A Statistical Analysis of Heterogeneity on Labour Markets and Unemployment Rates in Colombia

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Autor	Camilo Alberto Cárdenas Hurtado, María Alejandra Hernández Montes, Jhon Edwar Torres Gorron
Páginas	153-196

153

DESARRO. SOC. 71, PRIMER SEMESTRE DE 2013, PP. X-XX, ISSN 0120-3584

Revista

Desarrollo y Sociedad

Primer semestre 2015

PP. 153-196, ISSN 0120-3584

A Statistical Analysis of Heterogeneity on Labour

Markets and Unemployment Rates in Colombia

Un análisis estadístico de la heterogeneidad

en los mercados laborales y las tasas de

desempleo en Colombia

Camilo Alberto Cárdenas Hurtado1

María Alejandra Hernández Montes2

Jhon Edwar Torres Gorron3

DOI: 10.13043/DYS.75.4

Abstract

In this paper, we study the structural factors that determine the differences in

unemployment rates and in labour market performance for Colombian cities.

Using cross-sectional data for 23 metropolitan areas, we apply an extension

of a principal axes method—Multiple Factor Analysis for Multiple Contingency

Tables (MFACT)—in order to identify unobservable factors that are relevant

when disentangling the heterogeneity observed among groups of variables

considered explanatory of regional unemployment differentials. Our findings

suggest that differences in qualified labour supply levels, participation incen-

tives and age structure are important when it comes to understanding regional

heterogeneity in terms of labour markets and unemployment rates in Colombia.

1 Banco de la República. Bogotá, Colombia. Corresponding author. ccardehu@banrep.gov.co.

2 Banco de la República. Bogotá, Colombia. mhernamo@banrep.gov.co.

3 Banco de la República. Bogotá, Colombia. jtorrego@banrep.gov.co.

Este artículo fue recibido el 28 de febrero de 2014, revisado el 24 de Junio de 2014 y finalmente

aceptado el 13 de mayo de 2015.

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In addition, clustering methods reveal that cities that display high unem-

ployment rates do not necessarily share the same structural characteristics;

that is, labour market frictions that give rise to unemployment are not the

same across Colombian cities.

Key words: Heterogeneous unemployment rates, regional labour markets, fac-

tor analysis.

JEL classification: R23, J40.

Resumen

En este artículo estudiamos los factores estructurales que determinan las dife-

rencias en las tasas de desempleo y en los mercados de trabajo de las ciuda-

des colombianas. Utilizando información de corte transversal para 23 áreas

metropolitanas, aplicamos una extensión de un método de ejes principales,

análisis factorial múltiple para múltiples tablas de contingencia (MFACT), con

el fin de identificar factores no observables que son relevantes para enten-

der la heterogeneidad observada entre grupos de variables que se consideran

explican las diferencias regionales en las tasas de desempleo. Nuestros resul-

tados sugieren que las diferencias en los niveles de mano de obra calificada,

incentivos a la participación laboral y la estructura etaria son importantes

para entender la heterogeneidad de los mercados de trabajo y de las tasas de

desempleo en Colombia. Además, un ejercicio de clustering revela que las ciu-

dades con altas tasas de desempleo no necesariamente comparten las mismas

características estructurales, esto es, las fricciones en el mercado de trabajo

que dan origen al desempleo no son las mismas en las ciudades colombianas.

Palabras clave: tasas de desempleo heterogéneas, mercados laborales regio-

nales, análisis factorial.

Clasificación JEL: R23, J40.

Introduction

The high levels and persistence of unemployment rates, together with the com-

plex dynamics observed in labour market structures in Colombia, have puzzled

local economists for decades now. Although some issues have been studied

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over the past few years (see, for example, Arango and Hamann, 2013; Urrutia,

2001), there are still several unanswered questions that, if solved, might lead

to a better understanding of the convoluted particularities of labour market

institutions in our country.

One of the most unexplored topics in Colombian labour market literature is

regional unemployment, as stated by Arango (2013). Some pioneer works

explaining regional and urban unemployment in Colombia are those by Jar-

amillo, Romero and Nupia (2000), Galvis (2002), Gamarra (2005) and Barón

(2013). However the topic has not been fully explored. Arango (2013) points

out that there are noticeable differences between Colombian cities when

analysing labour market performance over the past few decades. His findings

show that there is an evident heterogeneity between cities in terms of labour

market indicators such as the unemployment rate, participation rate, occupa-

tion rate, underemployment rates, salaries and education.

He shows that some cities, such as Pereira, Popayán and Quibdó, have per-

sistently displayed high unemployment rates over the past few years, while

others, like Bogotá, Barranquilla, Bucaramanga and Cali, have seemingly per-

formed better over the same time span. There are several feasible explana-

tions for these differences, but still not a single definite one. Very few articles

(if any at all) have explored the driving factors that determine the contrasts

in unemployment rates between regions or cities in Colombia. For example,

to the best knowledge of the authors, only Díaz (2011) has provided valuable

evidence of spatial clustering of different types of municipalities that share

differences in economic and socio-demographic attributes. The author claims

that unemployment and labour market performance in these cities rely heav-

ily on the geographical distribution of those attributes. However, we argue

that factors that determine urban and regional unemployment rate differen-

tials do not necessarily depend on spatial interactions between labour mar-

kets, but are inherent to the labour market structure itself. We expect to find

that the municipalities that share common factors in labour market structures

also display similarities in their unemployment rate levels.

