Territorial Convergence in Ecuador: The Role of Economic Sectors

Territorial Convergence in Ecuador: The Role of Economic Sectors

Territorial Convergence in Ecuador:
The Role of Economic Sectors and Spatial Spillovers
Rodrigo Mendieta Muñoz1, Nicola Pontarollo2
October, 2015
Abstract
The paper analyses the subnational convergence process of Ecuador during the period 2007-2013
through a spatial panel econometric technique. The advantage of this technique is to provide a reliable
estimation because it takes into account the spatial interaction in the territory. Ecuador is characterised
by severe cantonal disparities, reflected in a heterogeneous economic and social geography that can
undermine a balanced development and a positive spatial multiplier effect within the country. In this
extent we measure the sectoral effects on economic growth proving that, despite the change of
productive matrix pushed by the government, this process if far to be completed. In particular the
country is too much focussed into low productive sectors which depress economic growth and the
manufacture sector is too much concentrated in few areas, preventing its possible positive effect into
the whole economy.
Keywords: Subnational convergence, Panel Spatial Econometrics, Economic Sectors
1 Facultad de Ciencias Económicas y Administrativas and Grupo de Investigación en Economía Regional, University of Cuenca,
Ecuador: [email protected]
2 Department of Economics, University of Verona, Italy, and Grupo de Investigación en Economía Regional, University of Cuenca,
Ecuador. Email: [email protected]
1
1. Introduction
Ecuador has been characterized by persisting severe cantonal disparities, reflected in a heterogeneous
economic and social geography, which accounts for cantons with asymmetric characteristics in terms
of productivity and competitiveness, as well as in terms of differentiated population and social
dynamics (Mendieta, 2015a; Ramón-Mendieta et al., 2013; Alvarado, 2011). These asymmetries
between subnational areas can inhibit the growth of domestic production and contribute to its
instability (CEPAL, 2010), becoming a problem of circular causation that can undermine the future
development of the whole country.
This process of unbalanced growth justifies the implementation of compensatory territorial policies
such as incentives for private investment, tax breaks, and provision of infrastructure in lagging
provinces (Espina, 1994). This kind of interventions, that started in the 90s together with policies and
reforms whose aim was to increase the decentralization and the autonomy in of the institutions that
manage development, obtained limited benefits in terms of reduction of asymmetries (Barrera, 2007).
From 2008, with the new constitution, the process of territorial compensation in Ecuador made
another push, with the creation of the National Secretariat of Planning and Development
(SENPLADES),
which coordinates the
processes of autonomy, promotes governance
decentralization, and seeks to expand local development capacities. In this context, the Central
Government has started the project called “Changing Productive Matrix” which wants to achieve
“productive diversification based on adding value; promotion of the exports and their expansion in
terms of products and destinations: substitution of imports, including the different actors;
deconcentration of production from the existing poles to the territories, and the continuous
improvement of productivity and competitiveness across all sectors of the economy” (Plan Nacional
del Buen Vivir, PNBV, 2013 - 2017: 73). More explicitly, as a policy guideline, it aims at closing
economic and social gaps at territorial level.
In this extent, in the last two decades, considering that the prevailing growth and development theories
could no longer explain empirical growth patterns, we assisted to a rethink of how economic
development takes place and of its relation with economic geography. Globalization has made
localities and their interaction more important for economic growth and prosperity (Rodríguez-Pose,
2011). The territory became an active part in the economic structure and the importance of aspects
such as human capital and innovation (endogenous growth theory), agglomeration and distance (new
economic geography), and institutions (institutional economics) was taken to the fore (Barca et al.,
2012).
This study focuses on the role of economic sectors into economic growth of the 221 Ecuadorian
cantons using spatial econometric tools. The classical growth regression, thanks to the recent
2
development of high-quality statistics at subnational level, has been augmented including sectoral
weights into the analysis. Ecuador is characterised by a relatively strong share of non-financial
services and agriculture, while it is widely differentiated in terms of manufactory, with some cantons
and provinces in which it is very concentrated. Furthermore, Ecuador has still low infrastructure level
in some areas of the country that represents a problem in allowing connectives and, finally, spatial
diffusion of economic phenomena. In principle, widespread differences among neighbour locations
could prevent and/or make relatively complicated the application of policies because their effects may
be confined to a very limited spatial dimension. In this extent, to test for spatial spillovers and for the
role of economic sectors in context of “Changing Productive Matrix” objective, the adopted
methodology is a spatial panel data estimation (Elhorst, 2009).
The paper is organised as follows. In the second section a brief overview of the economic structure
of Ecuador is given. In the third section it is describe the empirical model and the estimation
technique, while in fourth we illustrate the results of our analysis. In last part, finally, we discuss the
conclusions of this research.
2. Approach to subnational economic disparities in Ecuador
The Republic of Ecuador, located northwest of South America, between Colombia (north) and Peru
(south), is divided into 24 provinces, 221 munipalities or cantons and 1,228 parishes, in an area of
283,500 squared kilometrs, with around 16 millions inhabitants.
During the eighties and nineties, like many Latin American countries, Ecuador's economy was
characterized by severe economic downturns. These were accompanied by political, social and
institutional instability. At subnational level this performance is reflected in a sharp economic and
social disparities (Mendieta, 2015a).
In the last years, according to the PNBV, in order to smooth the territorial gaps, many strategies has
been implemented such as an unprecedented level of public investment deployed throughout the
country, especially on roads, hydroelectric projects and in various areas among which health and
education, which was made possible from the significant government revenues derived mainly from
high oil prices and a more efficient tax collection3. Also, through the Code of Land Management,
Autonomy and Decentralization (COOTAD) in force since 2010, several institutional mechanisms that
promote decentralization of governance, and seeks to expand the capacity for autonomy and local
development have been implemented.
3
Since the seventies, the oil extraction is the most important activity for Ecuadorian economy. In 1974, oil represented
42.51% of public sector revenues, 62.01% of exports and 13.15% of national value added. By 2014, these proportions
were 18.47%, 51.70% and 10.41% respectively (Central Bank of Ecuador, 2015).
