innovation, structural change, & inclusion a cross country

Innovation, Structural Change, & InclusionA Cross Country Analysis

Amrita Saha1 Tommaso Ciarli2

1Institute of Development Studies, [email protected], University of Sussex, [email protected]

7th European Conference on Corporate R&D and InnovationInnovation for Industrial Transformation

JRC & OECDSeville, September 25-27, 2019

Saha, Ciarli Innovation, Structural Change, & Inclusion CONCORDi19, Seville 0 / 24

1. Introduction

Outline

1 Intro & motivation

2 Theoretical framework and research questions

3 Data & method

4 Results

5 Discussion


1. Introduction 1. Motivation

Motivation

Innovation nurturers structural change in economies and societies,and both lead to (economic) development (Syrquin, 1988; Cimoli andDosi, 1995; Verspagen, 2004; Hidalgo et al., 2007).

Innovation is disruptive (Schumpeter, 1911), and may havedistributional consequences (Aghion et al., 2015; Lee, 2011;OECD, 2015)

Economic growth and structural change tend to reduce poverty(Ravallion and Chen, 2003), but the extent depends on how income isdistributed (Bourguignon, 2003)

The concept of inclusive innovation is loose, with limitedunderstanding of how to achieve it (Chataway et al., 2014; Cozzensand Sutz, 2014)

We know little about how inclusion influences innovation and SC


1. Introduction 2. Research Question

A 3-way chicken or egg problem

Research Question

How are innovation, structural change, and inclusion related over time?

INNOVATION

STRUCTURAL CHANGE INCLUSION

Source: (Ciarli et al., 2018)


1. Introduction 3. Available evidence

Relatively established [ Plot ]

Innovation & Structural Change

[INN ⇒ SC] emergence of new sectors, increases in productivity and firm size,capital intensity, entrepreneurship, and changes in consumption patterns

[SC ⇒ INN ] SC requires introduction of new technologies (Dosi G, 1997; Ruttan,2002); Firms cope with the disequilibria generated by SC (Lundvall, 1992)

INNOVATION


++



Relatively ambiguous [ Plot , Plot ]

Innovation & Inclusion [INN ⇒ INC?]

Innovation generates winners and losers (Schumpeter, 1911; Helpman et al., 2010):

(i) income distribution (ii) “frugal innovation” (iii) grass-roots (Paunov, 2013)

Innovation may increase inequality but also mobility (Aghion et al., 2015)

Structural Change & Inclusion [SC ⇒ INC?]

Imbalances that accompany structural change – e.g.: capital accumulation, factorscomposition, substitution of domestic for foreign labour/knowledge, skills :– ↑ income inequality in the short run (Kuznets, 1973; Ravallion, 2004),– ↓ poverty (McMillan and Rodrik, 2011; UNU-WIDER, 2012)

INNOVATION


+/-+/-



Pretty much unknown[INC ⇒ INN?] Does inclusion influence innovation in the long run?

Some degree of oligopoly is required for firms to have an incentive andresources to innovate (Arrow, 1962; Malerba and Orsenigo, 1997)

Background of inventors (Akcigit et al., 2017; Aghion et al., 2017)

[INC ⇒ SC?] Which forms of inclusion scale up to structural change?

E.g. access to goods and finance (frugal)

Grass-roots innovation?

INNOVATION


?

?


Dynamic relations between innovation, SC, & inclusion Scen.

+

INNSC

+

+

INC

+

R

+B R

--

-

Exclusive SC

Inclusive SC

Exclusive INN

Inclusive INN

Notes. INN: innovation; SC: structural changes; INC: inclusion; R: reinforcing mechanisms – a positive shock in one variableinduces a positive effect in the other variable; B: balancing mechanisms – a positive shock in one variable induces a negativeeffect on the other variable. Blue indicates a positive impact; red indicates a negative impact. Source: Ciarli et al. (2018)

3. Main results

Main findings (under construction)

Only left hand side of virtuous cycle: INC ⇒ SC and INN, but theother reinforcing mechanism does not work (INN, SC ; INC)

Nor INN nor SC have a strong significant effect on INC

INN leads to SC (employment) and SC (other) leads to INN

INNOVATION


+

+

+

INNOVATION


+

+

+

(a): Employment (b): Broad

These relations are complex and depend on different aspects(indicators) of INN and INC


4. Empirical strategy 1. Indicators

Indicators

Main challenge

INN, SC, and INC, are multidimensional concepts

Measured (observed) variables highly correlated

Indicators

principal components to construct linear combinations Details

3-years rolling averages & all variables standardized

use first component to construct index scores

We loose significant detail, but we capture multiple dimensions of therelation between INN, SC, and INC.



