Empirical results from a random-effects regression model show that ethnic heterogeneity has a negative effect on growth. The negative effect is seen largely in the hampering of efficiency improvements, but not capital accumulation.Ethnic fractionalization, Ethnic polarization, Efficiency improvement, Capital accumulation, Random-effects model.

Yamamura, Eiji

English

Research Papers in Economics

1 
 
Decomposition of ethnic heterogeneity on growth 
 
Eiji Yamamura 
 
Department of Economics, Seinan Gakuin University 
6-2-92 Sawara-ku, Nishijin, Fukuoka 814-8511, Japan 
Tel: +81-(0)92-823-4543; Fax: +81-(0)92-823-2506; E-mail: yamaei@seinan-gu.ac.jp 
 
 2 
 
Abstract 
 
Empirical  results  from  a random-effects  regression  model  show that  ethnic 
heterogeneity has a negative effect on growth. The negative effect is seen largely in the 
hampering of efficiency improvements, but not capital accumulation. 
 
 
Keywords:  Ethnic  fractionalization,  Ethnic  polarization,  Efficiency  improvement, 
Capital accumulation, Random-effects model. 
 
JEL classification: H11, O43   
 
 
 3 
 
1. Introduction 
Since the 1990s, there has been a growing interest among economic researchers 
in the relationship between ethnic diversity and economic performance (Alesina and La 
Ferrara,  2005).  Easterly  and  Levine  (1997)  showed  a  negative  association  between 
ethnic  heterogeneity  and  economic  growth.  Ethnic  heterogeneity  has  a  detrimental 
influence on economic development, affecting the probability of conflict and reducing 
investment  (Mauro,  1995;  Montalvo  and  Reynal-Querol,  2005a,  2005b). 1   Ethnic 
heterogeneity also possibly influences growth in other areas, an issue that is open for 
discussion.  Thus,  it  is  worthwhile   to  investigate  through  which  channels  ethnic 
heterogeneity affects growth.  
To analyze channels of  economic growth, data envelopment analysis (hereafter, 
DEA) constructs a world production frontier and then decomposes labor productivity 
growth  to  three  co mponents:  technological  catch -up,  capital  deepening,  and 
technological change  (Kumar and Russell,  2002).  In addition,  researchers can use 
regression  analysis  to  examine  how  initial  outputs  per  worker  influence  these 
components (Yamamura and Shin, 2007, 2008; Yamamura, 2011).   
This paper aims to improve the above method and then apply it in an attempt to 
provide new empirical evidence through an investigation into the influence of ethnic 
heterogeneity on growth.   
 
2. Data and Model   
Kumar and Russell (2002) used DEA to construct a cross-country data set by 
                                                   
1  Previous works examined the effect of religious heterogeneity on economic 
development, which relate to works exploring the influence of ethnic heterogeneity 
(Alesina et al., 2003; Montalvo and Reynal-Querol, 2003). 4 
 
decomposing labor productivity growth into three components. They conducted a simple 
OLS regression model with output per worker from 1965 as the independent variables 
and the dependent variables were the percentage changes between 1965 and 1990 for 
output  per  worker,  technological  change,  the  efficiency  index,  and  the  capital 
accumulation index. In their estimations, both unobservable individual and time effects 
were ignored. This led to estimation bias. 
Following Kumar and Russell (2002), this paper also uses DEA to construct a 
panel dataset for 57 countries, from 1965 to 1990, using the Penn World Table.2  With 
this dataset, I used random-effects estimations to reduce omitted variable bias caused 
by the tim e-invariant features  of the  various  countries.3  I  also incorporated year 
dummies into this model to capture  individually invariant  time-specific effects. The 
estimated function takes the following form:  
GriT-to  =  0 +  1 Ln(Output)  it0  +  2 (Ethnic  polarization)  i  +  3 (Number  of  natural 
disasters) it0 + 4 (Government size) it0 + 5 (Years of schooling) it0 +  t i    +uit,   
where GriT-to represents labor productivity growth and the change in any of the 
three dependent variables (i.e., Efficiency, Capital, and Technique) in country i from 
each base year t0 to year T (t0 = 1965, …, 1989 and T = 1966, …, 1990).  represents 
regression  parameters,     is  the  time-invariant  individual effect  of  each  country,   
represents the year specific effects, and u is an error term. As stated earlier,   and   
are controlled. The key independent variable that captures ethnic heterogeneity is the 
ethnic  polarization  index.  Classical  works  have  previously  used  an  ethnic 
                                                   