3
Figure 1: Provinces of Ecuador
ID
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Province
Azuay
Bolívar
Cañar
Carchi
Chimborazo
Cotopaxi
El Oro
Esmeraldas
Galápagos
Guayas
Imbabura
Loja
Los Ríos
Manabí
Morona
Napo
Orellana
Pastaza
Pichincha
Santa Elena
Santo Domingo
Sucumbíos
Tungurahua
Zamora
Area (km2)
8 639
3 254
3 908
3 699
6 479
6 569
5 988
14 893
8 010
17 139
4 599
11 027
6 254
18 400
25 690
13 271
20 773
29 520
9 494
3 763
4 180
18 612
3 334
10 556
Source: authors’ elaboration on the basis of INEC.
These actions and strategies begin to show their effects in terms of economic growth Martin (2012)
and poverty reduction (Mideros, 2012). World Bank data, on the other hands, highlight that, from
2006 to 2011, the rate of extreme poverty was reduced from 16.9% to 7% and the Gini index
decreased from 54% to 48.7%. In this extent, the economic growth can be considered inclusive,
consistent with the Boston Consulting Group report according to which, from 2006 to 2012, Ecuador
was the oil country that better transformed its oil wealth on well-being (Beal et al., 2012).
But were these apparent positive results distributed equally within the country? Is it possible to speak
of balanced results? Are these performances accompanied by a process of territorial convergence?.
These questions implicitly imply to evaluate how national and local productive matrix has evolved in
order to determine if the process of improvement in well-being is sustainable over time.
This is shown in table 1 and 2 where we have the average sectoral weight by province in 2007 and
2013, respectively4. In addition, in the last two columns provincial Gross Value Added per capita
(GVA/pop) and population per squared kilometer (Density) is reported. The data are provided by the
4
Following the indications of the Central Bank of Ecuador and some other authors, we excluded the gross value added
related to oil production because it is does not create wealth in the cantons where it is produced (Mendieta, 2015a; RamónMendieta et al., 2013). Provinces of Santa Elena and Santo Domingo were created after 2007 from the provinces of
Guayas and Pichincha respectively and then they were included only in 2013.
4
Central Bank of Ecuador and the GVA in USD is in constant prices with base year 20075. Excluding
Galápagos, Pichincha, Guayas and Azuay which have the highest level of Gross Value Added per
capita in 2007 and 2013, a significant performance is shown by Tungurahua and El Oro in the last
considered year. On the contrary, Morona Santiago, Napo, Zamora Chinchipe, Bolivar, Chimborazo
and Orellana have the lowest level of development in both years. Besides persisting gap between rich
and poor provinces, the interesting point is that the more developed ones have the highest population
density, just concentrated in provincial capital cities. In contrast, less developed provinces have
greater dispersal of population.
With regard to the provincial production structure, minimal changes are observed between 2007 and
2013 (first 10 columns of Tables 1 and 2). The subnational structure is predominantly based on nonfinancial services, that include trade and accommodation and food services, transportation,
information and communications, and real estate professionals services.
The manufacturing sector maintained a weight of around 16% of domestic Value Added. In 2013, in
addition to Pichincha, Guayas and Azuay provinces, also Esmeraldas, Santa Elena, Manabi,
Tungurahua, Sucumbíos, Santo Domingo and Imbabura reach an industrial weight equal to 10% of
Value Added. These provinces are also characterised by the highest rates of population density. It is
worth nothing that the manufacturing sector is very concentrated in few cantons. In particular
Guayaquil and Quito create around 60% of the manifacturing Value Added and, if we consider only
the provincial capitals, they produce the 80% of total manifacturing Value Added.
The weight of the agricultural sector is important in some provinces with low levels of development
like Los Rios, Esmeraldas, Cotopaxi, Carchi y Bolivar. In contrast El Oro, which has a important
banana production, shows higher GVA per person.
According to the actual development model, the public administration sector, plus the education and
health services, are important for creation of economic value especially in poor provinces with low
levels of population density like Morona Santiago, Napo, Bolivar, Pastaza, Zamora Chinchipe and
Orellana. In connection with this, the construction sector shows an increase between 2007 and 2013
in all provinces. These results are associated with public investment in infrastructure and housing.
5
Central Bank of Ecuador does not produce annual cantonal data on Gross Domestic Product. Anyway, GVA per head is
one of the headline indicators used, for example, in UK regional policy (Dunnell, 2009). According to BIS (2010: 3), in
fact, “Gross Value Added per head is typically used for considering performance levels within a country. Although there
are some criticisms of this metric it has the advantage that it provides a full picture of performance implicitly including
both productivity and employment effects”. In addition, GVA, which measures the contribution to the economy of each
individual producer, industry or sector is used in the estimation of Gross Domestic Product (GDP) when using the
production or income approaches. In this extent, GVA can be used as a proxy of GDP.
5
Table 1: Percentage of contribution by sector to total provincial Value Added 2007
Province
Azuay
Bolivar
Cañar
Carchi
Cotopaxi
Chimborazo
El Oro
Esmeraldas
Guayas
Imbabura
Loja
Los Rios
Manabi
Morona Santiago
Napo
Pastaza
Pichincha
Tungurahua
Zamora Chinchipe
Galapagos
Sucumbios
Orellana
Santo Domingo
Santa Elena
Total
In bold the main sector
Agricult.
Mines
Manufac.
Water
Construct.
Basic serv.
Fin. serv
Pub. adm.
