Manufacturing growth accelerations in developing countries
Nobuya Haraguchia, Bruno Martoranob, Marco Sanfilippoc, Anirudh Shingald aUNIDO; bMaastricht University/UNU-MERIT; c University of Bari and University of
Antwerp d ICRIER/European University Institute/University of Sussex Abstract
This paper investigates the factors driving manufacturing growth accelerations in a sample of 134 developing countries over the period 1970-2014. Based on a methodology developed by Hausmann et al. (2005), we first identify growth acceleration episodes of manufacturing value added (MVA) by their year of initiation and according to a country’s income classification. We then estimate a probit model to explain what factors predict these MVA growth accelerations. Our results show that human capital and institutions represent contextual factors that favour the growth of manufacturing, together with macroeconomic policies related to investment, and openness to foreign trade and capital. We also find that most of these factors not only foster episodic accelerations of industry, but they contribute as well to a sustained process of industrialization that characterised the process of economic growth of a few successful countries over the last four decades.
JEL Classification: Industrialization; Growth; Developing Countries Keywords: O14; L16
Acknowledgements: We would like to thank the Editor, Andy McKay, and one anonymous referee for their comments. We are also grateful to Alison Cathles for making available data for vocational secondary schooling.We acknowledge financial support by the Government of Japan through the Development Cooperation Trust Fund. The views expressed here are those of the authors and do not reflect the views of the United Nations Industrial Development Organization.
1. Introduction
Industrialization has recently returned at the core of the developmental discourse and industrial policies are gaining renewed interest among national and international policy-makers (Benetrix et al. 2015; Dietsche, 2017). Traditional arguments in favour of industrialization emphasize its role as an engine of economic growth (Szirmai, 2012). This mostly emanates from the capacity of manufacturing to spur productivity growth, structural transformation and innovation, enhancing the process of economic development (Rodrik, 2013). Moreover, fostering the growth of the industrial sector can help laggard economies in the developing world achieve objectives spanning from the employability and livelihoods of a large share of the population to the entry and the upgrading into global value chains (AfDB, 2017).
But, how does industrialization happen? How can policy-makers promote manufacturing growth accelerations in developing countries? This paper contributes to the literature by focusing on the underlying factors explaining the onset of industrialization and its acceleration for 134 developing countries over the period 1970-2014. For this purpose, we apply a methodology developed in a seminal paper on growth accelerations by Hausmann et al. (2005). Unlike Hausmann et al. (2005), our focus is on manufacturing value added (MVA) and not on GDP growth episodes and so we use a different set of control variables, as these are more consistent with our research objective.
Our analysis is developed in two stages: first, we identify growth episodes of MVA in the sample countries by the year of initiation of such episodes and according to their World Bank’s income classification (low-, lower-middle and upper-middle income); second, we estimate a Probit model to explain what factors predict these MVA growth accelerations using a set of economic, institutional and policy-related controls. Our results show that a number of factors correlate with the probability of developing countries to undertake a process of accelerated industrialization. Human capital and institutions represent contextual factors that favour the rapid expansion of manufacturing, together with macroeconomic policies related to investment and openness to foreign trade and capital. Importantly, we also find that most of these factors not only foster episodic accelerations of industry, but they contribute as well to a sustained process of industrialization that characterised the process of economic growth of a few successful countries over the last four decades.
Our findings contribute to an existing literature that has so far almost exclusively looked at the effects of industrialization. An important departure is Rodrik (2013) who shows that manufacturing is the only sector capable of achieving unconditional convergence, based on his analysis covering a large sample of countries. Notable cross-country evidence has emerged to show that industrialization (measured as the share of manufacturing in total value added) is indeed a significant driver of economic growth, this being true for both developing and developed countries (Szirmai and Verspagen, 2015; Cantore et al., 2017). All this links well with evidence on the importance of structural transformation, i.e. moving resources from less to more productive sectors of the economy, for economic growth and development (McMillan et al., 2016). Yet, most recent analyses are also bringing in evidence supporting a pattern of so-called premature deindustrialization, i.e. a quicker shift towards services, currently taking place in some developing countries (Rodrik, 2016; IMF, 2018). So, while there is evidence on the likely consequences of industrialization on growth and structural transformation, and on the way the importance of manufacturing has changed over time both within and across countries (Diao et al., 2017; Haraguchi et al., 2017), little is still known on
how industrialization actually happens. Some literature has dealt with the factors driving (or hampering) industrialization (Haraguchi et al. 2018; Newman et al. 2016), though mostly using a descriptive, or a case study-based, approach1. Relative to existing work, our analysis
adds value by looking at what drives the upsurge of industrialization episodes, in turn providing evidence with potentially useful and important policy implications, especially for developing countries still at early stages of structural transformation.
The rest of the paper is organized as follows. Section 2 develops the analysis to identify manufacturing growth accelerations and provides descriptive evidence. Section 3 describes the empirical analysis, including the methods and the variables adopted. Section 4 presents the results. Section 5 concludes, drawing some policy implications.
2. Identifying growth episodes in manufacturing value added over 1970-2014
We begin by classifying all countries as low-income (LI), lower-middle income (LMI) or upper-middle income (UMI) following the World Bank’s income classification (WBIC) and using the mode of the classification for each country over 1987-20142. This approach has the
advantage of classifying every country on the basis of the income classification that was applicable to it for the majority of the 28 years over 1987-2014, instead of using a beginning-or end-of-sample-year income classification that may have changed over time and hence would not be fully representative. Small economies i.e. those with population less than 1 million at the end of the sample year (i.e. 2014) are excluded from the sample as these are likely to bias the identification of MVA growth episodes and the subsequent empirical analysis.3 The resulting sample has 134 countries of which 105 are classified as LI or LMI
and the remaining 29 countries belong to the UMI category.
In their seminal contribution, Hausmann et al. (2005: 306) consider three conditions that define a per capita GDP growth acceleration episode. We use the same criteria to define MVA growth acceleration episodes, thus:
(1) mvag(t, t+7) >= 3.5% (growth of MVA is rapid)
(2) del_mvag(t,7) = mvag(t,t+7) - mvag(t-7,t) >= 2% (growth of MVA accelerates)
(3) mvat+7 >= max(mvai), i<=t (post-growth output of MVA exceeds pre-episode peak)
where mvag(t,t+7) refers to the average annual growth rate of real manufacturing value added
between years “t” and “t+7”; mvag(t-7,t) is the average annual growth rate of real
manufacturing value added between years “t-7” and “t”; del_mvag(t,7) concerns the change in
the average annual growth rates of real manufacturing value added between years “t” and “t+7” and years “t-7” and “t”; and mva refers to the real manufacturing value added. Thus, according to equation (1), MVA growth is classified as rapid if the average annual growth rate of real MVA in an eight-year period exceeds 3.5 ppa. An MVA growth acceleration is defined in equation (2) by the difference in the average annual growth rates of real MVA between proceeding and preceding eight years exceeding 2 ppa. Finally, according to equation (3), the post-growth output of real MVA must be in excess of the pre-episode peak to qualify as an MVA growth acceleration. Note that Hausmann et al. (2005) use the
1 Some useful studies looking at the (long run) drivers of industrialization and industrial policies are, for instance, Lane (2017) on South Korea and Liu (2018) on China.
2 1987 is the first year for which we have information on countries’ income status from the World Bank. 3 This is consistent with existing literature (for e.g. see Hausmann et al. 2005 and Haraguchi et al. 2018).