This article explores such differences by analysing the determinants of dif-

ferentials in unemployment rates for a set of Colombian metropolitan areas,

following the framework proposed by Elhorst (2003). We build a high dimensional

dataset for these cities and by studying the relationships between variables and,

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among observations; we aim to find the structural factors that help understand

the regional heterogeneity in labour market indicators described by Arango

(2013). In order to identify such factors, we use exploratory multivariate sta-

tistical analysis techniques. These methods, which are well known for their

suitability for dimensionality reduction, allow for the synthesis of informa-

tion encoded in a high dimensional dataset into a lower dimensional space

of factors that admit graphical representations and an easier interpretation.

The resulting factors will be interpreted as structural variables that explain

regional differences in unemployment rates.

We rely on Multiple Factor Analysis methods (MFA, Escofier and Pagés 2008)

and their extension to tables containing various contingency tables (MFACT),

introduced by Bécue-Bertaut and Pagés (2004, 2008). In contrast to other

factor methods, the main characteristic of this methodology is that it sum-

marizes a dataset composed of both continuous and discrete variables, and

various contingency (frequency) tables, into a new set of factors that can be

projected in a lower dimensional space. Therefore, we can take advantage

of different types of data that might be useful to understand labour market

structures and unemployment in Colombian metropolitan areas. We are also

interested in discovering whether cities can be grouped into different clusters

that share common structural determinants of regional unemployment dif-

ferentials. These clusters are built based on the resulting factors, which imply

that geographical location is not necessarily determinant on their construction.

This article consists of six sections, including this Introduction. In Section I, we

describe the determinants of differentials on regional labour markets proposed

by Elhorst (2003), enriched by a complementary literature review. Section II

describes the statistical methodology used in this paper and the data. Section

III covers the main results of the MFACT exercise. Clustering results are shown

in Section IV. The final section concludes and suggests that in order to reduce

unemployment rates and assure better labour conditions in Colombian cities, it

is important to count for the heterogeneity observed in regional labour markets.

Our results also suggest that unemployment is the result of several different

frictions in labour markets and should not be studied from a single perspective.

I. Explaining Regional Labour Market Differentials

Regional heterogeneity in labour markets and unemployment rates are topics

that have been long addressed from both theoretical and empirical perspectives.

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The academic literature on this subject has benefited from the contributions

made by the so-called new economic geography (NEG) and the equilibrium-

disequilibrium theories. The former suggests that the presence of economies

of scale in a certain location might foster productivity gains, industrial clusters

and urban development, which in turn allow for lower unemployment rates

when comparing these regions to sparse, non-developed peripheral regions.

Recent advances in NEG suggest that the factors that yield agglomeration

and regional productivity differences are also the ones that induce unemploy-

ment disparities (Epifani and Gancia, 2005). On the other hand, equilibrium-

disequilibrium theories argue that unemployment rate differentials will arise

as a result of labour mobility restrictions and the presence of amenities that

might attract labour supply to a certain city or region (Blanchard and Katz,

1992; Marston, 1985). However, we do not aim to discuss theoretical mod-

els concerning regional unemployment differentials; instead, we focus on the

empirical perspective.

According to Elhorst (2003), variables that explain differentials in regional

unemployment fall into one or more of the categories here presented. On

one hand, there are endogenous variables that are related to the city’s popu-

lation and the dynamics of regional labour markets; on the other, there are

exogenous variables that are not directly related to the labour force or the

equilibrium reaching mechanism. We stress that no attempt is made to be

exhaustive in reviewing the existing literature, since it is not the main goal

of this paper. We focus on influential papers on regional unemployment top-

ics that have enriched labour economics literature over the past few decades.

Accordingly, Elhorst states that variables can be categorized into one of the

following groups:

A. Demographic Structure

Variables such as birth rate, age structure and other related demographic indi-

cators have been found to be determinant on the labour supply size in the long

run (Biffl, 1998; Chawla, Betcherman and Banerji, 2007; Lerman and Schmidt,

1999). A region will display persistence in its unemployment rate if its popula-

tion growth is higher than the employment creation rate. In addition, when the

age structure of the population is skewed towards young and old individuals,

the region is more likely to display high unemployment rates (Lottman, 2012).

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B. Participation

Mixed results have been found when assessing the significance of these kinds

of variables in explaining regional unemployment differentials. Authors usually

think of a positive (non-linear) relationship between unemployment and partici-

pation rates. However, it has also been found that higher unemployment rates

are usually accompanied by low participation rates. Several explanations arise:

according to Fleisher and Rhodes (1976), low participation rates might reflect low

levels of human capital investment and low levels of labour commitment. Also,

lower female participation rates are often explained by the presence of children

in the household. The latter implies a trade-off for female workforce between

having a family and pursuing a career (Martínez, 2013). Finally, changes in par-

ticipation rates greater than those in occupation rates might also yield higher

unemployment levels (Blundell and MaCurty, 1999; Da Rocha and Fuster, 2006).