Innovation (INN) Plot , Ranking

Innovation inputs and outputs

Formal InnovationI Research & development expenditure (% of GDP)I Journal Articles (per capita in logs)

Firm Innovation Capabilities

I Researchers in R&D (per million people)I Technicians in R&D (per million people)I Firms using technology licensed from foreign firms (%)I Firms with quality certification (%)

Information & Communication Technology

I Internet Users (per 100 people)I Mobile users per capitaI % of firms having their own Web siteI % of firms using e-mail to interact with clients or suppliers



Inclusion (INC) Plot , Ranking

Beyond Poverty & Inequality

Poverty

I Poverty GapI Poverty Head Count Ratio

InequalityI Gini Index

Employment

I Wage & Salaried Workers

Gender participation

I Firms with female participation in ownership (%)I Female permanent full-time workers (%)



Structural Change (SC) Ranking

Traditional & Broad

Indicator 1: Traditional (employment)I % Employment in industryI % Employment in services

Indicator 2: BroadI Total Factor productivityI Urbanization (Urban Population % of Total)I Firm size (Log of the number of permanent full-time workers)I Gross capital formation (% of GDP)


4. Empirical strategy 2. Data

Data

2000-2013 for 33 low and middle income countries:

Botswana, Brazil, Burkina Faso, Cambodia, Chile, China, Colombia, Congo Dem. Rep.,

Ethiopia, Gambia, Ghana, India, Kenya, Malaysia, Maldives, Mexico, Morocco,

Mozambique, Myanmar, Nepal, Niger, Nigeria, Pakistan, Peru, Philippines, Rwanda,

Senegal, South Africa, Sri Lanka, Thailand, Turkey, Uganda, Vietnam, Zambia

Time variation is different across countries and variables that is dictatedby data availability

Sources:World Development Indicators (WDI)World Bank Enterprise Survey (WBES)UNIDO


4. Empirical strategy 3. Method

Dynamic relations: cross country analysis (macro)Panel vector autoregression (VAR) (Abrigo and Love, 2016): developingcountries & 13 years

Yit = ηi +

n∑j=1

βjYi,t−j + εit

where for country i, and time t;

Yit-1x3 vector containing the set of endogenous variables [SC, INN, INCL]

Yi,t−jβj coefficient matrices show the contemporaneous and (one year) lag effects

ηi-country-specific FE; j-Lags; System GMM (j = 5) – first differences

Identification of contemporaneous effects (Choleski decomposition):(1) SC, (2) INN, (3) INCL

Optimal lag order using the 3-model selection criteria (Andrews and Lu, 2001).First-order panel VAR model

Estimation of orthogonalised impulse-response functions


4. Results 1. Main relations

Impulse response functions: SC(emp), INN, INC [ Table , Stab. ]

Impulse Response functions (IRFs) from the baseline estimation with employment based structural change index-SC1, innovationindex-INN and the inclusion index-INC. IRF confidence intervals computed using 200 Monte Carlo draws.



Impulse response functions: SC(emp), INN, INC

All variables positively serially correlated

Strongest effect on Innovation comes from a shock in Inclusion

Inclusion also has a positive strong effect on Structural Change, after 3 years

Neither Innovation nor Structural Change have a significant effect onInclusion

No reinforcing mechanism between SC & INNI Innovation leads to Empl. SC (but only in the short period)I But Empl. SC does not lead to innovation

Variance decomposition [ Table ]

INC explains large part of INN and SC, but not the other way round



IRFs: SC(broad), INN, INC [ Table ]

Impulse Response functions (IRFs) from the baseline estimation with broad structural change index-SC2, innovation index-INNand the inclusion index-INC. IRF confidence intervals computed using 200 Monte Carlo draws.