Teaching
Health
GVA/pop
Density
0.0550
0.3400
0.2070
0.2100
0.2690
0.1340
0.2540
0.2030
0.0780
0.0960
0.1600
0.3860
0.2110
0.1770
0.1380
0.0750
0.0510
0.0680
0.1550
0.1790
0.1250
0.1930
0.0090
0.0000
0.0030
0.0010
0.0000
0.0010
0.0230
0.0000
0.0040
0.0010
0.0010
0.0000
0.0010
0.0000
0.0000
0.0000
0.0020
0.0010
0.0340
0.0000
0.0000
0.0000
0.1480
0.0170
0.0850
0.0510
0.0700
0.0820
0.0430
0.3920
0.1930
0.0720
0.0400
0.0360
0.1560
0.0170
0.0150
0.0480
0.1870
0.1180
0.0170
0.0080
0.3340
0.1880
0.1170
0.0000
0.0000
0.0050
0.0090
0.0070
0.0020
0.0000
0.0080
0.0020
0.0010
0.0030
0.0010
0.0590
0.0140
0.0000
0.0040
0.0790
0.0070
0.0000
0.0000
0.0000
0.1040
0.1030
0.1560
0.1110
0.1340
0.1590
0.1100
0.0760
0.0850
0.1660
0.1670
0.0720
0.1130
0.1130
0.1610
0.1440
0.0770
0.1260
0.1220
0.0930
0.0800
0.0540
0.3460
0.2150
0.3170
0.3550
0.2990
0.3370
0.3420
0.1710
0.4560
0.4170
0.3350
0.2680
0.2970
0.2380
0.2780
0.3780
0.4550
0.3850
0.2410
0.5020
0.2330
0.2230
0.0480
0.0140
0.0290
0.0170
0.0150
0.0240
0.0170
0.0050
0.0230
0.0310
0.0380
0.0090
0.0140
0.0170
0.0090
0.0200
0.0490
0.0330
0.0120
0.0090
0.0090
0.0110
0.0700
0.1390
0.0710
0.1370
0.0710
0.1070
0.0750
0.0620
0.0440
0.0870
0.1420
0.0660
0.0780
0.2170
0.2180
0.1980
0.0560
0.0630
0.2410
0.1550
0.1030
0.1950
0.0550
0.1220
0.0900
0.0790
0.0890
0.1000
0.0740
0.0650
0.0560
0.0910
0.0670
0.0950
0.0900
0.1090
0.1030
0.0840
0.0440
0.0660
0.1190
0.0150
0.0840
0.1010
0.0350
0.0340
0.0340
0.0280
0.0320
0.0380
0.0340
0.0180
0.0270
0.0260
0.0450
0.0520
0.0300
0.0400
0.0530
0.0440
0.0280
0.0480
0.0480
0.0090
0.0220
0.0240
3635.236
1568.134
2232.576
2070.465
2130.334
1852.564
2662.382
3236.061
3528.432
2218.044
2051.096
2113.852
2056.027
1401.639
1558.224
2124.35
4585.378
2821.255
1558.292
7115.918
2385.675
1881.13
81.2036
45.2529
69.4800
42.4252
63.4738
67.9295
99.8291
28.4787
194.7800
82.5790
39.1593
101.646
68.7892
5.68900
7.52789
2.55119
211.580
142.247
8.22337
2.8239
8.75076
5.81498
0.11181
0.00371
0.16411
0.01372
0.09411
0.39811
0.03053
0.06523
0.06161
0.03052
3138.64
53.1945
6
Table 2: Percentage of contribution by sector to total provincial Value Added 2013
Province
Azuay
Bolivar
Cañar
Carchi
Cotopaxi
Chimborazo
El Oro
Esmeraldas
Guayas
Imbabura
Loja
Los Rios
Manabi
Morona Santiago
Napo
Pastaza
Pichincha
Tungurahua
Zamora Chinchipe
Galapagos
Sucumbios
Orellana
Santo Domingo
Santa Elena
Total
In bold the main sector
Agricult.
0.0349
0.2083
0.1101
0.2423
0.2408
0.1279
0.2514
0.3376
0.0843
0.0727
0.1041
0.4123
0.1261
0.0724
0.1072
0.0994
0.0406
0.0532
0.0664
0.0898
0.1118
0.1793
0.0999
0.0668
0.1033
Mines
0.0099
0.0000
0.0024
0.0005
0.0006
0.0005
0.0378
0.0002
0.0033
0.0010
0.0004
0.0000
0.0010
0.0001
0.0000
0.0000
0.0033
0.0004
0.0232
0.0000
0.0000
0.0000
0.0002
0.1599
0.0037
Manufac.
0.1844
0.0204
0.0767
0.0325
0.0516
0.0880
0.0480
0.1876
0.2108
0.1193
0.0308
0.0350
0.1656
0.0287
0.0162
0.0518
0.1691
0.1541
0.0168
0.0109
0.1477
0.0256
0.1235
0.1783
0.1576
Water
0.0362
0.0100
0.0104
0.0104
0.0107
0.0145
0.0111
0.0101
0.0129
0.0172
0.0106
0.0085
0.0104
0.0212
0.0121
0.0137
0.0087
0.0207
0.0151
0.0073
0.0064
0.0260
0.0121
0.0161
0.0127
Construct.
Basic serv.
0.1787
0.1311
0.2155
0.1089
0.1268
0.1670
0.1140
0.0809
0.1253
0.1844
0.1713
0.0883
0.1595
0.1209
0.1154
0.1328
0.1118
0.1032
0.1413
0.0938
0.1361
0.0733
0.1195
0.2438
0.1283
0.3425
0.2645
0.3275
0.3509
0.3334
0.3036
0.3273
0.1905
0.3835
0.3785
0.3940
0.2607
0.3091
0.3182
0.3282
0.3242
0.4021
0.4446
0.3216
0.5715
0.3583
0.3246
0.3855
0.3136
0.3657
7
Fin. serv
0.0497
0.0277
0.0361
0.0223
0.0203
0.0292
0.0190
0.0044
0.0271
0.0250
0.0353
0.0077
0.0138
0.0224
0.0123
0.0316
0.0486
0.0474
0.0089
0.0072
0.0094
0.0147
0.0178
0.0064
0.0317
Pub. adm.