2  Kumar and Russell (2002) admitted that their method includes the possibility of an 
implosion of the technological frontier. Henderson and Russell (2005) precluded an 
implosion of the frontier over time. In this paper, it is also precluded.  
3  The independent variables used in this paper were not available for 10 of the 57 
countries. Hence, the data from only 47 countries were used in the estimation.  5 
 
fractionalization  index  to  capture  ethnic  heterogeneity  (Mauro,  1995;  Easterly  and 
Levine, 1997). In addition to the ethnic fractionalization index, an ethnic polarization 
index  has  also  been  developed  and  used  as  an  alternative  measure  (Montalvo  and 
Reynal-Querol, 2005a, 2005b; Reynal-Querol, 2002). Thus, to check the robustness of 
the estimation results, I used both ethnic fractionalization and ethnic polarization as 
proxy variables for ethnic heterogeneity.4  Ethnic heterogeneity is expected to result in 
conflict, hampering the cooperation and communication required to enhance technology 
diffusion  and  efficiency  improvement s.  Proxies  for  ethnic  heterogeneity  hold 
time-invariant features. Hence, their effects cannot be estimated when  a fixed-effects 
model is used. To examine these effects, a random-effects model is used in this paper. 
The other independent variables used in this model are the values in the base 
year t0. I have incorporated per capita GDP taken in log-form in t0 to control for initial 
levels of productivity. These data are sourced from the Penn World Table (PWT 6.3).5 
Natural disasters  are considered to influence economic growth (Skidmore and Toya , 
2002). To capture this effect, the number of natural disasters that have occurred in the 
sample countries are included.6  Government size is measured by  a country’s general 
government final consumption expenditure (% of GDP) sourced from the World Bank 
(2006).  To  capture  the  human  capital  effect,  the  number  of  years  at  school  is 
incorporated, as used by Easterly and Levine (1997).7   
                                                   
4  Data on ethnic fractionalization and polarization is available at 
http://www.econ.upf.edu/~reynal/data_web.htm (accessed on June 1, 2011). 
5  The data are available from Center of International Comparisons at the University of 
Pennsylvania. http://pwt.econ.upenn.edu/ (accessed May 1, 2007).   
6  The data were obtained from the International Disaster Database 
http://www.emdat.be (accessed on June 1, 2011). 
7  The number of years at school are not available for some years. Therefore, to construct 
panel data additional data were generated by interpolation based on the assumption of 
constant changes in rates to make up for this deficiency. The data are available from 
http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,content6 
 
 
3. Results 
The estimation results of the random-effects model with year dummy variables from 
1966 to 1990 are reported in Tables 1 and 2. Table 1 presents the results when the 
ethnic  polarization  index  is  used  as a  proxy  for  ethnic  heterogeneity, while  Table  2 
exhibits the results when ethnic fractionalization is used. In each table, the results for 
the dependent variables output per capita change are shown in column (1). The results 
for efficiency change, capital accumulation, and technological progress are shown in 
columns (2), (3), and (4).   
In Tables 1 and 2, the Hausman test does not reject the null-hypothesis that the 
differences in coefficients between a fixed-effects model and a random-effects model are 
not systematic. This result implies that the random-effects model is valid and preferred. 
I will now focus on the results of the proxy for ethnic heterogeneity. I see from Table 1 
that ethnic polarization yields the negative sign in columns (1)–(4). Furthermore, ethnic 
polarization is statistically significant at the 1% level in columns (1) and (2), but not 
statistically significant in columns (3) and (4). Table 2 also indicates the negative sign 
for ethnic fractionalization in columns (1)–(4) and statistical significance at the 1% level 
in  columns  (1)–(2),  but  not  in  columns  (3)–(4).  Thus,  it  follows  that  while  ethnic 
heterogeneity has a detrimental effect on growth and efficiency improvement, it does 
not affect capital accumulation and technological progress.   
These  estimation  results  provide  evidence  that  ethnic  heterogeneity  hampers 
economic  growth  via  an  impediment  of  efficiency  improvement,  rather  than  in  a 
reduction of capital accumulation and technological progress.   
                                                                                                                                                     