3.5% and 2% thresholds in equations (1) and (2), respectively, as these correspond roughly to the long-term average growth rate of the OECD countries and therefore signify rates at which developing countries may need to grow to catch up with the OECD. However, our context is different since the focus of our research is MVA. Therefore, we calculate annual growth rates of real (in constant USD in 2005) manufacturing value added (mvag) for each country over 1970-2014. Average mvag for all countries in the sample over 1970-2014 is 4.9% in the overall sample. However, in our analysis, we use different mvag thresholds in equation (1) by using the mean mvag of the LI (5.8%), LMI (4.2%) and UMI (4.4%) income country samples in each respective case to generate condition (1) by WBIC. Thus, equation (1) is modified as follows:
(1.1) mvag(t,t+7) >= 5.8% (growth of MVA is rapid for low-income countries)
(1.2) mvag(t,t+7) >= 4.2% (growth of MVA is rapid for lower middle-income countries)
(1.3) mvag(t,t+7) >= 4.4% (growth of MVA is rapid for upper middle-income countries)
A MVA growth acceleration episode that satisfies the three conditions in equations (1)-(3) is further characterized as a sustained growth episode if it satisfies an additional fourth condition following IMF (2012):
(4) mvag(t+7, t+14) >= 3%
Thus, according to equation (4), if real MVA growth in the eighth year of a MVA growth episode is in excess of 3 ppa, then the MVA growth acceleration episode is characterized as a sustained MVA growth episode. This filtering strategy yields a full list of growth episodes of MVA from which we identify the beginning of a growth episode taking the first year (t) of a growth episode for each country and every (t+7) year after that.
The first three conditions yield a large number of MVA growth accelerations – 135 episodes in all4, of which 119 are found to be sustained, following the requirement of condition (4).
Table A1 in the Appendix reports all of these MVA growth episodes along with their year of initiation; each country’s WBIC; the average growth of MVA in the eight years preceding (mvag(t-7,t)) and proceeding (mvag(t,t+7)) the initiation of the growth episode; the difference
between the post-and pre-acceleration growth (del_mvag); and a variable indicating whether the MVA growth episode was sustained i.e. whether mvag(t+7,t+14) >= 3%. Table 1 reports the
number of MVA growth episodes (G.E.) and those sustained (S.G.E.), based on the year of initiation of the G.E., grouped by WBIC, continent and decade. Data show that the majority
4 A potential issue is that MVA growth accelerations could be overlapping with GDP growth episodes, making our analysis hardly distinguishable from that by Hausman et al. (2005). Following the suggestion of an anonymous referee, we thus also used the Hausman et al. (2005) conditions to determine GDP growth acceleration episodes. Equation (1) was again modified according to WBIC as in the present paper, using 1970-2004 average GDP growth rates of 3.7% for LICs, 4.0% for LMICs and 3.6% for UMICs. In doing so, we found that only 59 of the 135 MVA growth accelerations were also identified as GDP growth accelerations (these are denoted by “1” under column “MVA g.e. also GDP g.e.” in Table A1). This finding obviates concerns about collinearity between GDP and MVA growth episodes. In another robustness check, we found that only 31 of the 135 MVA growth accelerations were associated with post-crisis (war, calamities) period of “reconstruction” (these are denoted by “1” under column “post crisis period” in Table A1) as opposed to “natural” spurts in manufacturing activity. The policy recommendations emanating from the empirical analysis that follows are also robust to post-crisis episodes of reconstruction as we control for a country experiencing war in the three years preceding the start of an MVA growth acceleration in the sensitivity analysis in Section 4.2 and in the results reported in Table A4 in the appendix. Unreported regression results were also found to be qualitatively similar to our main findings if we simply excluded countries experiencing war in the three years preceding the start of an MVA growth acceleration from analysis.
of LI and LMI MVA growth episodes were observed in Africa and Asia. For UMI countries, Europe had the largest number of MVA growth episodes during the 1990s.
Table 1: Number of MVA growth episodes by decade and continent
Low income Lower-middle income Upper-middle income
Decade Continent #G.E. #S.G.E. #G.E. #S.G.E. #G.E. #S.G.E.
1970s Africa 7 6 2 1 Asia 3 3 2 2 1 0 1980s Africa 2 2 2 2 1 1 Asia 6 6 6 6 3 3 Australia 1 Europe 1 1 North America 3 3 3 2 South America 1 1 1 1 1990s Africa 13 11 3 3 1 1 Asia 8 7 5 5 2 2 Europe 9 6 7 6 North America 3 1 1 1 South America 1 1 1 0 2000s Africa 8 8 2 2 2 2 Asia 3 3 8 8 1 1 Australia 1 1 Europe 2 1 1 1 North America 1 1 1 1 South America 3 2 3 3
In addition to the sheer number of MVA growth accelerations, the magnitude of the typical acceleration is also striking. Conditional on a growth acceleration of at least 2 ppa, the average MVA growth acceleration is found to be 6.6 ppa for LI and LMI countries (excluding countries like Benin (1978), Gambia (1978) and Liberia (1999) that were outliers in these years due to epidemics/civil strife and 11.5 ppa with these outliers) and 6.0 ppa for UMI countries. Moreover, within each WBIC, there are several episodes of sustained MVA growth acceleration well above the respective averages: for instance, Jordan and Lesotho (1978), Cambodia (1981), Bolivia (1987), Afghanistan (1996) and Azerbaijan (2000) amongst LI and LMI countries and Lebanon (1983), Lithuania (1999) and Botswana (2004) amongst UMI countries.
Following Hausmann et al. (2005), we also estimate the (unconditional) probability of a sustained MVA growth acceleration by dividing the number of sustained MVA growth episodes by the number of country-years in which such an episode could have occurred. The latter is calculated by summing up all the country-years in our sample and eliminating the
first and last 7 years for each country (since an episode as defined could not occur during these years) as well as a 7-year window after the occurrence of each episode (since our filter takes this period as belonging to the same episode). Applying this rule, we obtain 3,321 possible occasions in which a sustained MVA growth episode could have occurred. Dividing our 119 sustained MVA growth episodes by 3,321, we get that the average probability of a sustained MVA growth transition in our sample as about 3.6 percent per year. This means that a typical developing country in our sample would have about a one-third chance of experiencing a sustained MVA growth transition at some point in any given decade.
As a final exercise, we try to identify from the larger group of countries that have experienced MVA growth accelerations, a sub-set of those that we will define in the rest of the analysis as “successful industrializers”. Our definition of “successful industrializers” is stricter than that based only on accelerations, and is based on meeting the following conditions:
1. We select only those countries that experienced — over our sample period — at least 2 industrial growth accelerations;
2. The growth accelerations should be classified as sustained, i.e. should satisfy condition n. 4 as stated above;
3. Successful industrializers should also experience a process of consolidating their manufacturing sector. To satisfy this additional criterion, the average share of MVA in GDP for the years following the end of the last acceleration should be higher than the world’s average share of MVA in GDP during the time period 1970-2014 (13.7%). These conditions enable us to a get to a smaller list of “successful industrializers”, which is reported in Table 2, together with some summary statistics. The inclusion of the first two criteria defines the longer-term dimension, identifying a process of continuous growth of the manufacturing sector over most of the years included in our sample. In addition, the last criterion is particularly relevant since it allows to exclude those countries for which the manufacturing sector represents a small and not significant share of the economy or for which exceptional manufacturing growth was linked to growth spillovers from other sectors, especially the primary, alluding to a lack of structural transformation.