0.0535
0.1446
0.0857
0.1026
0.0804
0.0991
0.0652
0.0638
0.0403
0.0668
0.1090
0.0623
0.0728
0.1595
0.1755
0.1491
0.1130
0.0526
0.1962
0.1273
0.0884
0.1516
0.0912
0.0560
0.0767
Teaching
0.0537
0.1252
0.0776
0.0792
0.0850
0.0993
0.0616
0.0872
0.0526
0.0762
0.0812
0.0831
0.0817
0.1541
0.1398
0.1179
0.0356
0.0625
0.1413
0.0395
0.0947
0.1416
0.0884
0.0949
0.0593
Health
0.0429
0.0496
0.0509
0.0400
0.0378
0.0580
0.0382
0.0273
0.0362
0.0469
0.0561
0.0309
0.0480
0.0905
0.0795
0.0662
0.0274
0.0465
0.0642
0.0214
0.0290
0.0434
0.0514
0.0125
0.0365
GVA/pop
Density
3827.7411
1669.8398
2621.9414
2346.3485
2390.9688
2035.5050
3326.9562
2806.5159
4087.5734
2883.4970
2496.1167
2503.6945
2511.0319
1638.0233
1927.7632
2315.2185
5730.6372
3035.9004
1696.1812
4940.1039
2405.7446
1766.9825
2535.3713
2282.6649
3563.7570
94.0983
50.1126
79.2407
46.7308
72.7545
75.6534
113.3054
36.0169
256.8649
94.2963
43.7998
116.8272
77.4618
6.9158
9.1479
3.1802
297.3308
160.6691
9.7016
3.4939
10.8324
6.7289
116.9389
92.7796
61.5688
Summarising the reported results, the production structure has only little changed, both at national
and subnational levels. The main critical points are the persistent importance of agricultural and nonfinancial sectors characterised by low productivity and labor skills and the excessive concentration
of manufacturing sector.
Furthermore, in accordance with the increase of central government spending, sectors like public
administration, education and health have increased in recent years. Their magnitude, anyway,
according to the tables below, seems to be not enough to create a multiplier effect in order to push
the change to a more productive structure. On the contrary, in provinces with the lowest population
density and GVA per hear they produce a large part of Gross Value Added both in 2007 and 2013.
If, from a side, the effects of public sector and of connected activities can be hampered by the little
population concentration (i.e. absence of economy of scale), on the other hand, their potential positive
impact would have been connected to the capacity of local policy makers to tailor policies related to
the specificity of each territory (Barca et al., 2012). This eventuality, in consideration of the low
performances of sparsely populated provinces and of the unchanged economic structure, has not been
accomplished, with the risk to undermine their long-run development perspectives.
These disparities, according with CEPAL (2009) and Silva (2005) would form different typologies
of territories, which would amplify inequalities at a higher level of geographical breakdown.
To check this regularly in the Ecuadorian case, figure 2 plots the average rate of economic growth
between 2007 and 2013 at cantonal level on the vertical axis against the GVA per capita on the
horizontal axis (in logarithms). The names in the figure correspond to the provincial Capital cantons.
In this period a cantonal average growth rate of -1.7% is recorded, with 58% of cantons characterized
by negative growth, and a similar proportion with a GVA per person in 2013 less than cantonal
average in 2007.
The subnational economic gap is evident, especially between provincial capitals and other cantons.
Most of these capitals have both a higher average level of Gross Value Added per capita and a higher
rate of growth. According to the aforementioned, Quito, Guayaquil and Cuenca, capital of Pichincha,
Guayas and Azuay respectively, which jointly account for over 45% of produced GVA, present the
best performance. Similarly, the poorest capitals correspond to the most backward provinces. Thus,
behind the negative relation between the initial cantonal GVA per person and growth, which means
that convergence is present, there is a strong problem related to the lack of growth performances of
most advanced cantons. In addition, as shown by Mendieta (2015b) this process does not correspond
to a decrease of disparity levels.
8
Figure 2: Relation between GVA per person in 2007 and average growth 2007-2013.
The cantonal low or negative growth is often related to the too strong dependence form row materials
(essentially agricultural products and oil) which is subject to the fluctuations of international prices.
In addition, these cantons and the surrounding ones have often grown for the services and productions
provided to the workers that migrate from other cantons. This created a “bubble” that involved in
particular agricultural, non-financial services and construction sectors, which create employment but
that are not the basis for a lasting growth. As a consequence, the row materials prices fluctuations
affect the economy in two ways: the first regards the various directly and indirectly connected sectors,
and the second regards to the multiplier effect on neighbour cantons.
On this bases, in the next point we delve over the Ecuadorian subnational convergence and the roles
of sectoral structure.
3. Convergence and econometric model
The specification of the empirical model for panel growth regressions is:
𝑔𝑟𝑖,𝑡 = 𝛼 + 𝛽𝑙𝑜𝑔(𝑦𝑖,𝑡−1 ) + 𝛿sectors𝑖,𝑡−1 + 𝜇𝑖 + 𝜀𝑖𝑡
(1)
Where the dependent variable gri,t represents the cantonal annual growth rate of per capita Gross
Value Added; 𝛼 is a constant term; 𝜇𝑖 are dummies specific to canton i which control for unvarying
9
factors determining differences in the steady states across cantons6; 𝑦𝑖,𝑡−1 is the per capita GVA in
canton i, of which there are 221, in the period 2007-20137; 𝛽, if negative, is the coefficient related to
the annual rate at which an economy converges to the long-run steady state. The additional variables
sectors𝑡−1 represent the relative weight of the GVA produced by the different economic sectors,
which assume an important role in light of the “Changing of Productive Matrix” plan and that, if
jointly significant, means that each economy converge to its own steady state because each one has
different structural characteristics and production function. In this last case we speak of conditional
convergence. On the contrary, i.e. if the additional variables are not significant, there is absolute
convergence: all economies converge to the same steady state because they are not structurally
different.
When dealing with data at regional or subnational level, standard growth regression models can suffer
of misspecification problems (McMillen, 2003; Fingleton and Lopez-Bazo, 2006). A number of
factors either unobservable or not properly measured related, for example, to culture, social capital
and institutional characteristics can affect regional performances. These factors, often spatially
correlated, i.e. with similar values in space, and can lead to spatially correlated error in regression
models, that, if ignored, may lead to biased results and hence misleading conclusions. In order to deal
with this issue, Fingleton and Lopez-Bazo (2006) show that scholars tend to use growth models that
incorporate spatial dependence as either autoregressive term or spatial error.
Another possibility, according to LeSage and Fischer (2008), is, in order tackle the issue of the
presence of spatial dependence in the disturbances and of omitted variables that exhibit correlation
with included variables is to use a spatial Durbin model.
