td><td>33.1</td><td>1270</td></tr><tr><td>Institutions (PC1)</td><td>0.10</td><td>2.12</td><td>-4.63</td><td>6.61</td><td>1270</td></tr><tr><td>Institutional external instruments (2)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Political Stability</td><td>-.29</td><td>.82</td><td>-2.81</td><td>1.62</td><td>1270</td></tr><tr><td>Regulatory Quality</td><td>-.10</td><td>.72</td><td>-2.16</td><td>2.26</td><td>1270</td></tr><tr><td>Government Effectiveness</td><td>-.13</td><td>.69</td><td>-1.75</td><td>2.44</td><td>1270</td></tr><tr><td>Voice and Accountability</td><td>-.27</td><td>.78</td><td>-2.12</td><td>1.23</td><td>1270</td></tr><tr><td>Control of Corruption</td><td>-.31</td><td>.70</td><td>-1.53</td><td>2.25</td><td>1270</td></tr><tr><td>Rule of Law</td><td>-.27</td><td>.70</td><td>-1.92</td><td>1.88</td><td>1270</td></tr></tbody></table>

Notes: (1) Three series of misalignments and their components are proposed in the paper. Each table in brackets refers to the concept we use in the text. The most standard method is with Table 5. *Under* is the absolute value of misalignment when negative. They refer to the different empirical options explored in Table 3, Table 4 and Table 5. (2) For each of the six indicators, instruments are based on the arithmetic mean for each country's neighbors. Instruments are coded in such a way that higher values represent a better-quality institution. For a given country, institutional external instruments refer to the different WGI measures for its neighboring countries.
Appendix 3. Tests of threshold effects on misalignment components (2003-2019).

<table><thead><tr><th colspan="3">Misalignments corrected for per capita GDP (excluding natural resource rents) and external terms of trade effects</th></tr><tr><th>Null assumption</th><th>Restrictions</th><th>P-value</th></tr></thead><tbody><tr><td>No threshold effects on Under and</td><td>η<sub>10</sub> = 0, η<sub>1L</sub> = η<sub>1H</sub></td><td>0.518</td></tr><tr><td>Over</td><td>η<sub>20</sub> = 0, η<sub>2L</sub> = η<sub>2H</sub></td><td></td></tr><tr><td>Threshold effect on Under only</td><td>η<sub>10</sub> = 0, η<sub>1L</sub> = η<sub>1H</sub></td><td>0.020</td></tr><tr><td>Threshold effect on Over only</td><td>η<sub>20</sub> = 0, η<sub>2L</sub> = η<sub>2H</sub></td><td>0.970</td></tr><tr><th colspan="3">Misalignments corrected for per capita GDP effect</th></tr><tr><td>No threshold effects on Under and</td><td>η<sub>10</sub> = 0, η<sub>1L</sub> = η<sub>1H</sub></td><td>0.019</td></tr><tr><td>Over</td><td>η<sub>20</sub> = 0, η<sub>2L</sub> = η<sub>2H</sub></td><td></td></tr><tr><td>Threshold effect on Under only</td><td>η<sub>10</sub> = 0, η<sub>1L</sub> = η<sub>1H</sub></td><td>0.002</td></tr><tr><td>Threshold effect on Over only</td><td>η<sub>20</sub> = 0, η<sub>2L</sub> = η<sub>2H</sub></td><td>0.240</td></tr></tbody></table>