It is worth noticing that while basically all countries in Table 2 experienced a surge in their share of manufacturing in GDP following the last acceleration, China experienced a decline. This is the result of a very high pre-first-acceleration manufacturing share in GDP, which despite a sustained pattern of manufacturing growth since the 1990s, could not be bettered post-last-acceleration. In fact, manufacturing in China grew slightly lower than the economy as a whole and was outperformed by the growth of the services sector, as in a typical process of structural transformation. Even so, the country’s mean MVA share in GDP post-last-acceleration is still 17.6 pp higher than the 13.7% world average share of MVA in GDP over the time period of our analysis.
Table 2: List of successful countries
Country Incomegroup
MEAN MVA/GDP before first acceleration MEAN MVA/GDP after last acceleration Difference pre/post (pp) Difference with WORLD average MVA/GDP (pp) Bangladesh L 12.82% 19.21% 6.39 5.51 Cambodia L 7.53% 18.97% 11.45 5.27 China LM 36.58% 31.29% -5.29 17.59 India L 13.78% 18.53% 4.75 4.83
Iran (Islamic Republic of) LM 3.75% 13.89% 10.14 0.19
Jordan LM 8.08% 15.36% 7.29 1.66
Lesotho L 3.73% 16.49% 12.76 2.79
Myanmar L 7.62% 17.40% 9.78 3.70
Poland UM 8.37% 21.31% 12.94 7.61
Sri Lanka LM 16.83% 19.36% 2.52 5.66
Trinidad and Tobago UM 12.62% 23.53% 10.91 9.83
Turkey LM 13.18% 17.65% 4.47 3.95
Viet Nam L 13.30% 19.34% 6.04 5.64
Figure 1 provides a graphical illustration of the pattern of structural transformation towards manufacturing experienced by some of the countries listed in Table 2, showing that they have generally experienced a declining trend in the share of the primary sector.
Figure 1: Changes in Agriculture and Mining VA vs Manufacturing VA (% GDP) in selected successful industrializers 1970197319761979198219851988199119941997200020032006200920122015 0 10 20 30 40 50 60 Bangladesh agr_min manuf 1970197319761979198219851988199119941997200020032006200920122015 0 10 20 30 40 50 60 Cambodia agr_min manuf 1970197319761979198219851988199119941997200020032006200920122015 0 10 20 30 40 50 60 70 Myanmar agr_min manuf 1970197319761979198219851988199119941997200020032006200920122015 0 5 10 15 20 25 30 35 40 45 50 Viet Nam agr_min manuf
Source: Authors’ calculations based on UN NAS
3. Predicting MVA growth transitions 3.1 Empirical analysis
We estimate a Probit model to explain the factors affecting the start of a MVA growth acceleration. Our dependent variable (MVAGE) is a dummy that takes the value of 1 in a
three-year period that is centered around the first year of the MVA growth episode (i.e. at t-1, t, and t+1 where t refers to the initiation of the MVA growth episode). This has two practical implications for our analysis. First, for each country experiencing an MVA growth episode, we keep information on the years around the initiation of the episode. And second, this also means that, for each episode recorded in a given year and in a given country, our comparison group consists of all countries that have not experienced an MVA growth episode in that same year.
The full sample comprises the 134 countries for which data on MVA are available over 1970-2014 and includes both countries that have and have not experienced MVA growth episodes.
However, we need to make two adjustments to the full sample before undertaking the empirical analysis. First, we drop the first and last seven years of data since MVA growth episodes as defined by condition (2) could not have been calculated for each country over these time spans. This means that, for the purpose of empirical analysis, the effective coverage of our dataset reduces to the period 1977-2007. Second, we drop all data pertaining to years t+2 through t+7 of an MVA growth episode, since this time period is considered to be a part of the same growth episode.
Our baseline estimating equation is the following: Pr (MV A¿it>0)=β
∑
Zit−1+θt+uitwhere i and t indicate the country and the year, respectively; MVAGE is the binary dummy
denoting the (country-specific) MVA growth episode at time t-1, t, and t+1; Zit-1 is the vector
of control variables (defined below); t are the year fixed effects used to control for country
invariant shocks (such as global financial crises; international commodity price fluctuations) that might have affected the timing of MVA growth acceleration; and uit is the idiosyncratic
error term. When the model is estimated for all countries included in the sample we also add income-level fixed effects to account for group specific patterns. Note that all the control variables included in Z have been computed as 3-year averages (covering the periods 1 to t-3) to account for both initial conditions and the potential persistence of some factors in affecting manufacturing growth.5 All models are estimated by a standard Probit, and to ease
interpretation of the results, all tables report marginal effects. It is also important to clarify at the outset that we cannot rule out potential endogeneity of some of the controls (though using lagged values should mitigate reverse causality) or a potential omitted variable bias. Hence, while we use data to unearth and describe some hitherto unknown relationships, we are careful to avoid interpreting these as causal effects. However, we think that the relationships uncovered are sufficiently interesting and with important policy implications to justify our analysis.
3.2 Control variables
The choice of the controls variables reflects an additional difference between our work and that of Hausmann et al. (2005), who have mostly focussed on country specific economic and policy-related shocks that in their opinion could have significantly affected the upsurge of a growth acceleration. However, while these types of shock could certainly affect manufacturing growth, they are more general in scope. We therefore include a set of economic and policy characteristics that, following the existing literature, are better qualified to capture specific changes in industrial growth in developing countries.
We start by including the log of real GDP per capita (lgdp_pc) to capture differences in the initial levels of income that are likely to be correlated with the speed with which each country catches up with the industrial frontier. Rodrik (2013) has recently documented the size of the patterns of conditional and unconditional convergence, showing that the latter are particularly relevant within the manufacturing sector. This is also relevant when undertaking the analysis by levels of income, given that within each group the long-time dimension of our data can hide heterogeneity in the patterns of development across countries and within income groups.
5 Reassuringly, we find that our results remain robust to using different lag structures of the control variables including by using 5-year averages or lagging the variables by 3 or 5 years. All these additional results are not reported for reason of space, but are available upon request to the authors.
We also account for the role of investment, whose role in boosting domestic productive capacity is well-known in the literature (Weiss and Clara, 2016). A related factor is represented by investment in human capital, which can influence the onset of industrialization fostering the accumulation of knowledge and competences. To capture this effect, we include a variable representing human capital endowments (hc) measured by the average number of years of schooling of both males and females aged 15 years and above, derived from the Barro-Lee (2013) dataset6.
Macroeconomic policies have strong implications on the pattern of industrial development and structural transformation undertaken by a country (Guzman et al. 2017). We thus include the management of the real exchange rate (reer) to account for the implications of currency devaluation, an instrument that has been frequently used to support the manufacturing sector (Rodrik, 2007). We also include an indicator of capital account openness, with the idea that different attitudes towards liberalization might affect differently the process of industrialization, either positively by lowering interest rates or negatively by increasing volatility and uncertainty in the financial market (Chari et al., 2012). For this purpose, we use the Chinn and Ito index (kaopen) that measures a country’s degree of capital account openness, based on original information included in the IMF’s reports on exchange arrangements and exchange restrictions, and measures the extent and intensity of capital controls in most countries worldwide (see Chinn and Ito, 2006, for a description of the index). An important dimension, also emphasized in related studies on growth accelerations (Hausmann et al. 2005; IMF, 2012), is represented by changes of the external environment. Therefore, we include an index of the commodity terms of trade (ToT) that is also likely to affect the timing of MVA growth accelerations.7
The quality of political institutions and the degree of political changes are also likely to affect the onset of manufacturing growth, especially in developing countries where the investment climate often represents a constraint to private sector development. While previous research has largely argued in favour of a stronger effect (on growth and development) of governance indicators (such as the rule of law) over measures of political regimes (Acemoglu and Robinson, 2005), we opt for the latter due to their availability over much longer time spans, and their high correlation with the former. We therefore approximate the quality of political institutions by means of a variable (pol) ranging from 0 (least democratic) to 10 (most democratic) produced by combining Freedom House and Polity indicators8.