C. Migration

Immigrant flows influence participation rates and reinforce the effects reported

for participation variables. Also, these flows have been found to be correlated

with regional disparities in economic performance and labour market con-

ditions (Blanchard and Katz, 1992; Pissarides and Wadsworth, 1989). How-

ever, the effect depends heavily upon the initial endowments (both human

and physical capitals) of the incoming population: If high, demand for quali-

fied workforce is likely to increase, as are net investment rates and aggregate

productivity (Eggert, Krieger and Meier, 2010; Moretti, 2012). If low, however,

new inhabitants will enter low skilled unemployment lines, as demand for

this type of labour might not increase as fast as supply does (Walden, 2012).

For the Colombian case, Barón (2013) reported that workforce mobility was

limited and did not have a significant effect on labour market indicators, but

responded to economic differences between regions.

D. Commuting

Détang-Dessendre and Gaigné (2009) found that long traveling times and long-

distance commuting have significant effects on unemployment duration and

labour market mismatching. Also, Brueckner, Thisse and Zenou (2002) argued

that firms’ market power when hiring new personnel is higher when work-

ers incur on high commuting costs, measured in both time and money spent.

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E. Wages

Theoretically, higher wages usually have a positive effect on labour supply

and a negative effect on labour demand and, in frictionless models; wages are

the result of the labour market equilibrium reaching mechanism (e.g. Cahuc

and Zylberberg, 2004, Ch. 5-7). Also, frictions related to workforce mobility

between regions or cities yield regional wage differentials (Bande, Fernández

and Montuenga, 2008). Lastly, wages serve as a productivity measure: differ-

entials in wages across regions can occur due to differences in labour produc-

tive skills (Burdett and Mortesen, 1998).

F. Regional Growth

Regions with good economic performance usually display low (structural)

unemployment rates and high productivity indicators. This result can be

encompassed in Okun’s law framework (Okun, 1962), but at regional level, as

in Oberst and Oelgemollër (2013).

G. Market Potential

Location factors matter for labour market dynamics: firms tend to settle in

regions where there is growth potential in terms of sales and stable house-

hold consumption perspectives, among other reasons (Krugman, 1995). As a

consequence, unemployment rates will be lower in those regions. In addition,

some approaches have argued that innovation plays a key role in unemploy-

ment reduction. Innovative sectors attract skilled labour force and have mul-

tiplier effects on employment in other sectors (Moretti, 2010, 2012).

H. Economic Structure

Regions with a diversified productive structure may be less affected by sector-

specific shocks and, therefore, exhibit lower unemployment rates throughout the

business cycle, as argued by Malizia and Ke (1993) and Izraeli and Murphy (2003).

I. Economic and Social Barriers

These are unobservable economic and social variables that discourage work-

force mobility between regions or cities and, therefore, act as frictions in

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regional labour markets (Elhorst, 2003). Frictions in real-estate markets, wel-

fare and social security programs, and general tightness of labour markets are

some of the variables in this group. Lottman (2012) and Walden (2012) pro-

vide some recent empirical evidence on this topic.

J. Education

Higher levels of educational attainment lower the risk of unemployment,

increase the likelihood of higher wages, and promote labour mobility between

regions (Mincer, 1991). Also, it has been empirically tested that high levels of

human capital stocks have spillover effects over the non-educated popula-

tion in labour market outcomes (Winters, 2013). Although the overall qual-

ity of workforce skills cannot be entirely measured by the average number of

years spent in education, it is a sufficient indicator that has been found to

be negatively correlated with the unemployment rate, even at regional level

(Eggert, Krieger and Meier, 2010).

K. Unionisation

From a theoretical perspective, the bargaining power of unions has been treated

as a distortion that deviates the labour market from its competitive equilib-

rium (Cahuc and Zylberberg, 2004, Ch.7). Unionisation has been found to be

correlated with lower labour demand and also to be an influential variable in

the wage setting mechanism, as argued by Mincer (1981), Lewis (1986), and

Farber (1986). More recently, the role of unionisation in the labour market has

been explored by Albagli, Garcia and Restrepo (2004), Freeman (2009), and

Krusell and Rudanko (2013).

L. Regional Natural Unemployment Rate and Persistence

Some authors argue that heterogeneity in regional unemployment arises

due to differences in persistence and natural unemployment rate measure-

ments between regions. This approach has been treated as a purely statis-

tical problem in a wide range of empirical studies, such as Brunello, Lupi

and Ordine (2000), Gomes and da Silva (2009), Lanzafame (2010) and de

Figueiredo (2010).

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II. Methodology and Data

A. The General Factor Analysis Method

Exploratory data analysis (EDA) is the process by which a researcher extracts

vital information from a large dataset, allowing him to understand the under-

lying structure that rules the relationships between observations and variables

as well as determine how ‘related to’ or ‘different from each other’ they are.