As shown by the results of the analysis in the followin section, the ad-hoc tests to check which is the
model to use in this context confirm that the framework proposed by LeSage and Fischer (2008) is
the correct one.
The regression model, then, becomes:
𝑔𝑟𝑖,𝑡 = 𝜌𝐖𝑔𝑟𝑖,𝑡 + 𝛼 + 𝛽𝑙𝑜𝑔(𝑦𝑖,𝑡−1 ) +
𝜑𝐖𝑙𝑜𝑔(𝑦𝑖,𝑡−1 ) + 𝛿sectors𝑡𝑖,−1 + 𝜂𝐖sectors𝑖,𝑡−1 + 𝜇𝑖 + 𝜀𝑖𝑡
(2)
Where W represents the spatial weights matrix that, as customary in literature, it is a queen
standardized by row and the convergence speed λ is determined from the following relation:
(𝛽 + 𝜑)/(1 – 𝜌) = (1 − e−𝜆×𝑇 ).
6
As observed by Elhorst (2009), often find weak evidence in favor of spatial interaction effects is found when time period
fixed effects are also accounted for. This is much more valid when dealing with a country in which most variables tend
to increase and decrease together in different spatial units because follow the national evolution over time.
7 In literature Henley (2005) uses GVA per head to measure convergence of UK regions.
10
According with Elhorst (2009), the spatial econometric model for a panel of N observations over T
time periods can be estimated along the same lines as the cross-sectional model, i.e. using Maximum
Likelihood (ML), provided that all notations are adjusted from one cross-section to T cross-sections
of N observations.
The partial derivative of Spatial Durbin Model, for the presence of the spatial autoregressive
parameter, is not straightforward. Differently from linear regression the partial derivative of the
dependent variable with respect to the explanatory variable, the marginal effects are described as
follow:
𝜕𝑔𝑟𝑖,𝑡
𝜕log(𝑦𝑖,𝑡−1 )
= (𝐈 − 𝜌𝐖)−1 (𝐈𝛽 − 𝐖𝜑)
(3)
Estimated effects do not depend only from β, but also from the sign and magnitude of ρ and φ. At this
regard LeSage and Pace (2009, 2009a) suggest the following scalar summary measures:
a. the average direct effect constructed as an average of the diagonal elements of (𝐈 − 𝜌𝐖)−1 (𝐈𝛽 −
𝐖𝜑);
b. the average indirect effect constructed as an average of the off-diagonal elements of
(𝐈 − 𝜌𝐖)−1 (𝐈𝛽 − 𝐖𝜑), where the off-diagonal row elements are summed up first, and then an
average of these sums is taken;
c. the average total effect is the sum of the direct and indirect impacts.
The key variable trough which spatial spillovers effects are transmitted is ρ: it expresses the relation
between dependent variable and its spatial lag. According to Fischer et al. (2009), the indirect effect
estimates, that correspond to spatial spillovers, can be interpreted in two ways. One interpretation
reflects how a change in the level of a variable for the study canton impacts the GVA per head growth
of other cantons which in turn negatively influences our typical canton’s GVA per head growth due
to the presence of negative and significant spatial dependence (ρ) on neighboring cantons’ GVA per
capita growth.
According to the second interpretation, spatial spillovers measure the cumulative impact of a change
in the initial level of a variable in a canton averaged over all other canons. The impact of a changing
in a single canton’s variable level on each of the other cantons’ GVA per head growth is small, but
cumulatively often exceeds the direct effect.
The interpretation of the estimate of the indirect impact is closely related to the concepts of average
total impact from and to an observation (LeSage and Pace, 2008). In the first case a change in initial
variable level impacts other cantons’ GVA per head growth, which, though spatial autocorrelation
coefficient ρ, influences study canton’s GVA per head growth. Essentially this is given by the sum of
the j-th column of (𝐈 − 𝜌𝐖)−1 (𝐈𝛽 − 𝐖𝜑). In the latter case a change in the initial level of a variable
11
of all other cantons affects GVA per head growth of a typical canton. In this case the effect is given
by the sum of the j-th row of (𝐈 − 𝜌𝐖)−1 (𝐈𝛽 − 𝐖𝜑).
4. Estimation results
The estimation results are in table 3. The estimation has been performed with both standard and spatial
panel technique and for both growth and level regression. To test the hypothesis whether the spatial
Durbin model can be simplified to the spatial error model one may perform a Wald or LR test. The
results reported for the two regressions confirms the choice of spatial Durbin both with Wald test and
LR test. Similarly, the hypothesis that the spatial Durbin model can be simplified to the spatial lag
model must be rejected for both Wald test and LR test. This implies that spatial error and spatial lag
models must be rejected in favor of the spatial Durbin model. Regarding the choice of fixed effect,
the results of Hausman's specification test indicate that the random effects model must be rejected in
all cases in favor of fixed effect.
As highlighted in the previous paragraph, table 3 does not allow to read the partial effects of the
variables of Spatial Durbin Model8, but it reports at least other two important outcomes: the first is
the joint statistical significance of regressors related to sectoral structure in growth regression (F-test
= 4.52, with p-value < 0.01), which implies that there is conditional convergence, and the second is
the statistical significance and magnitude of the spatial lag parameter, which gives information
regarding spatial spillovers. The spatial autoregressive term ρ is equal to around 0.27 in both cases.
The multiplier effect, thus, is around 1.40, which means that 40% of growth (GVA per head) is
already reflected in neighborhood growth (GVA per head), through indirect reaction effects from
neighbors that depends from the interaction among cantons in the country. As shown in the table,
failure to account for this redundancy would lead the OLS estimates of the direct (marginal) impacts
biased upwards in magnitude, because the model is misspecified by omission of spatial spillover
effects. The spatial Durbin model accounts for the redundancy induced by spatial autocorrelation in
explanatory variables as well as spillover effects (interactions) among cantons that lead to
interdependence in cantons’ behaviour.
8
In Appendix A the two models are estimates using two alternative spatial weights matrix specifications: queen matrix
of degree 2 and knearneigh of order 5 and the results are confirmed. The spatial weights matrix chosen for the model
presented in the text guarantees the lowest AIC.