Panel VAR(1) for Structural Change, Innovation, Inclusion


Inclusion has an immediate positive effect on broad SC


No reinforcing mechanism between SC & INNI Broad SC leads to innovation (but only in the short period)I But Innovation does not lead to broad Structural Change


4. Results 2. Unpacking INN and INC

Which aspects of INC and INN? (very preliminary)

INC ⇒ INN & SC

Formal (R&D)

Diffusion of ICTs

Firm capabilities

INN ⇒ INC

Formal (R&D)

Firm capabilities (negative)

INC ⇒ INN & SC

Poverty (negative)

Poverty & inequality

INN ⇒ INC

Poverty and inequality


5. Discussion

Contribution

Research Question

How are innovation, structural change, and inclusion related over time?

Theoretical Framework:I The impact of SC and INN on inclusion may weaken the virtuous cycle

between SC and INNI Depending on the effect of INC on INN and SC

Empirical Evidence:

I Data for Developing countries across 13 yearsI Capture multidimensionality to study the dynamic relations

Contribution:

I Examine the 3-way relation for developing countriesI Understand if inclusion leads to further structural change & innovation


5. Discussion

How are INN, SC, and INC related over time?

The main nurturer of SC and INN is inclusion (so far) Results

Virtual cycle between SC and INN (depending on different SC)

But neither feed back on INC [ Scenarios , Figure ]

Source: based on Ciarli et al. (2018)

Unpacking indicators of INN and INC suggest these relations are complexand depend on different aspects of INN and INC


5. Discussion

Policy implicationsSo far

The only variable that seems to reinforce inclusion is inclusion

If INC has a positive effect on INN and SC (which reinforce eachother) there seem to be two clear policy implications

I Improve INC, beyond poverty and inequalityI Make INN and SC more inclusive (they do not seem to be so now):

Inclusive Structural Change

More nuanced policy implications

Unpack indices to provide a better understanding of which aspect ofINN, and INC are most relevant

Current/Future agenda:

Unpack indicators and variables

Micro-level comparisons (employer-employee)


Background papers

Ciarli, Tommaso, Maria Savona, Jodie Thorpe, and Seife Ayele. 2018.“Innovation for Inclusive Structural Change. A Framework and ResearchAgenda.” SPRU Working Paper Series 2018-04. Brighton, UKhttps://ideas.repec.org/p/sru/ssewps/2018-04.html

Saha, Amrita, and Tommaso Ciarli. 2018. “Innovation, Structural Change,and Inclusion. A Cross Country PVAR Analysis.” SPRU Working PaperSeries 2018-1. Brighton, UKhttps://ideas.repec.org/p/sru/ssewps/2018-01.html

Saha, Amrita, Jodie Thorpe, and Seife Ayele. 2018. “Inclusive StructuralChange: Case Studies on Innovations in Breeding Practices in Kenya andAnti-Retroviral Therapy Service Provision in Mozambique.” IDS WorkingPapers 505. Brighton, UK: IDS, Sussex.https://opendocs.ids.ac.uk/opendocs/bitstream/handle/

123456789/13485/Wp505_Online.pdf?sequence=1&isAllowed=y

https://ideas.repec.org/p/sru/ssewps/2018-04.html

https://ideas.repec.org/p/sru/ssewps/2018-01.html

https://opendocs.ids.ac.uk/opendocs/bitstream/handle/123456789/13485/Wp505_Online.pdf?seq uence=1&isAllowed=y

https://opendocs.ids.ac.uk/opendocs/bitstream/handle/123456789/13485/Wp505_Online.pdf?seq uence=1&isAllowed=y

Many thanks for your attention!Tommaso Ciarli, [email protected]

Amrita Saha, [email protected]

Background material

Structural Change & Innovation-2000 & 2012

(a): 2000 (b): 2012

[ Relations ]


Background material

Inclusion & Innovation-2000 & 2012

(a): 2000 (b): 2012

[ Relations ]


Background material

Inclusion & Structural change-2000 & 2012

(a): 2000 (b): 2012

[ Relations ]


Background material

Variables: PCA

I =∑

Wijxij

where

xij : value of the j-th variable forming the composite index

Wij is the relative weight of the j-th variable

3-years rolling averages; all variables standardised

For all indices, we use the first component explaining the highest varianceto construct the index scores

We loose significant detail, but we capture multiple dimensions of therelation between INN, SC, and INC.