Figure 2 compares average values of some of the explanatory variables selected for countries that experienced accelerations and those that did not, thereby providing a preliminary sketch of what we should expect from the multivariate analysis. Generally, countries that experienced manufacturing accelerations (in any period) were characterised by lower exchange rates and more restrictive capital accounts, as well as by higher degrees of democracy and slightly higher levels of investment.
6 As the data are originally reported in 5-year spans, we follow standard practice and interpolate them to obtain full coverage for all countries included in Barro-Lee’s (2013) original sample.
7 This variable comes from Spatafora and Tytell (2009) and is defined as the ratio of weighted real commodity export prices to weighted real commodity import prices, where the weights are a country’s export and import shares in GDP, respectively.
8 This variable is extracted from Teorell et al. (2016). More specifically, the original variables, i.e. Freedom House and Polity indicators, are first transformed to a 0-10 scale. Then, a new variable is built, based on the simple average of the two.
Figure 2. Average value selected variables, by presence of acceleration
Source: Authors’ calculations
Table 3 presents a description of the variables and their source, while Table A2 in the appendix provides descriptive statistics for the entire sample.
Table 3. Variable description
Variable Description Source
lgdp_pc Log of real GDP per capita UN NAS
gfcf_gdp Gross fixed capital formation as a share of GDP UNIDO
hc Average numbers of years of schooling (m/f, 15+) Barro and Lee (2013)
reer Index of real effective exchange rate Bruegel9
kaopen Indicator of capital account openness Chinn and Ito (2008)
ToT Index of commodity terms of trade Spatafora and Tytell (2009)
pol The quality of Democracy Freedom House extracted fromTeorell et al. (2016)
4. Regression results on the determinants of manufacturing growth accelerations Table 4 reports the results of empirical analysis on the determinants of manufacturing growth accelerations. Marginal effects are reported in all tables, together with robust standard errors. Columns 1 to 4 include the specification based on the broader definition of an acceleration (satisfying equations 1-3 in the identification exercise) for the group of developing countries
as a whole and for the sub-groups of LI, LMI or UMI countries, respectively. Overall, the way most of the control variables enter in all the regressions is consistent, but there are still some interesting differences in both the signs and magnitude of the coefficients across the income groups considered (Table 4).
Taking the results for the whole group of countries as a reference (column 1), we show that the starting point matters to predict growth accelerations in manufacturing. The coefficient of per capita GDP, measuring cross country differences in the levels of income in the years preceding an acceleration, is in fact negative and statistically significant. This result is also found to be consistent across different income groups, and stronger in magnitude for the groups of LI and UMI.
In contrast, we do not find statistically significant evidence for the level of investment in GDP being linked to manufacturing growth in the overall sample, though the sign of the coefficient is positive as expected (column 1). Being a long-term driver of growth, this variable might not necessarily capture sudden changes in the pattern of industrialization, which is what we investigate in this first part of the analysis. Yet, the investment coefficient becomes statistically significant for LI countries, a group for which it is possible that new investment is fostering industrialization even at low levels of capital formation. As expected, we also find a strong effect of endowment in human capital (measured by the years of education in the working population) for all the groups considered (though it is not statistically significant for the group of LI countries). This result highlights once more the importance of human capital as a source of knowledge, which is a necessary condition to allow taking advantage of technological changes and kicking off the process of industrialization.
Table 4 shows also that macroeconomic policies can play a key role in explaining manufacturing growth accelerations. The adoption of an undervalued exchange rate regime tends to increase the competitiveness of the manufacturing sector. Indeed, the role of exchange rate as an effective industrial policy tool is widely recognised (Frenkel and Rapetti, 2014; Rodrik, 2008). Countries have largely used competitive exchange rates to promote industrialization, especially in tradable industries with high learning spillovers, as re-emphasized in recent work by Guzman et al. (2017). This is what we find for our overall sample, but not for the individual income groups. We also find a negative and significant relation between capital openness and manufacturing growth acceleration. Episodes of MVA growth acceleration are associated with policy selectivity towards foreign capital, especially in lower income countries10. This seems consistent with the theory, which has highlighted
some unintended consequence of large financial inflows, especially in countries lacking solid and credible financial institutions (Taylor, 2000; Cornia and Martorano, 2012).
Factors related to the international integration of countries may also play a strong role in explaining growth accelerations in the manufacturing sector. Indeed, primary commodity exporters experiencing favourable terms of trade are less likely to embark on accelerated growth in manufacturing (as also found by Hausmann et al. 2005). Finally, our indicator of institutional development, which measures the level of democracy, has a positive and statistically significant coefficient, confirming once more the role of good institutions in the pattern of development of laggard economies. The existence of democratic and high-quality institutions tends to create the suitable environment and bundle of positive incentives for
10 Upper middle income countries are generally more open, and so less heterogeneous, in terms of capital accounts.
starting entrepreneurial activities, consolidating existing ones and therefore promoting the process of manufacturing growth acceleration. It must be noted, however, that this coefficient is no longer statistically significant for both the LI and the UMI countries (Table 4).
But, to what extent do these findings characterise episodes of manufacturing, rather than overall economic growth? In Table A1 we have already shown that a contemporaneous occurrence of MVA and GDP growth accelerations is unlikely. As an additional test, we replicate our main results using a different definition of the dependent variable so that it now identifies a GDP growth episode, following Hausmann et al. (2005). Results of this exercise, reported in Table A3 in the appendix, show that there are indeed some differences in the two sets of findings. Variables such as exchange rate, terms of trade and even political stability play a different role in explaining GDP growth accelerations and are in fact more relevant in the context of MVA accelerations; in contrast, domestic investment only seems to explain GDP growth episodes. These differences further corroborate the relevance of our analysis, and enforce the potential implications emanating from it.
Table 4. Main Results, drivers of MVA accelerations
(1) (2) (3) (4)
Main LI LMI UMI
lgdp_pc -0.10169*** -0.24258*** -0.06023*** -0.17529* [0.020] [0.042] [0.022] [0.091] gfcf_gdp 0.18919 0.53342*** -0.07626 -0.23612 [0.126] [0.127] [0.200] [0.218] hc 0.02354*** 0.00466 0.01992*** 0.06186* [0.005] [0.010] [0.008] [0.037] reer -0.00002** 0.00033 -0.00003 0.00053 [0.000] [0.000] [0.000] [0.000] kaopen -0.03304*** -0.06089*** -0.06411*** 0.00088 [0.008] [0.021] [0.014] [0.007] tot -1.07739*** -0.66067 -1.32416*** -1.46776* [0.212] [0.458] [0.291] [0.853] pol 0.00960*** 0.00903 0.01007** 0.00929 [0.004] [0.007] [0.005] [0.008] Observations 1,599 540 672 319
Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1
Note: All specifications include year fixed effects. Specification reported in column (1) includes as well income group fixed effects.