12
Table 3: Estimation results
Growth regression
Level regression
Fixed effect
Sp. Durbin Fixed Effects
Fixed effect
Sp. Durbin Fixed Effects
GVA/pop
-0.558646***
-0.484414***
(-19.0563)
(-14.89264)
Agricult.
-0.660982***
-0.623907***
-0.323446***
-0.422615 ***
(-4.76412)
(-4.267607)
(-2.639105)
(-3.320153)
Mines
-0.18782
-0.116192
-0.301372*
-0.317334 *
(-0.89808)
(-0.542392)
(-1.714591)
(-1.767734)
Manufac.
0.004634
0.032708
0.02471
0.012859
(0.058346)
(0.389477)
(0.329596)
(0.163173)
Wather
-0.638562***
-0.505776**
0.150382
0.143614
(-2.62580)
(-2.017313)
(0.721993)
(0.672185)
Construct.
-0.345284**
-0.32589*
-0.773991***
-0.996743 ***
(-2.18238)
(-1.888022)
(-5.52865)
(-6.696764)
Basic serv.
-0.399461***
-0.311218**
-0.479902***
-0.56558 ***
(-2.99249)
(-2.235447)
(-4.042562)
(-4.605736)
Fin. serv
-2.76394***
-0.919766
-3.702141***
-3.240624 ***
(-3.32684)
(-1.037408)
(-5.679752)
(-4.688952)
Pub. adm.
-0.891434***
-0.596144***
-1.987847***
-2.170926 ***
(-4.43791)
(-2.798952)
(-11.73778)
(-12.350536)
Teaching
-0.721497***
-0.155097
-3.209762***
-3.493777 ***
(-3.26248)
(-0.633182)
(-17.75647)
(-18.240437)
Health
-0.841962**
-0.45467
-2.094742***
-2.150659 ***
(-2.35733)
(-1.22645)
(-7.068351)
(-7.04409)
W×GVA/pop
-0.088618
(-1.616013)
W×Agricult.
-0.063956
0.68959 ***
(-0.253164)
(3.153001)
W×Mines
-0.093373
0.016743
(-0.521087)
(0.109976)
W×Manuf.
-0.140098
-0.043021
(-1.020512)
(-0.333831)
W×Wather
-0.089434
0.006115
(-0.197299)
(0.0158)
W×Construct.
-0.094116
1.091239 ***
(-0.336522)
(4.49605)
W×Basic serv.
-0.307921
0.670966 ***
(-1.257106)
(3.113458)
W×Fin. serv
-5.56791***
-0.434561
(-3.593327)
(-0.353163)
W×Pub. adm.
-0.14004
1.392347 ***
(-0.369571)
(4.278844)
W×Teaching
-1.284715***
2.522441 ***
(-3.071721)
(7.30082)
W×Health
-0.615525
0.412515
(-0.925208)
(0.740366)
ρ
0.263962***
0.278986 ***
(7.929891)
(9.125238)
R-squared
0.2645
0.4433
0.4141
0.9467
corr-squared
0.2589
0.3115
0.4106
0.4357
sigma^2
0.0175
0.0182
0.0164
0.017
N° obs
1326
1547
log-likelihood
869.78
1049.37
F-test cond. regr.
4.52 (p-val. < 0.01)
Wald sp. lag
48.87 (p-val. < 0.01)
82.55 (p-val. < 0.01)
LR sp. lag
55.44 (p-val. < 0.01)
93.64 (p-val. < 0.01)
Wald sp. error
52.25 (p-val. < 0.01)
38.30 (p-val. < 0.01)
LR sp. error
62.35 (p-val. < 0.01)
44.19 (p-val. < 0.01)
Hausman test
383.39 (p-val. < 0.01)
630.05 (p-val. < 0.01)
*Significant at 1%, ** significant at 5%, *** significant at 10%. t-stat in brackets. Source: authors’ elaboration.
13
Table 4 reports the estimated direct, indirect and total effects of the growth regression. The results
show that convergence is taking place and the average rate is 9.59%, with a half-life of only 7.22
years9. As the reported values concern the conditional convergence, this means that Ecuadorian
cantons are very close to their own steady state, which signifies that, in order to guarantee a lasting
growth, it is extremely urgent to intervene in order to increase technology levels to make more
efficient cantonal productive structure. In addition, in the extent in which we have conditional
convergence, this makes that cantons will reach very differentiated steady state because each single
economy will converge to its own steady state. The result of this process is to increase country
inequality, which is exactly what PNBV is trying to avoid.
The signs related to the significant sectors are negative. Agriculture, which has a prevailing role in
the creation of gross value added in Ecuador, is also characterized by a low productivity and this is
reflected into its contribution to growth (Hausmann et al., 2010). As expected, it has a global effects,
which means that it involves the entire country through the multiplier effect. Water procurement, as
well as construction have only a strictly direct effect, which is probably related to the fact that these
sectors are well developed only in some cantons, which are located mainly in the province of Zamora
and in cantons where mining and big public infrastructures, as hidroelectric, are been build. Basic
services, as well as agriculture, is a very low productive sector, which doesn’t only characterize less
developed cantons. This is shown by the significant indirect and total effect, which proves that the
negative effects spreads over the whole economy exceeding cantonal borders. Financial sectors have
a negative indirect and total impact which can be related to the lack of financial services in many
cantons and the concentration in only some of them. This causes that less endowed ones suffer the
competition of neighbor endowed cantons which exploit the resources of the weaker. The negative
impact of public administration and teaching is related to the high weight of these sectors into low
productive areas, where they are much more concentrated. At this regard, here we can measure the
inefficiency of these sectors into promoting growth in two extents: the first is in failure into fostering
the change in the productive matrix shown also in tables 1 and 2, and the second in being productive
by themselves.
The implied speed of convergence is calculate as 𝜆 = −ln(1 − (𝛽 + 𝜑)⁄(1 – 𝜌))⁄𝑇 . The time τ it takes to move half
way to the balanced growth path is calculated as: 𝜏 = −ln(0.5)/𝜆 .