Those relationships and contrasts are often thought to be driven by a series

of non-observable variables, known as factors, which are obtained from the

data using statistical methods.

Factor analysis methods are exploratory multivariate statistical techniques that

aim to produce a simplified representation of the variance (inertia) structure

of a high dimensional dataset. Factors themselves form a set of variables that

belong to a lower dimensional space; i.e., factor analyses reduce the dimen-

sionality of the original problem, thus, allowing for more easily formulated

conclusions and interpretations of the observed data. A high inertia structure

yields greater heterogeneity among the individuals within the sample, which,

in turn, is evidenced in the values that the factors attain for each observation.

For example, if observation i scores high on the first factor, and observation

i' scores low, then it means that i and i' are different (heterogeneous) along

the set of observed variables that are summarized by this factor. Therefore,

the analysis is restricted to a one-dimensional problem, rather than over the

whole set of dimensions (variables) that were initially considered.

However, dimensionality reduction comes at a cost. In order to ensure that

the database’s original inertia structure remains as unchanged as possible, we

have to place some restrictions on the exercise of dimensionality reduction.

Factor analysis reduces to a restricted maximization problem: we aim to max-

imize the information contained in the original dataset into a lower dimen-

sional set of variables, guaranteeing that each one of the resulting factors

carry different pieces of information (i.e., are independent from each other).

A mathematical approach is readily presented, but for introductory, yet com-

prehensive, references on this topic see Escofier and Pagés (2008), Johnson

and Wichern (2007), and Peña (2002).

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Following, we explain how these factors are obtained. Let X be a I (individuals)

x K (variables) matrix. Since XK

, we can define a metric on X in order to

measure the distance between any two points x x i j I

i j

and , , Î. The weights

used when computing these distances, labelledmkfor each variable4, are defined

by the K x K matrix M. Usually M is defined as an identity matrix of size K.

In fact, when M is diagonal (i.e. m M

kÎdiag( )), the distance between points

i and j is computed as d i j x x m

k K i k j k k

, ,

( , ) = ( )

∈

∑

− ⋅ . Since mk weights the

influence of each variable

k KÎ

in the computation of this distance, M is usu-

ally understood as the “columns’ weights” matrix.

The shape of the individuals’ cloud in

Kis completely defined by X and M.

However, when calculating the inertia structure (variance) of X, the weight

associated to an individual i, pi, enters into the computation. These weights

are ordered in a diagonal matrix D of rank I , i.e. p diag D

iÎ( ). The more het-

erogeneous the individuals, the richer the inertia structure of X.

Now let F XMu

uhh

= be the projection of X over a single vector uh in

K. The

variance of X projected over uh is i I i uhuhuhh h

p F i F DF u MX DXMu

∈

∑

′ ′

[ ( )] = = '

Factor analysis methods aim to find a new set of orthonormal vectors u h H

h, Î5,

such that the inertia projected over each one of them is maximized. The set

of unitary vectors uh that satisfy

u XMu u MX DXMu u u u

uhKh h h h h h

arg Inertia , s.t.

max ' '{ ( ) := ' } =|| |||=1; (1)

are the eigenvectors of the diagonalizable matrix X'DXM ordered according

to their associated eigenvalues ranging from the highest (in absolute value),

l1, to the lowest, lK. Note that, by construction, the inertia projected over

uh will be lhh H, ∀ ∈ , i.e., Inertia( ) =XM k K k

∈

∑

Principal Component Analysis (PCA), Correspondence Analysis (CA) and Mul-

tiple Correspondence Analysis (MCA) are specific cases of this general factor

method, and each one has its own specification for matrices X, D and M. For a

detailed presentation of each method, see Escofier and Pagès (2008, Ch. 1-4)

and Greenacre (2007).

4 The subindex k denotes the kth element of diag(M).

5 Again, subindex h denotes the hth vector belonging to a set of cardinality H  K.

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B. Dealing With Mixed Datasets

Usually observations are simultaneously described by joint juxtaposed sets of

quantitative (numerical) or qualitative (categorical) variables, and contingency

tables, as shown in Figure 1. In this I x K matrix, we have J groups of variables:

Jq quantitative groups, Jc categorical groups and Jf frequency tables. Each group

has Kj variables, which means that j J j

K K

∈

∑

= . For notational purposes, xikj

corresponds to a numerical realization and zikj is a dichotomous variable that

assigns 1 if XI belongs to category k in Kj or 0 if not. fikj is the ratio of the

number of occurrences of xi for variable k K j

Î to the total number of reali-

zations on the contingency table, i.e., fikj = x x

ikj i k ikj

å å

Figure 1. Global Table

J groups of variables

Jq Sets of

quantitative variables

Jc Sets of

categorical variables Jf Frequency tables

1 ... k ... Kj1... k ... kj1 … k … KjK

J1 JqJcJf

. . . . . . . . .

xikj zikj fikj

Source: Author's adaptation from Bécue-Bertaut and Pagès (2008).