4.1 Alternative measures of manufacturing growth accelerations
In this sub-section, we examine the sensitivity of our main results to different definitions of the dependent variable. We do this by focusing, respectively, on (i) countries that experienced sustained accelerations and (ii) the group of “successful industrializers”.
(i) Determinants of sustained acceleration
As reported in Section 2, 119 out of 135 episodes of manufacturing growth accelerations have been identified as sustained growth episodes i.e. cases in which MVA growth in the
eight-year period was equal or higher than 3 ppa. Therefore, as a further step in our analysis, we test our model focusing only on these cases of acceleration.
Table 5 shows that while these results are very similar to the baseline specification, there are some interesting exceptions. In contrast to the baseline results, we find statistically significant evidence for levels of investment in GDP being linked to sustained episodes of MVA growth acceleration in the overall sample (Table 5). This means that the level of investment matters to move from episodic accelerations to a sustained path of industrialization. Surprisingly, human capital is no longer a significant factor in explaining sustained cases of accelerations in low and middle-income countries while its coefficient is still positive and statistically significant for the overall sample and in the case of upper and middle-income countries (Table 5). In addition, the coefficient of REER is positive and statistically significant in the sample of LI countries (Table 5). Indeed, an overvalued exchange rate may be helpful in promoting the development of the “infant industry” decreasing the cost of imported machinery and reducing food prices (and so real wages in the industrial sector). However, this strategy may not be sustainable in the long-term as shown by the case of Latin American countries in the 1960s and 1970s (Cornia and Uvalic, 2012). Lastly, the index of democracy turns statistically insignificant in the case of lower and middle-income countries and positive and significant in the case of upper and middle-income countries (Table 5).
Table 5. Determinants of sustained acceleration
(1) (2) (3) (4)
Main LI LMI UMI
lgdp_pc -0.09002*** -0.18680*** -0.06392*** -0.13101*** [0.018] [0.037] [0.019] [0.037] gfcf_gdp 0.27751*** 0.48914*** 0.08223 -0.00336 [0.093] [0.108] [0.161] [0.123] hc 0.01798*** 0.01255 0.00851 0.04855*** [0.005] [0.008] [0.006] [0.010] reer -0.00001* 0.00050** -0.00004 0.00042 [0.000] [0.000] [0.000] [0.000] kaopen -0.03242*** -0.06274*** -0.04447*** -0.00459 [0.008] [0.019] [0.012] [0.006] tot -1.04457*** -0.59630 -1.12347*** -1.46801*** [0.181] [0.412] [0.241] [0.338] pol 0.00646** 0.00531 0.00517 0.01366*** [0.003] [0.006] [0.004] [0.004] Observation s 1,656 560 696 329
Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1
Note: All specifications include year fixed effects. Specification reported in column (1) includes as well income group fixed effects.
(ii) Determinants of long-run successful industrialization
In this section, we focus on the sub-group of countries that we have defined “successful industrializers” according to the additional criteria discussed in Section 1. Results are reported in Table 6. From a methodological point of view, the main difference with the baseline models is that the dependent variable now takes the value of 1 to identify a
“successful industrializer” over the whole sample period considered. The comparison group therefore includes countries that cannot be classified as “successful”, irrespective of whether they have experienced an MVA growth acceleration or not at any time.
The results remain largely consistent with the previous ones emphasizing once more the role that some structural characteristics and policies have in affecting manufacturing growth also in the longer run. This said, there are a few differences worth noticing. First, the coefficient of per capita GDP, measuring cross country differences in the level of income in the years preceding an acceleration, is not significant for the whole sample and it turns positive and statistically significant in the case of UMI countries (Table 6). Second, the coefficient of investment now becomes a statistically significant predictor of MVA growth for the sample as a whole, as well as for the groups of countries at lower income (Table 6). This result seems to suggest that it is necessary to build a certain level of investment to make sure that the process of industrialization becomes sustainable. Third, the coefficient of the exchange rate is no longer statistically significant in column 2, but it keeps being negative and significant for LI and UMI countries.
Table 6. Results, group of successful countries
(1) (2) (3) (4)
Main LI LMI UMI
lgdp_pc -0.01700 -0.07040*** 0.03125 0.05366* [0.016] [0.023] [0.025] [0.030] gfcf_gdp 0.51215*** 0.35919*** 0.41695*** -0.12798 [0.068] [0.096] [0.130] [0.111] hc 0.03603*** 0.01392*** 0.04009*** 0.02193*** [0.004] [0.004] [0.007] [0.005] reer -0.00000 -0.00058*** -0.00001 -0.00061*** [0.000] [0.000] [0.000] [0.000] kaopen -0.03026*** -0.02385*** -0.01483 -0.00351 [0.007] [0.008] [0.010] [0.005] tot -1.07886*** 0.99911***- -0.43493** 0.57545*** -[0.153] [0.272] [0.200] [0.132] pol 0.01303*** 0.01718*** -0.01611*** 0.01305*** [0.003] [0.004] [0.005] [0.003] Observations 2,055 696 875 484
Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1
Note: All specifications include year fixed effects. Specification reported in column (1) includes as well income group fixed effects.
4.2 Further sensitivity analysis
In further regressions, reported in Table A4 in the appendix, we add some additional controls to our main specification to capture further dimensions that can affect manufacturing growth. First, we show that results remain unaffected when controlling for the initial share of manufacturing value added on GDP (lmva_gdp), whose coefficient is negative and significant (with the exception of UMI, for which it is not statistically significant but still negative). This
result, therefore, suggests that countries experiencing an acceleration are those starting with lower levels of industrialization. Second, we consider the level of labour costs (lwage_pc) as an important factor in shaping the process of industrialization, especially for laggard economies. As emphasized in recent work by Gelb et al. (2017: 7): “..the “footloose” industries that have typically served as the entry point for industrialization generally involve labor-intensive segments of industrial value chains”, which are normally found in poor countries mostly endowed with cheap labour. Using a constructed proxy for average wage level based on the work of Haraguchi et al. (2018)11, we confirm that low labour costs
constitute an important factor in determining episodes of manufacturing growth acceleration. The coefficient is particularly strong (and significant) for LMI, a group in fact including some of the manufacturing powerhouses in GVCs. Third, we introduce a variable measuring geographic centrality (geo) – constructed as the sum of inverse distance between each country and its partners (with data from CEPII) - since geography and access to (foreign) markets is an important channel to stimulate demand for locally produced goods. The variable is positively related to manufacturing growth, confirming the importance of good geography in shaping the developmental potential of countries.
Table A5 in the appendix shows some additional results. In columns 1-4, we include the degree of ethnic fragmentation of the society. This variable, which is sourced from Alesina et al. (2013), denotes the probability that two individuals randomly selected in the population do not share the same ethnic characteristics (high values mean more fragmentation). Less fragmentation and higher cohesion are key ingredients in building social networks which may play a key role in overcoming initial costs of industrialization. Results in Table A5 confirm that that less fragmentation is related to an increase in the probability of promoting industrial accelerations. Looking at the different income sub-groups, this result is confirmed for those at lower income, which are unsurprisingly among the most fragmented and less industrialized countries (including most of Sub-Saharan African countries and Afghanistan, Pakistan amongst others).