Back


Background material

Innovation Index Indicators

Source: Author’s calculations using World Bank data


Background material

Inclusion Index Indicators



Background material

Innovation & Inclusion Index By INN , Indicators



Background material

SC Index: Indicator1 & Indicator2 Indicators



Background material

Unpacking Innovation: Formal, Firm-Level & ICTOverall



Virtuous: full positive cycle Back

INC favours INN INC reduces INN1

+

INNSC

+

+

INC

+

R+

R

Inclusive SC

Inclusive INN

+

INNSC

+

+

INCR+

B-

Inclusive SC

Inclusive INN

Scenarios in the dynamic relations between innovation, structural change, and inclusion.

Source: Author elaboration.

Exclusive structural change Back


INNSC

+

+

INC

+

R

+

R

-

Exclusive SC

Inclusive INN

INNSC

+

+

INCR

+B

-

-

Exclusive SC

Inclusive INN



Exclusive innovation Back


+

INNSC

+

+

INC

+

R R

-

Inclusive SC

Exclusive INN

+

INNSC

+

+

INCR B

-

-

Inclusive SC

Exclusive INN



Slow growth Back Results


INNSC

+

+

INC

+

R R

--

Exclusive SC

Exclusive INN

INNSC

+

+

INCR B

--

-

Exclusive SC

Exclusive INN



Four scenarios Back

t SC INN INC t SC INN INCt-1 t-1SC + + SC + +

1 INN + + INN + +INC + + INC + -

SC + - SC + -2 INN + + INN + +

INC + + INC + -

SC + + SC + +3 INN + - INN + -

INC + + INC + -

SC + - SC + -4 INN + - INN + -

INC + + INC + -Notes: INN: innovation; SC: structural changes; INC: inclusion; EXC: exclu-sion. “+/-” indicates a positive/negative relation between the variable in t−1and in t.

Background material Analysis

Empirical strategy

Build indices for INN, SC, and INC, using the first component explainingthe highest variance [ Sum Stats ]

Fixed-effects (within) regression with Driscoll and Kraay standard errors[ Specification , Results ]

Dynamic relations: panel vector autoregression (PVAR)



Summary Statistics for Indexes [ back ]

Variable Obs Mean Std. Dev. Min MaxMain IndexesStructural Change Index (SC) 386 0.470 0.263 0 1Inclusion Index (INC) 422 0.621 0.256 0 1Innovation Index (INN) 299 0.261 0.213 0 1

Other IndexesOther Structural Change Index (Other SC) 350 0.450 0.267 0 1Firm-Level Innovation Index (INN1) 357 0.364 0.222 0 1R&D Innovation Index (INN2) 396 0.443 0.196 0 1Science & ICT Innovation Index (INN3) 390 0.171 0.181 0 1Trade Index 422 0.195 0.195 0 1Education Index 422 0.428 0.234 0 1Finance Index 422 0.294 0.222 0 1



The ambiguous relations Strategy

Fixed-effects (within) regression with Driscoll and Kraay standard errors

INNit = αi + β1SCit + β2INCi,t−1 + εit

INCit = αi + γ1INNit + γ2SCi,t−1 + εit

where for country i, and time t;

βk γk and are the coefficients;

αi are country-specific FE

εit is a country specific error term



Panel OLS regression results-Model 1 & Model 2Strategy

MODEL 1 MODEL 2Dependent Variable-Innovation Dependent Variable-Inclusion

Variables I II Variables III IV

Structural Change 0.325*** 0.213*** Innovation Index 0.262*** 0.251***Index (0.081) (0.046) (0.010) (0.013)

Inclusion Index 1.183*** Structural Change 0.033(t-1) (0.099) Index (t-1) (0.019)

Constant 0.116*** -0.572*** Constant 0.557*** 0.551***(0.024) (0.068) (0.006) (0.005)

Observations 286 264 Observations 299 264Countries 22 22 Countries 23 22

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1



Panel OLS regression results-Model 1 & Model 2

Both SC and INC are related to INN, although part of the relationbetween SC and INN seems to be explained by INC in t-1