Multiple Factor Analysis for Contingency Tables (MFACT) was developed by

Bécue-Bertaut and Pagès (2008, 2004) to deal with mixed datasets (Figure

1). In MFACT, the distance between individuals is determined by the informa-

tion available on the numerical and categorical variables, and the contingency

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tables. This represents an advantage in comparison to the separate analysis

approach using PCA, MCA, and CA, respectively. In MFACT, the weight of each

variable k belonging to a group j, mk

j, is standardized by the first eigenvalue

computed in each individual analysis on j, l1

j, i.e. new columns weights are

m k K j J

j j

/ , ,

l∀ ∈ ∀ ∈ . Readers are referred to Escofier and Pagès (1994,

2008) and Pagès (2002, 2004) for an explanation of MFA methods.

In sum, MFACT can be understood as a general factor method applied to a

global table X subject to some previous transformations (which depend on the

nature of the variables), with a specific metric M and the rows weights D . Matri-

ces are specified in Table 1. Supplementary projections and graphical repre-

sentations are also supported.

Table 1. MFACT Matrices

Quantitative

Variables Categorical Variables Frequency Tables

Xx x

ikj kj

-z p z

p z

ikj i I i ikj

i I i ikj

− ⋅

⋅

∈

∑

( ) ff

f f

ikj

i j

i kj

−











⋅

⋅

⋅⋅

⋅

⋅⋅ ⋅

i I i ikj

k K ji I i ikj

p z

∈

∈ ∈

∑

∑ ∑

⋅

⋅l1

fkj

Dpi

Source: Author’s adaptation from Bécue-Bertaut and Pagès (2008).

Dealing with a mixture of quantitative, categorical and frequency tables in the

global analysis brings forth a number of issues when deciding which weights

are assigned to individuals. On PCA and MCA (quantitative and categorical

tables) individual weights are set according to the user’s preferences and are

usually fixed to be uniform across all rows ( p I

i=1/ ). However, on a multi-

ple contingency table, individual weights are determined by the row margins

(p f

i i

=×× , where f f

ik K j J ikj⋅⋅ ∈ ∈

∑ ∑

=). MFACT can operate under any specifica-

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tion of matrix D. We set D as in Bécue-Bertaut and Pagès (2008) ( . . ),i e p f

i i

= ××

to favour cities with greater populations and to avoid distorted results influ-

enced by uniform individual weights.

C. Data

We build a large dataset consisting of 182 variables measured for 23 Colom-

bian urban areas6, which are further categorized into 23 groups.

The 182 variables are classified into groups that belong to two broad categories:

quantitative variables (119) and contingency (frequency) tables (63). Quantita-

tive groups are: demographic variables (Demo_c), participation (Part_c), inter-

regional migration (Mig_c), commuting (Mob_c), market structure (Mktst_c),

regional growth (Regw_c), market potential (Mktp_c), educational attainment

(Edu_c), wages (Wag_c), unionisation (Unio_c) and economic and social bar-

riers (Esbr_c). In addition, 12 contingency tables that count for 63 variables

are constructed: age structure (Demo_f1), age structure for men (Demo_f2),

age structure for women (Demo_f3), marital status for men (Part_f1), marital

status for women (Part_f2), waged employment structure (Mktst_f1), employ-

ment structure by occupational position (Mktst_f2), employment structure

by economic sector (Mktst_f3), educational attainment structure for unem-

ployed population (Edu_f1), educational attainment structure for employed

population (Edu_f2), educational attainment structure for working age popu-

lation (Edu_f3) and educational attainment structure for inactive population

(Edu_f4). The dataset was constructed with information obtained from the

National Statistics Administrative Department (DANE), Ministry of Education

(MEN), Ministry of Finance (MHCP), Department for Social Prosperity (DPS),

Economic Commission for Latin America and the Caribbean (ECLAC), Observa-

tory for the Colombian Caribbean (Ocaribe) and the Central Bank of Colombia

(Banco de la República). A description of the dataset is presented in Appendix

1 and is available upon request. Due to the lack of information availability in

6 8 metropolitan areas (Bogotá, Medellín, Cali, Barranquilla, Bucaramanga, Cúcuta, Pereira and Mani-

zales) that sum a total of 52 municipalities, and 15 capital cities (Pasto, Ibagué, Montería, Cartagena,

Villavicencio, Tunja, Florencia, Popayán, Valledupar, Quibdó, Neiva, Riohacha, Santa Marta, Armenia

and Sincelejo) where representative samples were obtained.

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eight variables for two cities7, we used the method presented in Husson and

Josse (2013) to handle missing data in our sample.

Given that according to Elhorst (2003), these variables are the structural

determinants of regional unemployment differentials, we expected the results

not to depend heavily on the year for which this exercise was computed. We

therefore chose 2010 for the analysis.

III. Results

Before presenting any results, we recall the fact that no assumption is made

on the multivariate distribution of the data. This means that no probabilistic

results shall arise from an MFACT exercise and, therefore, we will not make

any kind of statistical inference from the dataset. Computations were made

using the statistical software R (R Core Team, 2013), and the FactoMineR pack-

age (Husson, Josse and Lê, 2008). We also point out that we project variables

belonging to groups Demo_f1, Mktst_f1 and Edu_f3 as supplementary, so these

groups do not add any extra information to the principal axes computations8.