Note: P-value is the rejection probability for the bootstrapped Wald test, the number of replications B=1000.
<table><caption>Appendix 4. Countries observations above the 33% threshold of undervaluation (2003-2019).</caption><thead><tr><th rowspan="2">Country</th><th>Average share of manufacturing</th><th>Share of resource rents</th><th>Undervaluation above the threshold</th><th>Number of years above the threshold</th></tr><tr><th>2003-2019</th><th>2003-2019</th><th></th><th></th></tr></thead><tbody><tr><td>Albania</td><td>6.15</td><td>1.59</td><td>0.35</td><td>2</td></tr><tr><td>Algeria</td><td>38.43</td><td>26.46</td><td>0.42</td><td>10</td></tr><tr><td>Argentina</td><td>18.11</td><td>5.95</td><td>0.35</td><td>2</td></tr><tr><td>Armenia</td><td>11.2</td><td>1.15</td><td>0.39</td><td>3</td></tr><tr><td>Azerbaijan</td><td>5.19</td><td>31.09</td><td>0.46</td><td>9</td></tr><tr><td>Belarus</td><td>22.91</td><td>1.57</td><td>0.41</td><td>12</td></tr><tr><td>Bolivia</td><td>11.48</td><td>13.11</td><td>0.34</td><td>2</td></tr><tr><td>Egypt</td><td>16.19</td><td>9.49</td><td>0.58</td><td>15</td></tr><tr><td>Georgia</td><td>8.52</td><td>.42</td><td>0.43</td><td>4</td></tr><tr><td>Indonesia</td><td>23.59</td><td>6.38</td><td>0.43</td><td>11</td></tr><tr><td>Iran</td><td>13.23</td><td>24.85</td><td>0.49</td><td>12</td></tr><tr><td>Jordan</td><td>18.26</td><td>1.74</td><td>0.42</td><td>4</td></tr><tr><td>Kazakhstan</td><td>11.56</td><td>23.02</td><td>0.49</td><td>8</td></tr><tr><td>Kuwait</td><td>5.68</td><td>52.02</td><td>0.50</td><td>10</td></tr><tr><td>Kyrgyzstan</td><td>12.95</td><td>3.33</td><td>0.40</td><td>6</td></tr><tr><td>Laos</td><td>9.81</td><td>11.5</td><td>0.43</td><td>4</td></tr><tr><td>Macedonia</td><td>12.45</td><td>1.06</td><td>0.38</td><td>2</td></tr><tr><td>Malaysia</td><td>23.81</td><td>9.53</td><td>0.41</td><td>11</td></tr><tr><td>Moldova</td><td>11.65</td><td>.23</td><td>0.46</td><td>8</td></tr><tr><td>Mongolia</td><td>6.48</td><td>24.2</td><td>0.49</td><td>9</td></tr><tr><td>Oman</td><td>10.01</td><td>41.1</td><td>0.53</td><td>11</td></tr><tr><td>Pakistan</td><td>13.25</td><td>2.05</td><td>0.38</td><td>6</td></tr><tr><td>Paraguay</td><td>17.96</td><td>1.57</td><td>0.57</td><td>1</td></tr><tr><td>Poland</td><td>16.69</td><td>.78</td><td>0.35</td><td>3</td></tr><tr><td>Qatar</td><td>11.52</td><td>32</td><td>0.54</td><td>13</td></tr><tr><td>Russia</td><td>12.46</td><td>11.88</td><td>0.43</td><td>4</td></tr><tr><td>Saudi Arabia</td><td>10.95</td><td>39.7</td><td>0.62</td><td>15</td></tr><tr><td>Serbia</td><td>14.43</td><td>1.31</td><td>0.36</td><td>3</td></tr><tr><td>Singapore</td><td>21.38</td><td>0</td><td>0.38</td><td>11</td></tr><tr><td>Sri Lanka</td><td>17.27</td><td>.12</td><td>0.40</td><td>11</td></tr><tr><td>Sudan</td><td>5.06</td><td>17.42</td><td>0.40</td><td>1</td></tr><tr><td>Thailand</td><td>28.65</td><td>2.4</td><td>0.39</td><td>11</td></tr><tr><td>Tunisia</td><td>14.35</td><td>2.46</td><td>0.40</td><td>2</td></tr><tr><td>Turkey</td><td>17.88</td><td>.28</td><td>0.45</td><td>4</td></tr><tr><td>United Arab Emirates</td><td>8.68</td><td>22.39</td><td>0.48</td><td>14</td></tr><tr><td>Ukraine</td><td>14.26</td><td>3.5</td><td>0.58</td><td>8</td></tr><tr><td>Uzbekistan</td><td>14.77</td><td>14.52</td><td>0.41</td><td>3</td></tr><tr><td>Venezuela</td><td>15.08</td><td>32.28</td><td>0.34</td><td>1</td></tr><tr><td>Vietnam</td><td>19.1</td><td>11.97</td><td>0.38</td><td>2</td></tr></tbody></table>

Note: The first two columns present averages across the entire 2003-2019 observation period. The third column displays the average rate of *Under*, only for years above the 33% threshold. The fourth and last column provides the number of years in case *Under* is above 33%.
ORCID

Patrick Plane https://orcid.org/0000-0002-2138-9399

Mohamed E. Chaffai https://orcid.org/0000-0002-8903-9555

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