The SC coefficient becomes significantly smaller when we control fortrade, education, financial development, and GDP

Past INC becomes not significant with controls [ Table ]

INC seems to be correlated to INN, but not to SC

Becomes negative when we control for trade, education, financialdevelopment, and GDP [ Table ]


Panel OLS regression results-Controls Model 1 [ Back ]

Model 1 Robustness

VARIABLES III IV V VI

Structural Change 0.168*** 0.162*** 0.074* 0.078***(0.034) (0.032) (0.040) (0.016)

Inclusion Index(t-1) -0.049 -0.044 0.058 0.038(0.055) (0.057) (0.202) (0.049)

Trade Index -0.306*** -0.273*** -1.381*** -0.167***(0.062) (0.063) (0.202) (0.037)

Education Index 0.811*** 0.858*** 0.739*** 0.348***(0.067) (0.078) (0.118) (0.094)

Finance Index 0.638*** 0.630*** 0.649*** 0.508***(0.053) (0.059) (0.033) (0.056)

FDI(% of GDP) -0.003***(0.000)

Social Contributions 0.008(0.006)

GDP Per Capita PPP 0.034***(0.003)

Constant -0.233*** -0.249*** -0.183 -0.246***(0.031) (0.037) (0.142) (0.029)

Observations 264 264 144 264Number of groups 22 22 12 22

Panel OLS regression results-Controls Model 2 [ Back , RC ]

Model 2 Robustness

VARIABLES III IV V VI

Innovation Index -0.064** -0.068** -0.018 -0.057*(0.022) (0.024) (0.060) (0.026)

Structural Change(t-1) 0.034 0.034 0.021 0.035(0.020) (0.020) (0.021) (0.021)

Trade Index 0.092** 0.094** -0.034 0.091**(0.032) (0.033) (0.077) (0.031)

Education Index 0.489*** 0.497*** 0.319*** 0.494***(0.029) (0.030) (0.037) (0.025)

Finance Index 0.222*** 0.223*** 0.202*** 0.220***(0.035) (0.035) (0.057) (0.036)

FDI(% of GDP) -0.000(0.000)

Social Contributions -0.005*(0.003)

GDP Per Capita PPP -0.001(0.001)

Constant 0.353*** 0.351*** 0.487*** 0.355***(0.006) (0.007) (0.022) (0.007)

Observations 264 264 144 264Number of groups 22 22 12 22

Background material Results

IRF: SC(emp), INN, INC [ Table , Stab. , Summary ]

Impulse Response functions (IRFs) from the baseline estimation with employment based structural change index-SC1, innovationindex-INN and the inclusion index-INC. IRF confidence intervals computed using 200 Monte Carlo draws.



Impulse response functions: SC(emp), INN, INC

All variables positively serially correlated


Inclusion also has a positive strong effect on Structural Change, after 3 years


No reinforcing mechanism between SC & INNI Innovation leads to Empl. SC (but only in the short period)I But Empl. SC does not lead to innovation

Variance decomposition [ Table ]

INC explains large part of INN and SC, but not the other way round



IRFs: SC(broad), INN, INC [ Table ]

Impulse Response functions (IRFs) from the baseline estimation with broad structural change index-SC2, innovation index-INNand the inclusion index-INC. IRF confidence intervals computed using 200 Monte Carlo draws.





Inclusion has an immediate positive effect on broad SC


No reinforcing mechanism between SC & INNI Broad SC leads to innovation (but only in the short period)I But Innovation does not lead to broad Structural Change



How are innovation, structural change, and inclusion related

over time?

⇒ No evidence of virtuous cycle: INC ⇒ SC and INN (persistently), butthe other reinforcing mechanism does not work (INN, SC ; INC)

Next

Unpacking Innovation


Background material Unpacking innovation

IRFs: SC(emp), Formal INN, INC [ back ]

Impulse Response functions (IRFs) from the baseline estimation with rapid structural change index-SC1, innovation index-INN2and the inclusion index-INC. IRF confidence intervals computed using 200 Monte Carlo draws.