The results for each separate analysis reveal a rich variance structure for

each group of variables, providing strong evidence for a MFACT approach.

The number of factors that summarize the total inertia of the original data-

set ( j J j

∈

∑

l= 26,6 ) are the projections of matrix XM over the eigenvectors

whose eigenvalues are greater than unity. 76.1% of this is summarized in the

first five principal axes (Figure 2).

A. Interpreting Principal Axes

We name the resulting factors after the groups of variables that contrib-

ute the most to the inertia projected onto each dimension, as highlighted

in Table 2. High correlations are also observed for the contributing groups.

7 Herfindahl and Hirschman’s index for exports diversity, weighted distance to closest markets, Herfindahl

and Hirschman’s index for market diversity, firm’s efficiency index, industrial density, store construction

costs, registration costs and sale taxes for Quibdó and Florencia.

8 By construction Edu_f1 + Edu_f2 + Edu_f4 = Edu_f3, Mktst_f2 + Mktst_f3 = Mktst_f1 and Demo_f2

+ Demo_f3 = Demo_f1

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Figure 2. Eigenvalues: Scree Plot for Global Factor Analysis

Plot of eigenvalues

Value

i-th eigenvalue

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Source: Author’s own calculations.

The first principal axis ranks cities according to their population’s educational

attainment, workforce productivity and their occupational positions. This axis

explains 32.8% of the total variance ( 1= 8,7 ) and is associated with vari-

ables such as number of waged workers, people with 13 or more years of for-

mal education or nominal and real wages. We label this dimension an “index

for quality of labour supply”.

The second factor has high loadings on participation variables and educa-

tional attainment of the unemployed and inactive population. This dimension

counts for almost 16.0% of the total variance ( l2= 4,2 ). Cities that display

negative values in this factor are those with high remittances per capita, a

demographic structure biased towards the older population and high unem-

ployment rates for low skilled workers. In contrast, cities that display positive

values are those that exhibit higher unemployment rates in the skilled popu-

lation and show low participation in the labour market. For these reasons, the

second axis has been labelled as the dimension for “participation and skilled

job demand frictions”.

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Table 2. Inertia Contribution and Correlations of Each Group of Variables

Contribution (%) Correlation

Groups Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.1 Dim.2 Dim.3 Dim.4 Dim.5

Demo_c 3.13 1.69 2.80 15.22 3.26 0.61 0.38 0.34 0.57 0.24

Part_c 0.15 13.88 5.16 0.26 0.10 0.12 0.77 0.42 0.28 0.06

Mig_c 2.16 0.04 13.85 0.99 11.79 0.44 0.08 0.69 0.43 0.44

Mob_c 5.95 2.31 1.32 0.08 0.62 0.72 0.31 0.21 0.04 0.10

Mktst_c 6.18 1.13 6.89 3.66 4.39 0.74 0.34 0.51 0.44 0.63

Regw_c 2.39 0.72 3.00 6.08 11.46 0.53 0.35 0.33 0.41 0.45

Mktp_c 6.68 0.60 3.00 4.75 5.14 0.76 0.16 0.32 0.32 0.29

Edu_c 5.48 0.83 17.63 18.04 2.70 0.89 0.26 0.79 0.70 0.42

Wag_c 7.54 2.71 0.12 3.91 5.62 0.81 0.37 0.17 0.29 0.31

Unio_c 1.16 0.60 4.31 15.00 0.33 0.32 0.16 0.38 0.57 0.07

Esbr_c 6.22 2.67 8.82 4.04 7.90 0.77 0.34 0.65 0.41 0.36

Demo_f2 7.36 9.23 1.11 0.75 0.66 0.82 0.68 0.33 0.23 0.12

Demo_f3 6.69 9.71 0.70 0.34 0.47 0.78 0.67 0.22 0.18 0.12

Part_f1 5.90 4.98 8.65 0.24 7.06 0.73 0.52 0.62 0.14 0.39

Part_f2 6.29 5.53 6.80 0.33 6.13 0.75 0.59 0.50 0.22 0.39

Mkts_f2 7.97 1.72 3.60 3.61 7.99 0.84 0.44 0.43 0.47 0.39

Mkts_f3 6.40 3.26 1.71 7.64 11.69 0.81 0.55 0.28 0.54 0.53

Edu_f1 1.57 17.59 3.25 6.07 2.95 0.43 0.87 0.49 0.58 0.34

Edu_f2 8.63 6.03 4.56 4.09 1.01 0.88 0.57 0.68 0.57 0.23

Edu_f4 2.14 14.74 2.71 4.88 8.75 0.49 0.80 0.68 0.56 0.40

Supplementary groups

Demo_f1 6.96 9.60 0.87 0.47 0.54 - - - - -

Mkts_f1 7.82 0.29 1.39 1.77 5.88 - - - - -

Edu_f3 6.24 8.95 3.78 4.51 1.27 - - - - -

Source: Author’s own calculations.