9
14
Table 4: Estimated effects growth regression
Direct
Indirect
-0.500575 ***
-0.282579 ***
(-15.45896)
(-4.620095)
Agricult.
-0.641355 ***
-0.292135
(-4.285698)
(-0.910377)
Mines
-0.129515
-0.177567
(-0.58713)
(-0.702314)
Manufac.
0.020931
-0.16579
(0.255783)
(-0.967374)
Water
-0.521766 **
-0.292607
(-2.032121)
(-0.502003)
Construct.
-0.337087 **
-0.241795
(-1.98465)
(-0.69636)
Basic serv.
-0.342233 **
-0.508159 *
(-2.413002)
(-1.660662)
Fin. serv
-1.32613
-7.485566 ***
(-1.518299)
(-3.870734)
Pub. adm.
-0.61922 ***
-0.401273
(-2.831179)
(-0.828133)
Teaching
-0.245075
-1.718108 ***
(-1.019649)
(-3.487147)
Health
-0.50816
-0.957237
(-1.376327)
(-1.133507)
*Significant at 1%, ** significant at 5%, *** significant at 10%. t-stat in brackets.
GVA/pop
Total
-0.783155
(-11.72415)
-0.93349
(-2.514647)
-0.307082
(-0.771559)
-0.14486
(-0.753326)
-0.814373
(-1.205938)
-0.578882
(-1.494991)
-0.850392
(-2.383499)
-8.811696
(-4.102852)
-1.020493
(-1.822438)
-1.963183
(-3.738077)
-1.465397
(-1.445231)
***
**
**
***
*
***
Table 5 shows results in line with the previous table. Agriculture has direct negative effect on local
production, but positive indirect effects. This confirms that agricultural activity does not directly
benefit rural areas in which it is concentrated, but a change in canton’s i level of agriculture averaged
over all other cantons is positive. The interpretation is related to the multiplier effect due to the use
of agricultural products by transforming industries, distributors and hotel and restaurants services.
Anyway the global effect is not significant because the two effects tend to cancel out reciprocally. A
similar interpretation can be applied to construction and basic services that have a positive indirect
effect that is not enough strong to widespread over space.
For both public administration and teaching, which have a negative direct and total effect, the spatial
spillovers are positive and significant. Own-canton sectoral magnitude increases will restrain GVA
per head in their own canton, but it has a positive effect in neighbouring cantons. This finding may be
associated with more concentrated development in big cities, reflected in higher GVA per capita, that has
left other cantons less-developed, which in turn has led to increase GVA per capita in those cantons. The
negative indirect effect, for the limited spatial multiplier effect, does not cancel out the negative direct
effect, resulting in a significant negative total effect. Financial services and health show a direct and total
negative effect while indirect effect is not significant. Finally the manufacturing sector still does not
have a benefit to the cantonal production, like mines and water.
These findings warns about the poor results in changing production model in terms of generating
higher subnational value added.
15
Table 5: Estimated effects level regression
Direct
Indirect
-0.379671 ***
0.763705 ***
(-2.899162)
(2.677163)
Mines
-0.323055 *
-0.105577
(-1.755977)
(-0.491694)
Manufac.
0.012132
-0.044125
(0.15821)
(-0.263025)
Water
0.149023
0.062401
(0.665215)
(0.11911)
Construct.
-0.933389 ***
1.083902 ***
(-6.546167)
(3.396886)
Basic serv.
-0.527864 ***
0.684126 **
(-4.217578)
(2.385036)
Fin. serv
-3.361519 ***
-1.707566
(-4.845014)
(-1.076643)
Pub. adm.
-2.113009 ***
1.042635 **
(-11.93012)
(2.553989)
Teaching
-3.384312 ***
2.044559 ***
(-17.69287)
(4.720775)
Health
-2.151766 ***
-0.222133
(-6.845171)
(-0.299453)
*Significant at 1%, ** significant at 5%, *** significant at 10%. t-stat in brackets.
Agricult.
Total
0.384035
(1.149186)
-0.428632
(-1.279971)
-0.031992
(-0.172227)
0.211424
(0.347261)
0.150513
(0.410045)
0.156262
(0.467082)
-5.069085
(-2.91838)
-1.070374
(-2.277204)
-1.339753
(-2.749389)
-2.373898
(-2.648202)
***
**
***
***
The results for the production function estimates are rather close to the estimates for the growth
regression, with the exception of indirect effects. These have negative sign in growth regression and
positive sign in level estimation. The discrepancy in level regression are related to the heterogeneous
economic structure in which some cantons take (indirect) benefit from the weakness of the others.
This determines that few areas (the main urban centres) “absorb” too much economic resources
mainly in terms of skilled workers, productive sectors and technology, making extremely difficult to
create a virtuous multiplier effect for the whole economy. The result of this unbalanced and
heterogeneous production structure is, as shown in section 2, a negative average GVA per capita
growth in which some sectors amplify the territorial imbalances.
5. Conclusions
The paper examines cantonal convergence using a spatial panel econometric approach that allows to
account for issues related to spatial dependence, which is a typical issue when regional or subregional
data are used. In addition, the role of sectoral economic structure in order to produce growth and
spatial spillovers is checked.
The results support the importance of considering spatial relationships in analyzing subnational
development in Ecuador, which appears asymmetrically distributed in space, with the most developed
areas that absorb potentially more productive sectors like manufactury. In period 2007-2013 the
cantonal conditional convergence rate is 9.59% annually. This implies that each canton converges to
its own steady state, which is marked by stark differences that meet specific local production
16
structures. The high convergence rate, furthermore, means a half-life of only 7.22 years, showing that
Ecuatorian cantons are very close to their steady state making crucial to improve efficiently modifing
the productive structure of the economy. Despite the Central Government’s project to change
productive matrix, the weight of non-financial and agricultural sector is still too strong and accounts
for almost 40% of Gross Value Added, reaching, in some provinces, the 41% of production. The
recent construction boom, involving unskilled labour work, may further explain the losing GVA per
capita of the country. In this context, public sector is not able to generate a multiplier effect, both for
their probable lack of efficiency and for the unfavourable context. In this extent the too strong focus
into low productive sectors which depress GVA per capita growth extend their negative effects
throughout the whole country via spatial multiplier. The multiplier, if compared with other contexts
like the European, is very low and the magnitude of indirect effects, as a consequence, is lower than
the direct effects. Considering that the indirect effects correspond to the average contribution of the
neighbour (and neighbour of the neighbour) cantons, this indicates that the spatial spillover effects
are very low, and their effects are strongly clustered in space. This is due to a heterogeneous growth,
which, for the structural limitations due, among other causes, to the heterogeneous production
structure, do not allow exploiting endogenous cantonal potentials and economy of scale.