In columns 5-8, we add a dummy taking the value of 1 if the country experienced a (international, civil or ethnic) war in the three years preceding the start of a growth acceleration (and independently on its intensity). This information is originally from the conflict list produced by the Center for Systemic Peace (CSP) Major Episodes of Political Violence (MEPV),1946–2013, and is extracted from the database provided by Teorell et al. (2016). We show that having experienced a war results in a lower probability of embarking upon a manufacturing acceleration, as this is likely to seriously affect both the productive capacity of a country and the participation of its young population in the labour market. The result is especially strong for the group of LMI countries, which includes countries (such as Iran, Iraq, Angola and Guatemala) that have experienced episodes of wars over the last decades.
We also include a measure of income inequality (gini). The idea is that high disparities may reduce the room for policy due to the presence of class-based power structures. However, high inequality may also hinder the development of domestic markets and the process of industrialization (Murphy et al., 1989). Our results confirm that manufacturing growth accelerations are less likely to kick start in more unequal societies, where local demand is not sustained by large income discrimination. In fact, the effect seems to be mainly driven by
11 This variable is constructed using available indicators on the shares of manufacturing wages in total and on the total number of persons employed in manufacturing (original data are from Haraguchi et al., 2017).
UMI countries, a level of income that is more likely to be related to high levels of inequality, and at which we could find some of the most unequal economies, i.e. Brazil and South Africa.
As a final exercise, we also made some changes in our core specification by replacing some variables with suitable alternatives. Results are reported in Table A6 in the appendix. First, we replace the level of per capita income with an index of total factor productivity (TFP) retrieved from the Penn World Tables. The negative and significant coefficient confirms that accelerations are more likely to happen in economies with lower levels of development and productivity (Table A6). Second, we replace the human capital control introducing a variable measuring the average number of years of vocational training. The information is extracted from a new database built by Cathles (2016) and is based on two main sources of data i.e. secondary schooling information extracted from the Barro and Lee (2013) dataset and enrolment data from UNESCO (Cathles, 2016)12. Even if this variable may be interpreted
with caution, it is relevant to note that it reports a marginal effect that is higher than the coefficient of human capital used in our baseline regression. An additional year of vocational training results in a probability of experiencing a manufacturing acceleration of 27% (against a mere 2.3% resulting from an additional year of education). This is an important finding, that merits more investigation in future research. Third, the index of commodity terms of trade is replaced by the standard measure of trade openness (export plus import on GDP). The coefficient is positive and statistically significant showing that greater integration in international trade may promote episodes of manufacturing growth accelerations.
5. Conclusions
This paper identifies episodes of MVA growth accelerations in developing countries. Following an approach similar to that developed by Hausmann et al. (2005), we identify 135 episodes of which 119 are found to be sustained, using information for 134 developing countries over the period 1970-2014. We also estimate that a typical developing country would have about a one-third chance of experiencing a sustained MVA growth transition at some point in any given decade. Lastly, we also identify a number of countries that could be defined as successful industrializers, and include those that achieved both a sustained process of industrial growth and levels of manufacturing in total value added greater than the world average.
Results of empirical analysis on the determinants of manufacturing accelerations provide some insights into how industrialization happens, providing useful inputs to countries still behind in the industrialization ladder. Overall, our paper suggests which factor endowments and policies may play a key role in the process of manufacturing growth acceleration and therefore in economic development. Importantly, our research also shows that most of the drivers that we identify do not only foster episodic accelerations of industry, but contribute as
12 Data are mainly based on UNESCO (see Cathles, 2016, for a more detailed definition). In UNESCO enrolment data, the two forms of education (secondary and vocational) are recorded separately, with the following definitions (both taken from p. 194 of UNESCO, 1969, as referred to in Cathles, 2016): “General education. Data presented under this heading cover academic secondary schools and academic secondary classes attached to institutions at other levels. Some vocational education may be included since a number of schools in certain countries offer courses combining the academic and vocational types of education..”. “Vocational education. Data presented under this heading cover all vocational education at the second level, e.g. technical, industrial, arts and crafts, trade, commercial, agriculture, fishery, forestry, domestic science, music, fine arts, etc., provided in vocational schools as well as in departments and classes attached to institutions whose main concern is education of other types and/or levels. Correspondence courses have generally been excluded, but various part-time courses, sometimes of very short duration, are included...”.
well to a sustained process of industrialization that characterised the process of economic growth in a few successful countries over the last four decades. While most of the factors we identify are likely to affect economic growth more generally, some are specific to kicking off industrialization, especially in low-income countries. These inter alia include specialized and competitive labour force, macroeconomic policies and policy to promote low inequality.