The third axis, which explains 12.9% of the total variance ( 3= 3, 4 ), is

related to education, migration and economic and social barriers groups.

In this dimension, cities are projected according to their middle and higher

public education coverage, net migration rates between and within (from

rural to urban spaces) regions, and (negatively) royalties per capita. This

dimension summarizes the differences between cities on a basis of oppor-

tunity. We label this axis as a “public educ ation efficie ncy and migration

vulnerabilit y index”.

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The fourth axis, that accounts for 8.1% of the total inertia ( l4=2, 2 ), has

high loadings on basic public education coverage, demographic (race) and

unionization variables. Positive values in this dimension suggest high pro-

portions of Afro-descendant populations and a low proportion of unionised

workers. We think of this axis as a “non-wage ri gidities dimensio n” in the

labour market. The fifth axis explains 6.3% of the total variance ( ),l5=1, 7

and it has high loadings on migration, regional growth groups and labour

market structure. This axis is interpreted as the “economic diversity axis and

labour absorption capacity”.

B. Interpreting Cities’ (Individuals) Projections

The heterogeneity of a cloud of I individuals in

Kis best understood by ana-

lysing the inertia of separate clouds in

2. Cities’ projections are the ordered

pairs ( ( ), ( )), =1, ...,23

F i F i i

uhuh+for and

h H=1,...,

1. First Principal Plane (First and Second Dimensions - Figure 3A)

Cities projected along the first dimension are ordered according to their work-

force skills, wages and occupational positions. On the positive side of the axis,

we identify cities with high levels of qualified labour supply, better salaries

and more stable, productive and promising job positions, as in Bogotá, Medel-

lín and Bucaramanga. In contrast, cities with low human capital stocks, low

wages and poor educational conditions, such as Quibdó, Florencia, and Rioh-

acha, are projected onto the left side of the axis.

Pereira, Cali and Manizales are projected onto the negative side of the second

dimension, opposed to cities like Tunja, Cartagena and Quibdó. That is, cit-

ies where low (high) unemployment rates prevail for the educated population

are located on the negative (positive) side of the axis. Also, Pereira, Cali and

Armenia are among the cities that receive greater inflows of remittances, in

contrast to Tunja, Riohacha and Quibó. Remittances are thought to discour-

age participation in urban labour markets.

Projections for the first principal plane give an initial insight into the struc-

ture of labour markets in Colombia (Figure 3). The distance from each projec-

tion to the origin measures likeliness to the average city on that dimension.

We explain the results for Tunja, Quibdó and Pereira.

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Tunja, which is projected onto the first quadrant, shows a highly qualified

workforce, along with Bogotá, Medellín and Bucaramanga; but it also displays

high unemployment rates for the skilled workforce, as in Quibdó, Valledupar

and Riohacha. Several arguments lead us to believe that supply characteristics

and demand needs on this labour market do not match. On one hand, the local

economy is biased towards agricultural activities, as suggested by the informa-

tion on departmental GDP provided by DANE; while, on the other, the economic

activity might not have evolved as rapidly as the educational attainment, acting

as a barrier to the creation of proper job positions for educated people. Quibdó,

for instance, is a city where economic opportunities are scarce. Poor economic

performance and few job positions for skilled people are, among others, factors

that determine the lack of willingness of its population to commit themselves to

build up better human capital stocks. Results for Pereira and some other cities

located in the coffee-growing region suggest that these cities are characterized

by an aging population, high remittance dependence, and low skilled workforce.

The coexistence of these factors represents difficulties for the accumulation of

human capital, since the population pyramid is already old and the incentives to

enrol in training programs are not sufficient for the working age population. As

a result, these cities have experienced poor economic growth over the past few

years, especially in those sectors that are intensive in low skilled labour force

(i.e. construction, retail, among others).

2. Second Principal Plane (Third and Fourth Dimensions - Figure 3b)

Along the third dimension, striking differences arise between cities like Quibdó,

Popayán or Florencia versus others such as Cali or Bogotá. The incentives for

migration are seemingly higher in the former group of cities: In addition to

violence and other political issues, low wages, poor education quality, higher

shares of single youngsters and poor economic conditions for low skilled work-

ers and young populations are, among others, the main reasons that encour-

age migration in these cities.

Projections along the fourth dimension distinguish cities with a high percent-

age of unionised workers, such as Popayán, Florencia or Neiva, from those with

a relatively low share, such as Barranquilla or Cartagena. This finding suggests

that negative valued cities in this axis face rigidities originated in the labour

supply side of market power. Also, this axis classifies cities depending on the

average size of households and other participation variables: most of those

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located in the Caribbean region are characterized by larger families and very

low female participation rates. Finally, there are some demographic charac-

teristics that also contribute to the computation of this axis. Cities such as in

Barranquilla, Quibdó and Cartagena in which the Afro-descendant proportion

of population is higher are projected onto the positive side.

In sum, these results provide solid evidence of heterogeneity in the determi-

nants of regional unemployment differentials, as suggested by Arango (2013).