MDK:20700002~pagePK:64214825~piPK:64214943~theSitePK:469382,00.html. 
(accessed June 2, 2011).   7 
 
 
4. Conclusions 
This study used panel data from 47 countries, from 1965 to 1989, to decompose 
the effect of ethnic heterogeneity, and to examine how it influences economic growth. 
Using  a  random-effects  regression  model  with  year  dummies,  I  found  that  ethnic 
heterogeneity  has  a  negative  effect  on  growth ,  mainly  by  hampering  efficiency 
improvement,  but  not  capital  accumulation.  I  interpret these  results  to  imply  that 
ethnic  heterogeneity  hinders  cooperation  and  communication  among  individuals; 
however, cooperation and communication are important for technology diffusion as well. 
As  a  consequence,  efficiency  improvement  is  hampered,  thereby  impeding  economic 
growth. 
 8 
 
References 
Alesina,  A.,  Devleeschauwer,  A.,  Easterly,   W.,  Kurlat,  S.,  Wacziarg,  R.,  2003. 
Fractionalization. Journal of Economic Growth 8 (2), 155– 194. 
Alesina. A., La Ferrara, E. 2005. Ethnic diversity and economic performance. Journal of 
Economic Literature 43, 762-800. 
Easterly, B., Levine, R., 1997. Africa’s growth tragedy: policies and ethnic divisions. 
Quarterly Journal of Economics 112 (4), 1203–1250. 
Henderson,  J.D.,  Russell,  R.R.,  2005.  Human  capital  and  convergence:  a 
production-frontier approach. Int. Econ. Rev. 46, 1167-1205. 
Kumar,  S.,  Russell,  R.R.,  2002.  Technological  change,  technological  catch-up,  and 
capital deepening: relative contributions to growth and convergence. Amer. Econ. 
Rev. 92, 527-548. 
Mauro, P., 1995. Corruption and Growth. Quarterly Journal of Economics 110, 681– 
712. 
Montalvo,  J.G.,  Reynal-Querol,  M.  2003.  Religious  polarization  and  economic 
development. Economics Letters 80, 201-210. 
Montalvo, J.G., Reynal-Querol, M. 2005 a. Ethnic polarization, potential conflict and 
civil war, American Economic Review, 95(3). 796-816. 
Montalvo, J.G., Reynal-Querol, M. 2005 b. Ethnic diversity and economic development, 
Journal of Development Economics, 76. 293-323. 
Skidmore, M., Toya,H. 2002. Do natural disasters promote long-run growth?, Economic 
Inquiry    40(4),664-687. 
Vigdor,  J.,  2002.  Interpreting  ethnic  fragmentation  effects.  Economic  Letters  75, 
271–276.   9 
 
World Bank, 2006. World Development Indicators, CD-ROM, World Bank, Washington, 
D.C. 
Yamamura,  E.,  Shin,  E.,  2007.  Technological  change  and  catch-up  and  capital 
deepening: relative contributions to growth and convergence: Econ. Bull., 15(3), 1-8. 
Yamamura, E., Shin, E., 2008. The benefit of efficiency improvement on growth and 
convergence: a study using Japan panel data. Econ. Letters 99, 209-211.   
Yamamura, E., 2011. Decomposition of the effect of government size on growth. Econ. 
Letters 112(3), 230-232. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 10 
 
 
 
 
 
 
 
Table 1 Random-effects estimates when ethnic polarization index is used (1965–1989) 
 
Note: Not reported here, year dummies are included in all estimations as independent 
variables.  Numbers  in  parentheses  are  z-statistics  calculated  based  on  the  robust 
standard error clustered within a country. *, **, and *** indicate significance at the 10%, 
5% and 1% levels, respectively. 
 