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Table A1: All MVA growth episodes (n=135, WBIC-specific thresholds used to classify rapid growth)
Country growth acc'n (t)Initial year of WBI_C acc'n period (t-Growth in pre-7,t) Growth in post-acc'n period (t, t+7) Difference between post-and pre-acc'n growth Whether growth episode sustained
MVA g.e. also GDP g.e. post-crisis period (was GDP negative the year before) Afghanistan 1996 L -10.9 19.2 30.2 1 Albania 1998 LM -8.1 18.9 27.1 1 1 1 Algeria 2006 LM 2.7 5.9 3.2 1 Angola 1997 LM -4.5 8.2 12.7 1 1 Argentina 1990 UM -0.2 5.8 6.0 1 1 Argentina 2001 UM 0.7 5.0 4.3 1 1 1 Armenia 1999 LM -8.6 5.1 13.7 1 1 Azerbaijan 2000 LM -7.4 10.2 17.6 1 1 Bangladesh 1988 L 2.0 6.0 3.9 1 Bangladesh 2003 L 5.9 8.0 2.1 1 Belarus 1998 LM -1.4 10.8 12.2 1 1 Benin 1978 L 0.7 51.9 51.2 1 1 Benin 1990 L 3.2 6.5 3.3 1 1 Bolivia 1987 LM -3.3 4.4 7.6 1 1 Bolivia 2003 LM 2.6 4.7 2.1 1 Botswana 1982 UM 11.8 14.7 2.9 1 1 Botswana 2004 UM 2.0 8.1 6.1 1 Bulgaria 1998 LM -5.2 6.0 11.2 1 1 Burundi 1978 L 5.5 8.1 2.6 1 Burundi 2003 L -0.7 10.2 10.9 1 1 Cambodia 1981 L -4.9 7.8 12.7 1 1
-Cambodia 1991 L 7.6 11.8 4.2 1 Cambodia 1999 L 13.4 17.1 3.7 1 1 Chad 1995 L 4.5 9.2 4.8 1 Chad 2003 L 7.1 9.3 2.2 1 1 Chile 1983 UM 2.8 6.0 3.2 1 1 China 1990 LM 8.2 10.2 2.0 1 China 2001 LM 8.5 10.9 2.4 1 Colombia 2000 LM 1.0 6.0 5.0 1 Congo 1978 LM 1.7 10.8 9.1 1 -Congo 1995 LM -1.8 4.7 6.5 1 1 Congo 2003 LM 5.3 7.7 2.5 1 1 Costa Rica 1985 UM 1.7 4.5 2.8 1 1 Cuba 1994 LM -4.3 6.3 10.6 1 Cuba 2006 LM 1.3 4.7 3.3 1 Czech Republic 1998 UM 0.6 6.5 5.8 1 1 Cote d'Ivoire 1982 L 4.7 7.7 3.0 1 Dominican Republic 1986 LM 2.8 4.9 2.1 1 1 Dominican Republic 1995 LM 3.4 6.0 2.7 1 Ecuador 1992 LM 0.6 12.0 11.3 1 Egypt 1978 LM 2.8 9.2 6.4 1 1 El Salvador 1988 LM -0.4 4.6 5.1 1 1 Estonia 1998 UM -0.5 8.2 8.6 1 1 Ethiopia 2002 L 4.5 7.5 3.0 1 1 -Gabon 1990 UM 1.9 5.6 3.7 1 Gambia 1978 L -12.9 59.2 72.1 1 Georgia 1999 LM -3.7 7.1 10.8 1 1
Honduras 1986 LM 2.0 4.3 2.3 1 Hungary 1992 UM -3.3 7.0 10.3 1 1 India 1981 L 4.6 7.3 2.6 1 India 2002 L 5.9 8.9 3.0 1 Iran 1983 LM 4.9 7.6 2.6 1 Iran 1995 LM 6.5 9.4 2.9 1 1 Iraq 2004 LM 0.3 7.6 7.3 1 1 Jordan 1978 LM 2.4 12.6 10.2 1 1 Jordan 1987 LM 5.7 8.7 3.0 1 Jordan 1997 LM 7.6 10.4 2.8 1 Kazakhstan 1998 LM -3.2 7.4 10.5 1 1 Kosovo 2001 LM 1.6 7.3 5.7 1 1 Kyrgyzstan 2007 L -2.0 6.2 8.3 1 Lao PDR 1979 L 2.8 7.2 4.4 1 1 Lao PDR 1987 L 6.6 12.0 5.4 1 Latvia 1999 UM -8.6 5.4 14.0 1 Lebanon 1983 UM -1.1 7.3 8.4 1 1 1 Lebanon 1991 UM 9.8 12.3 2.5 1 1 Lebanon 2004 UM 3.1 5.4 2.3 1 1 Lesotho 1978 L 2.6 12.4 9.9 1 Lesotho 1995 L 10.5 13.1 2.6 1 Liberia 1999 L -33.4 370.5 403.8 1 Libya 2001 UM 0.5 4.7 4.2 1 1 Lithuania 1999 UM -7.4 9.1 16.5 1 1 Malawi 2002 L -1.3 11.1 12.4 1 1 1 Malaysia 1984 UM 8.9 11.3 2.4 1 Mali 1978 L 2.4 7.2 4.8 1 Mauritania 1978 L 3.1 8.9 5.8 1 1
Mauritania 1992 L 4.3 11.2 6.8 1 Mauritania 2007 L -2.8 5.8 8.7 1 Mauritius 1980 UM 6.8 8.8 2.0 1 Mongolia 1995 L 1.4 8.0 6.6 1 Mozambique 1991 L -2.1 7.2 9.3 1 1 Mozambique 1999 L 8.5 13.6 5.1 Myanmar 1989 L 0.5 6.0 5.6 1 1 1 Myanmar 1997 L 5.3 18.3 13.1 1 1 Namibia 1980 LM 2.7 5.6 2.9 1 Namibia 1996 LM 1.9 4.3 2.4 1 Nepal 1978 L 3.8 7.0 3.2 1 Nepal 1990 L 8.9 12.0 3.1 Nicaragua 1994 LM -3.5 4.9 8.5 1 1 1 Niger 2005 L 0.7 8.1 7.3 1 Nigeria 1999 L -2.1 7.6 9.7 1 1 Nigeria 2007 L 8.3 12.7 4.4 1 Oman 1978 UM 10.5 27.6 17.1 1 Oman 1993 UM 10.2 12.7 2.5 1 Pakistan 1978 L 4.6 9.5 4.9 1 1 Pakistan 1998 L 4.1 7.8 3.7 1 Panama 1989 UM 0.0 5.5 5.5 1 1 Panama 2005 UM -1.2 4.5 5.7 1 1 Papua New Guinea 1989 LM 2.3 4.6 2.4 1 Papua New Guinea 2003 LM 0.5 5.7 5.2 1 1 Peru 2001 LM 3.6 6.5 2.9 1 Poland 1985 UM 0.4 5.8 5.4 1 Poland 1993 UM 7.2 9.7 2.4 1 1
Poland 2003 UM 6.4 9.5 3.1 1 Romania 1998 LM -3.9 4.3 8.2 1 Russia 1999 LM -6.5 7.2 13.7 1 Rwanda 1979 L 3.8 7.7 4.0 Rwanda 1996 L -2.2 8.0 10.2 1 1 Slovakia 1998 UM -0.5 9.9 10.4 1 1 Somalia 1996 L -3.5 6.2 9.7 1 Sri Lanka 1982 LM 2.1 4.4 2.3 1 Sri Lanka 1990 LM 5.6 8.6 3.0 1 Palestine 1985 LM 3.6 6.4 2.7 1 1 Palestine 2001 LM 3.2 6.7 3.5 1 Swaziland 1981 LM 14.2 17.3 3.1 1 1 Syria 1978 LM 6.4 12.0 5.5 1 1 Syria 1990 LM 7.0 14.8 7.8 1 1 1 Syria 2001 LM 19.7 35.4 15.8 1 Macedonia 1999 LM -4.4 6.2 10.5 Thailand 1983 LM 8.7 11.4 2.7 1 Togo 1994 L 2.0 9.8 7.8 1 1 1 Trinidad and Tobago 1989 UM -0.7 4.5 5.2 1 1 Trinidad and Tobago 1997 UM 4.4 13.2 8.8 1 1 Turkey 1980 LM 4.7 6.9 2.2 1 Turkey 2001 LM 2.5 4.7 2.2 1 1 Turkmenista n 2001 LM -1.1 12.2 13.3 1 1 Tanzania 1996 L 3.2 6.3 3.1 1 Tanzania 2004 L 6.8 8.9 2.0 1 Uganda 1982 L -0.3 6.3 6.5 1 1
Uganda 1990 L 4.4 13.2 8.7 1 1 Ukraine 2000 LM -8.6 9.4 17.9 1 Uruguay 2001 UM -0.4 4.4 4.8 1 1 Uzbekistan 2001 LM 0.0 4.6 4.7 1 1 Venezuela 2004 UM 0.5 4.6 4.1 1 1 1 Viet Nam 1981 L 5.1 7.3 2.2 1 1 Viet Nam 1990 L 3.2 9.6 6.4 1 1
Table A2. Descriptive statistics
Variable Obs Mean Std. Dev. Min Max
lgdp_pc 2,944 7.08 1.16 4.03 10.03 gfcf_gdp 2,913 0.21 0.11 0.01 1.59 hc 2,505 5.61 2.72 -0.04 12.76 reer 2,536 160.10 1133.91 0.38 56273.63 kaopen 2,371 -0.49 1.30 -1.89 2.39 ToT 2,526 1.00 0.05 0.70 1.32 pol 2,666 4.37 3.02 0.00 10.00
Table A3. Results using GDP growth accelerations
(1) (2) (3) (4)
Main LI LMI UMI
lgdp_pc -0.05600*** -0.11176*** -0.06199** -0.15752** [0.018] [0.038] [0.024] [0.077] gfcf_gdp -0.68053*** -0.85507*** -0.65132** -0.18709 [0.151] [0.173] [0.293] [0.332] reer -0.00001 0.00034* -0.00001* 0.00045 [0.000] [0.000] [0.000] [0.001] kaopen -0.01300* 0.01221 -0.03808** 0.00130 [0.008] [0.013] [0.015] [0.014] hc 0.01671*** 0.04133*** 0.00544 0.01373 [0.005] [0.010] [0.009] [0.014] pol -0.00215 0.00736 0.00590 -0.00991 [0.003] [0.006] [0.005] [0.009] tot -0.19988 0.58779 -0.65397** 0.05879 [0.243] [0.494] [0.318] [0.443] Observations 1,267 393 473 220
Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1
Note: The dependent variable of these regressions is a dummy taking 1 if a country experienced a GDP growth acceleration in year t, as well in the year preceding and following t. All specifications include year fixed effects. Specification reported in column (1) includes as well income group fixed effects.