This has some important policy implications and opens various problems for the objectives of the
PNBV. The low value of spatial multiplier means that a policy intervention in a determined canton
tends to spread out and reinforce its effects generating a spillover effect only in a limited extent,
exhausting after a certain distance. This makes localities and their interaction much more important
for economic growth and prosperity implying that policies tailored on the specificity of each territory
are fundamental (Barca et al., 2012). The priority aims of these policies have to be multiple. The first
one is decentralizing manufactory sector and/or creating incentives related to the creation of collateral
services and productions in order to exploit the maximum from the existing manufactory sectors. This
requires an in-deep analysis of the actual situation with the involvement of institutional actors and
territorial stakeholders. The second point is to reinforce the local networks investing in both ‘harder’
(i.e. routes) and ‘softer’ infrastructure (human capital and research capacity). The third point is to add
an explicit spatial dimension to the policy objectives. In addition to the reduction of existing
disparities, the aim has to be avoiding territorial imbalances making both sectoral policies which have
a spatial impact and subnational policy more coherent through an improved territorial integration and
cooperation.
The Ecuadorian case proves that economic development of lagging development areas is not an
automatic process but involves a wide variety of aspects related to the peculiarities of each territory
that cannot be ignored.
17
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19
Appendix A: robustness check
Table A1: Spatial Durbin estimation results with alternative Spatial Weights Matrices
GVA/pop
Agricult.
Mines
Manufac.
Wather
Construct.
Basic serv.
Fin. serv
Pub. adm.
Teaching
Health
W×GVA/pop
W×Agricult.
W×Mines
W×Manuf.
W×Wather
W×Construct.
W×Basic serv.
W×Fin. serv
W×Pub. adm.
W×Teaching
W×Health
ρ
Growth regression
Queen deg. 2
K=5
-0.464737 ***
-0.46239
(-14.152282)
(-14.370022)
-0.584853 ***
-0.651377
(-3.959386)
(-4.508056)
-0.087951
-0.166631
(-0.410789)
(-0.795009)
0.014609
0.003645
(0.175536)
(0.044094)
-0.497293 **
-0.580454
(-1.983264)
(-2.343626)
-0.305497 *
-0.395657
(-1.781397)
(-2.348089)
-0.333317 **
-0.35631
(-2.376166)
(-2.59047)
-0.501772
-0.678434
(-0.568467)
(-0.779205)
-0.549062 ***
-0.566672
(-2.581979)
(-2.727135)
-0.112852
-0.211785
(-0.46135)
(-0.874261)
-0.388279
-0.54308
(-1.052125)
(-1.498179)
-0.139235 *
-0.08539
(-1.85582)
(-1.34479)
-0.354089
0.048475
(-0.860461)
(0.16447)
0.353218
0.431663
(0.885391)
(0.753383)
-0.225165
0.025805
(-0.998072)
(0.148229)
-1.26266
0.211253
(-1.582343)
(0.376196)
-0.515446
0.063391
(-1.133258)
(0.19328)
-0.492242
-0.29499
(-1.253622)
(-1.007835)
-9.202847 ***
-6.732083
(-4.270335)
(-3.687686)
-0.057171
-0.386153
(-0.098601)
(-0.867066)
-1.661652 ***
-0.699409
(-2.941126)
(-1.566162)
0.09706
-0.880269
(0.100015)
(-1.026984)
0.315965 ***
0.380913
(6.916456)
(10.23494)
Level regression
Queen deg. 2
***
**
**
***
***
***
***
-0.450953
(-3.56179)
-0.262695
(-1.483652)
-0.026979
(-0.34924)
0.151837
(0.71902)
-0.996354
(-6.777835)
-0.589438
(-4.832514)
-3.654764
(-5.393371)
-2.201225
(-12.859507)
-3.591825
(-18.924875)
-2.184121
(-7.291658)
***
0.940496
(2.676025)
0.107074
(0.320602)
0.086026
(0.412261)
-0.864707
(-1.282727)
1.577881
(4.056908)
0.876097
(2.577091)
1.967005
(1.191924)
2.414625
(4.913865)
3.283791
(6.952903)
1.059557
(1.316279)
0.393958
(10.072037)
***
R-squared
0.4456
0.4679
0.9481
corr-squared
0.3287
0.3257
0.4495
sigma^2
0.0181
0.0174
0.0165
N° obs
1326
log-likelihood
877.86
896.01
1072.06
F-test cond. regr.
3.82 (p-val. < 0.01)
3.62 (p-val. < 0.01)
*Significant at 1%, ** significant at 5%, *** significant at 10%. t-stat in brackets.
20
K=5
***
***
***
***
***
***
***
***
***
***
***
***
-0.422615
(-3.320153)
-0.317334
(-1.767734)
0.012859
(0.163173)
0.143614
(0.672185)
-0.996743
(-6.696764)
-0.56558
(-4.605736)
-3.240624
(-4.688952)
-2.170926
(-12.350536)
-3.493777
(-18.240437)
-2.150659
(-7.04409)
***
0.68959
(3.153001)
0.016743
(0.109976)
-0.043021
(-0.333831)
0.006115
(0.0158)
1.091239
(4.49605)
0.670966
(3.113458)
-0.434561
(-0.353163)
1.392347
(4.278844)
2.522441
(7.30082)
0.412515
(0.740366)
0.278986
(9.125238)
***
0.9467
0.4357
0.017
1547
1049.37
*
***
***
***
***
***
***
***
***
***
***
***