 
 
 
 
 
 
 
 
 
 
 
  Growth 
 
(1) 
Efficiency 
improvement 
    (2) 
Capital 
accumulation 
(3) 
Technological 
progress 
(4) 
Ln(Output)  -0.002 
  (-0.72)- 
-0.001 
  (-0.84)- 
0.004 
  (1.06)- 
-0.0001 
  (-0.11)- 
Ethnic 
polarization 
-0.036*** 
(-4.51) 
-0.023*** 
(-3.70) 
-0.008 
(-1.33) 
-0.0003 
(-0.15) 
Number of natural 
disasters 
0.001** 
(2.22) 
0.001* 
(1.70) 
0.0003 
(1.07) 
0.0003*** 
(2.60) 
Government size 
 
-0.001 
(-1.06) 
0.0003 
(0.90) 
-0.0008** 
(-2.39) 
-0.0001 
(-0.84) 
Years of schooling 
 
0.001 
(0.72) 
0.0001 
(0.19) 
-0.0001 
(-0.06) 
0.0002 
(0.54) 
Constant 
 
1.07*** 
(35.8) 
1.01*** 
(53.5) 
1.00*** 
(28.9) 
1.00*** 
(107.9) 
Hausman test 
 
11.2 
p-value=0.99 
28.1 
p-value=0.45 
24.6 
p-value=0.64 
6.57 
p-value=1.00 
Groups  47  47  47  47 
Observations  1121  1121  1121  1121 11 
 
 
 
 
Table  2  Random-effects  estimates  when  ethnic  fractionalization  index  is  used 
(1965–1989) 
Note: Not reported here, year dummies are included in all estimations as independent 
variables.  Numbers  in  parentheses  are  z-statistics  calculated  based  on  the  robust 
standard error clustered within a country. *, **, and *** indicate significance at the 10%, 
5% and 1% levels, respectively. 
 
 
 
 
 
 
 
 
 
 
 
 
  Growth 
 
(1) 
Efficiency 
improvement 
    (2) 
Capital 
accumulation 
(3) 
Technological 
progress 
(4) 
Ln(Output)  -0.007** 
  (-2.10)- 
-0.004* 
  (-1.87)- 
0.003 
  (0.70)- 
-0.0003 
  (-0.23)- 
Ethnic 
Fractionalization 
-0.044*** 
(-4.56) 
-0.023*** 
(-3.16) 
-0.012 
(-1.50) 
-0.001 
(-0.46) 
Number of natural 
disasters 
0.001*** 
(3.52) 
0.001*** 
(2.61) 
0.0003 
(1.15) 
0.0003** 
(2.50) 
Government size 
 
-0.0004 
(-0.88) 
0.0005 
(1.25) 
-0.0008** 
(-2.39) 
-0.0001 
(-0.80) 
Years of schooling 
 
0.001 
(0.86) 
0.0002 
(0.25) 
-0.0001 
(-0.07) 
0.0002 
(0.56) 
Constant 
 
1.11*** 
(35.9) 
1.03*** 
(47.9) 
1.01*** 
(26.1) 
1.00*** 
(98.1) 
Hausman test 
 
8.99 
p-value=0.99 
28.1 
p-value=0.45 
28.8 
p-value=0.41 
5.97 
p-value=1.00 
Groups  47  47  47  47 
Observations  1121  1121  1121  1121 

a. Ethnic polarization, potential conflict and civil war,

Africa’s growth tragedy: policies and ethnic divisions.

b. Ethnic diversity and economic development,

Corruption and Growth.

Decomposition of the effect of government size on growth.

Do natural disasters promote long-run growth?,

Ethnic diversity and economic performance.

Human capital and convergence: a production-frontier approach.

Interpreting ethnic fragmentation effects. Economic Letters 75, 271–276. 9 World Bank,

Religious polarization and economic development.

Technological change, technological catch-up, and capital deepening: relative contributions to growth and convergence.

The benefit of efficiency improvement on growth and convergence: a study using Japan panel data.

Decomposition of ethnic heterogeneity on growth

Decomposition of ethnic heterogeneity on growth

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