Background material Unpacking innovation

IRFs: SC(emp), Firm-level INN, INC [ back ]

Impulse Response functions (IRFs) from the baseline estimation with rapid structural change index-SC1, innovation index-INN1and the inclusion index-INC. IRF confidence intervals computed using 200 Monte Carlo draws.


Background material Rob checks

Other Robustness Checks

Control for GDP, trade, education, and financial development (OLS)[ Tables ]

Changing order of Cholesky decomposition; INN, SC, INCL

PVAR with sub-indexes


Background material More results


[ Back ]

(1) (2) (3)VARIABLES Structural Change SC1 Innovation Inclusion

Structural Change(t-1) 0.523*** 0.030* -0.019(0.075) (0.018) (0.013)

Innovation(t-1) 0.197*** 0.686*** 0.038***(0.037) (0.049) (0.012)

Inclusion(t-1) -0.180** 1.021*** 0.872***(0.081) (0.126) (0.033)

Observations 176 176 176Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1



Stability Conditions [ Back ]

Checking stability conditions, we draw graphs of the eigenvalues of thecompanion matrix.



Panel VAR(1): SC (other), INN, INC [ back ]

INNOVATION


+

+

+



Variance decomposition: SC (emp), INN, INC [ back ]

Response SCIndex empl INN INCLvariable Impulse variable Impulse variable Impulse variable

Years SC INN INCL SC INN INCL SC INN INCL0 0 0 0 0 0 0 0 0 01 1 0 0 0.0177 0.9823 0 0.0630 0.0634 0.87362 0.9949 0.0046 0.0006 0.0119 0.9184 0.0696 0.0506 0.0472 0.90223 0.9859 0.0105 0.0036 0.0134 0.7930 0.1936 0.0429 0.0370 0.92014 0.9738 0.0154 0.0108 0.0161 0.6599 0.3240 0.0379 0.0304 0.93175 0.9587 0.0187 0.0226 0.0180 0.5462 0.4358 0.0344 0.0261 0.93956 0.9411 0.0207 0.0382 0.0189 0.4567 0.5244 0.0319 0.0231 0.94517 0.9217 0.0217 0.0566 0.0193 0.3880 0.5927 0.0299 0.0209 0.94918 0.9011 0.0222 0.0768 0.0192 0.3353 0.6455 0.0284 0.0193 0.95229 0.8800 0.0223 0.0977 0.0190 0.2942 0.6867 0.0272 0.0181 0.9547

10 0.8590 0.0222 0.1187 0.0188 0.2618 0.7195 0.0262 0.0171 0.9567

The percent of variation in the row variable that is explained by the column variable for 10periods ahead.



Panel VAR(1): SC (other), INN, INC [ Back ]

(1) (2) (3)VARIABLES Structural Change SC2 Innovation Inclusion

Structural Change(t-1) 0.931*** 0.002 0.022(0.067) (0.042) (0.021)

Innovation(t-1) -0.075 0.871*** 0.005(0.055) (0.051) (0.018)

Inclusion(t-1) 0.593*** 0.784*** 0.892***(0.133) (0.139) (0.050)

Observations 189 189 189Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

SC: Urbanization (Urban Population % of Total); Firm size (Log of the numberof permanent full-time workers); Total Factor productivity; Gross capital

formation (% of GDP)


Four scenarios [ back ]

t SC INN INC t SC INN INCt-1 t-1SC + + SC + +

1 INN + + INN + +INC + + INC + -

SC + - SC + -2 INN + + INN + +

INC + + INC + -

SC + + SC + +3 INN + - INN + -

INC + + INC + -

SC + 0 SC + -4 INN + 0 INN + -

INC + + INC + -Notes: INN: innovation; SC: structural changes; INC: inclusion; EXC: exclu-sion. “+/-” indicates a positive/negative relation between the variable in t−1and in t.


References I

Abrigo, M. R. M. and Love, I. (2016). Estimation of panel vectorautoregression in stata. Stata Journal, 16(3):778–804(27).

Aghion, P., Akcigit, U., Bergeaud, A., Blundell, R., and Hemous, D.(2015). Innovation and Top Income Inequality.

Aghion, P., Akcigit, U., Hyytinen, A., and Toivanen, O. (2017). TheSocial Origins of Inventors.