Table A4. Additional results, drivers of MVA accelerations
Initial MVA Labor cost Geographic centrality
Main LI LMI UMI Main LI LMI UMI Main LI LMI UMI
lgdp_pc -0.11056*** -0.23074*** -0.06694*** -0.10180* 0.00596 -0.00966 0.05672 -0.23120** -0.12759*** -0.22288*** -0.10696*** -0.13108* [0.020] [0.042] [0.021] [0.052] [0.029] [0.015] [0.050] [0.116] [0.022] [0.042] [0.029] [0.073] gfcf_gdp 0.16056 0.47763*** 0.04744 -0.35596 0.24801* 0.02720 -0.11358 -1.46570 0.19062 0.51314*** 0.01755 -0.31803 [0.105] [0.119] [0.180] [0.322] [0.129] [0.042] [0.315] [0.892] [0.146] [0.143] [0.216] [0.294] Hc 0.03310*** 0.02188* 0.02477*** 0.04708 0.03610*** 0.00185 0.06308*** 0.09163** 0.02783*** -0.00981 0.02267*** 0.05090 [0.005] [0.012] [0.007] [0.030] [0.008] [0.003] [0.014] [0.046] [0.006] [0.009] [0.009] [0.035] Reer -0.00005 0.00049** -0.00010 0.00062 -0.00006 0.00001 0.00011 -0.00015 -0.00002** 0.00037* -0.00003 0.00070 [0.000] [0.000] [0.000] [0.001] [0.000] [0.000] [0.000] [0.001] [0.000] [0.000] [0.000] [0.001] Kaopen -0.03537*** -0.06689*** -0.05909*** 0.00080 -0.03823*** -0.00360 -0.08518*** 0.01175 -0.03036*** -0.07917*** -0.05362*** 0.00316 [0.008] [0.021] [0.013] [0.006] [0.010] [0.006] [0.022] [0.018] [0.009] [0.024] [0.016] [0.007] Tot -0.52857*** -0.49574 -0.50866** -0.89263* -0.85574** -0.01728 -0.82461 -1.57821*** -1.26837*** -0.85967 -1.77484*** -1.17095 [0.191] [0.435] [0.244] [0.534] [0.347] [0.033] [0.783] [0.601] [0.225] [0.523] [0.351] [0.781] Pol 0.00961*** 0.01319** 0.00578 0.01135 0.00076 0.00014 -0.01119 0.02077 0.01278*** 0.00433 0.02005*** 0.00771 [0.003] [0.007] [0.004] [0.010] [0.005] [0.000] [0.010] [0.017] [0.004] [0.007] [0.006] [0.008] mva_gdp -1.43463*** -1.26605*** -1.03340*** -0.72395 [0.170] [0.382] [0.202] [0.604] lwage_pc -0.08507*** -0.00368 -0.08099** -0.04005 [0.020] [0.006] [0.036] [0.052] Geo 2.96974*** -3.35124* 3.67708*** 1.21452 [0.671] [2.006] [1.181] [1.026] Observations 1,599 540 672 319 877 127 348 199 1,414 476 596 290
Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1
Table A5. Additional results, drivers of MVA accelerations
Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1
Note: All specifications include year fixed effects. Specifications on the whole sample (those with the header ‘main’) include as well income group fixed effects.
Ethnic fragmentation War Inequality
Main LI LMI UMI Main LI LMI UMI Main LI LMI UMI
lgdp_pc -0.09501*** -0.20789*** -0.06031*** -0.17677* -0.10846*** -0.23848*** -0.06753*** -0.17979* -0.08789*** -0.22905*** -0.04396* -0.16880*** [0.020] [0.046] [0.022] [0.095] [0.020] [0.044] [0.022] [0.093] [0.020] [0.043] [0.024] [0.044] gfcf_gdp 0.18020 0.46299*** -0.05895 -0.20260 0.18211 0.53284*** -0.12791 -0.26957 0.19884 0.51763*** -0.09439 -0.13881 [0.132] [0.132] [0.202] [0.200] [0.127] [0.127] [0.191] [0.248] [0.125] [0.131] [0.204] [0.111] Hc 0.02203*** 0.00010 0.01971*** 0.05898 0.02334*** 0.00457 0.01979*** 0.06383* 0.01535*** 0.00205 0.02222** 0.03586*** [0.005] [0.010] [0.007] [0.037] [0.005] [0.010] [0.007] [0.039] [0.006] [0.010] [0.010] [0.009] Reer -0.00001** 0.00033 -0.00003 0.00051 -0.00001* 0.00034 -0.00002 0.00059 -0.00001** 0.00034 -0.00003 0.00068*** [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Kaopen -0.02952*** -0.04849** -0.06521*** 0.00095 -0.03374*** -0.05927*** -0.06031*** -0.00004 -0.03020*** -0.05667*** -0.06166*** -0.00483 [0.008] [0.020] [0.014] [0.006] [0.008] [0.021] [0.013] [0.007] [0.008] [0.020] [0.013] [0.005] Tot -1.15720*** -0.49265 -1.26993*** -1.46130* -1.07641*** -0.67310 -1.28510*** -1.50550* -1.04257*** -0.61639 -1.31145*** -1.04228*** [0.217] [0.463] [0.301] [0.878] [0.212] [0.461] [0.283] [0.878] [0.214] [0.478] [0.286] [0.261] Pol 0.00955*** 0.00860 0.00987** 0.00802 0.01022*** 0.00852 0.01005** 0.00929 0.00632* 0.00787 0.00381 0.00452 [0.004] [0.006] [0.005] [0.007] [0.004] [0.007] [0.005] [0.008] [0.004] [0.007] [0.005] [0.003] Ethnic -0.09055** -0.14086* 0.03059 -0.03590 [0.039] [0.072] [0.054] [0.047] War -0.05607*** 0.01617 -0.10776*** [0.021] [0.045] [0.021] Gini -0.46391*** -0.15996 -0.37664* -0.28667** [0.110] [0.171] [0.215] [0.122] Observations 1,599 540 672 319 1,599 540 672 308 1,576 540 649 319
Table A6. Main results, drivers of MVA accelerations (1) (2) (3) (4) VARIABLES Main TFP Voc_trainin g Trade_open lgdp_pc 0.10169***- -0.09611*** 0.08862*** -[0.020] [0.021] [0.019] gfcf_gdp 0.18919 0.10712 0.31776*** 0.02582 [0.126] [0.115] [0.105] [0.116] hc 0.02354*** 0.01760*** 0.02006*** [0.005] [0.006] [0.005] reer -0.00002** 0.00019 0.00020* -0.00001*** [0.000] [0.000] [0.000] [0.000] kaopen 0.03304***- -0.01155 -0.02532*** 0.04361*** -[0.008] [0.008] [0.008] [0.008] tot -1.07739*** -0.42896 -1.33052*** [0.212] [0.287] [0.227] pol 0.00960*** 0.00499 0.00789** 0.00718** [0.004] [0.004] [0.004] [0.003] trade_op 0.06368** [0.026] tfp 0.28496*** -[0.061] voc_training 0.27240*** [0.037] Observations 1,599 1,209 1,444 1,702
Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1 Note: All specifications include income group fixed effects and year fixed effects.