Akcigit, U., Grigsby, J., and Nicholas, T. (2017). The Rise of AmericanIngenuity: Innovation and Inventors of the Golden Age.

Andrews, D. and Lu, B. (2001). Consistent model and moment selectionprocedures for gmm estimation with application to dynamic panel datamodels. Journal of Econometrics, 101(1):123–164.

Arrow, K. (1962). The Economic Implications of Learning by Doing.Review of Economic Studies, 29(2):155–173.



References IIBourguignon, F. (2003). The growth elasticity of poverty reduction:

explaining heterogeneity across countries and time periods. In Eicher,T. S. and Turnovsky, S. J., editors, Inequality and growth: Theory andpolicy . . . , chapter 1, pages 3–26. MIT Press.

Chataway, J., Hanlin, R., and Kaplinsky, R. (2014). Inclusive innovation:an architecture for policy development. Innovation and Development,4(1):33–54.

Ciarli, T., Savona, M., Thorpe, J., and Ayele, S. (2018). Innovation forInclusive Structural Change. A Framework and Research Agenda.

Cimoli, M. and Dosi, G. (1995). Technological paradigms, patterns oflearning and development: an introductory roadmap. Journal ofEvolutionary Economics, 5:243–268.

Cozzens, S. and Sutz, J. (2014). Innovation in informal settings:reflections and proposals for a research agenda. Innovation andDevelopment, 4(1):5–31.



References III

Dosi G (1997). Opportunities, Incentives and Collective Patterns ofTechnological Change. The Economic Journal, 107(444):1530–1547.

Helpman, E., Itskhoki, O., and Redding, S. (2010). Inequality andUnemployment in a Global Economy. Econometrica, 78(4):1239–1283.

Hidalgo, C. A., Klinger, B., Barabasi, A.-L., and Hausmann, R. (2007).The Product Space Conditions the Development of Nations. Science,317(5837):482–487.

Kuznets, S. (1973). Modern Economic Growth: Findings and Reflections.American Economic Review, 63:247–258.

Lee, N. (2011). Are innovative regions more unequal? evidence fromEurope. Environment and Planning C: Government and Policy,29(1):2–23.

Lundvall, B.-A. (1992). National Systems of Innovation: Toward a Theoryof Innovation and Interacting Learning. Pinter Publisher, London.



References IV

Malerba, F. and Orsenigo, L. (1997). Technological Regimes and SectoralPatterns of Innovative Activities. Industrial and Corporate Change,6(1):83–118.

McMillan, M. and Rodrik, D. (2011). Globalization, structural change andproductivity growth. In Bacchetta, M. and Jansen, M., editors, Makingglobalization socially sustainable, chapter 2, pages 49–84. InternationalLabour Organization and World Trade Organization, Geneva.

OECD (2015). Innovaiton Policies for Inclusive Development. Scaling UpInclusive Innovations. Technical report, OECD, Paris.

Paunov, C. (2013). Innovation and Inclusive Development. OECDPublishing, page 67.

Ravallion, M. (2004). Pro-Poor Growth: A Primer.

Ravallion, M. and Chen, S. (2003). Measuring pro-poor growth.



References V

Ruttan, V. W. (2002). Sources of Technical change: Induced Innovation,Ecvolutionary Theory and Path Dependence. In Arnulf Grubler NebojsaNakicenovic, W. D. N., editor, Technological change and theenvironment, chapter 2, pages 9–39. Resources for the Future.

Schumpeter, J. A. (1911). Theory of Economic Development: An Inquiryinto Profits, Capital, Credit, Interest, and the Business Cycle. HarvardUniversity Press, Cambridge, MA.

Syrquin, M. (1988). Patterns of Structural Change. In Chenery, H. B. andSrinivasan, T. N., editors, Handbook of Development Economics,volume 1, pages 203–273. North–Holland, Amsterdam.

UNU-WIDER (2012). Structural Change, Poverty Reduction and IndustrialPolicy in the BRICS. Technical report, UNU-WIDER, Helsinki.

Verspagen, B. (2004). Structural change and technology. A long view.Revue economique, 55(6):1099–1126.


innovation, structural change, & inclusion a cross country

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