However, one of the most interesting findings in this paper is that cities with

high unemployment levels do not necessarily share the same underlining struc-

ture on an economic, demographic, educational or even cultural basis. It is

clear that there are great disparities between regions in terms of unemploy-

ment rates, but not all are due to the same reasons.

IV. Clustering

Clustering and multivariate data analysis techniques are complementary meth-

ods (Lebart, 1994, p.162), since studying the similarities between individuals

in a lower dimensional space leads to a better understanding of the structure

of the data. We group cities that share the same characteristics along the five

principal axes we found in the MFACT step. Following Husson, Josse and Pages

(2010), we combine MFACT results and both hierarchical (Ward’s criterion) and

partitional (k-means algorithm) clustering methods.

To interpret each partition, we measure the association between the cluster

(understood as a categorical variable) and each (group of) quantitative vari-

able and each frequency (contingency) table, and check its significance as

in Bécue-Bertaut and Pagès (2008, pages 3261-3262). The resulting clusters

suggest that differentials in unemployment rates are associated with differ-

ent factors across Colombian cities.

A. First Cluster: Quibdó, Florencia, Riohacha and Valledupar

Cities belonging to this cluster are Quibdó, Florencia, Valledupar and Rioh-

acha. Although their individual unemployment rates were not the highest

among the sample and are not significantly different from those observed for

the urban areas (12.4% on average for 2010), there are notorious differences

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in other variables that determine quality of life and human capital formation.

MFACT results suggest that this cluster groups cities that are, on a statistical

basis, different from the others because of their outstandingly low educational

attainment and their poor economic and social perspectives that influence on

participation and human capital accumulation decisions.

Cities in this cluster are statistically different on a demographic basis: over

50% of the population are young (0 to 25 years old), and the Afro-descendant

and indigenous populations are the most representative ethnic groups. Younger

people are more likely to be unemployed, mostly due to lack of expertise and

education (Furnham, 1985). Also, there is empirical evidence of race discrim-

ination in employment and wage setting (Darity and Mason, 1998), mean-

ing higher unemployment rates for Afro-descendants and indigenous people.

Results show that these cities are net migrant recipients, as these are capital

cities of departments where conditions are not favourable for the rural popula-

tion, mostly due to security problems, lack of rural development and poor health

and education coverage. Average Educational attainment is very low for these

cities: illiteracy rates are the highest in the sample and the occupied workforce

has the lowest levels of years of schooling, as projections over the first principal

axis confirm. In addition, access to communication services and technology is

scarce, as revealed by Internet service coverage and computer usage indicators.

The economic activities that play an important role in local GDP are less pro-

ductive and add less value as compared to other departments. Mining activi-

ties, for example, counted for about a third of their GDP (30%) on average

over the past few years, almost ten times higher than the total national share

over the 2000-2010 period (3.4%). Shares of industry, commerce and finance

are significantly below the national average (3.2% vs. 14.0%; 8.7% vs. 12.4%;

and 6.0% vs. 20.7%, respectively). Finally, average wages are about 20% lower

than those paid in other cities of our sample.

In sum, this cluster is made up of cities where inhabitants have low educa-

tional and productive skills, while economic structure is biased towards activi-

ties that are not workforce intensive and that are not chained to other sectors

that add more aggregated value to the economy. We recall that poverty and

inequality have deeper roots in political and economic issues that are not

entirely related to poor labour conditions.

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Figure 3. Projections over the First and Second Principal Planes

Dim 2 (15.96%)

Dim 1 (32.80%)

-2

-4

-6

-10 -5 0

Quibdó Riohacha

Valledupar Santa Marta Cartagena

Tunja

Sincelejo Montería Neiva

Villavicencio

Florencia Pasto Ibagué

Armenia

Cali_AM

Manizales_AM

Medellín_AM

Bucaramanga_AM

Popayán

Barranquilla_AM

Bogotá_AM

Cúcuta_AM

Pereira_AM

a) Individuals Representation: First and Second Axis

Dim 3 (12.90%)

-2

-4

Dim 4 (8.11%)

-4 -2 0 2 4 6

Cartagena

Barranquilla_AM

Quibdó

Santa Marta

Cali_AM Riohacha

Pereira_AM

Armenia

Bogotá_AM

Villavicencio

Ibagué Bucaramanga_AM

Valledupar

Cúcuta_AM Manizales_AM

Tunja Neiva

Pasto Popayán

Florencia

Montería Medellín_AM

Sincelejo

b) Individuals Representation: Third and Fourth Axis

Source: Author’s own calculations.

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B. Second Cluster: Popayán, Pasto, Montería, Neiva,

Villavicencio and Sincelejo

Cities belonging to this cluster are Popayán, Pasto, Montería, Neiva, Villavi-

cencio and Sincelejo. Unemployment rates for these cities are somewhat het-

erogeneous, but they do still share underlining characteristics in their labour

market structure. For example, the average ratio of non-salaried workers to

total workforce in these cities is above the total national ratio (68.1% vs.

53.9%), as well the average share of self-employed working population (51.7%

vs. 46.8%). This feature is a relevant characteristic of this cluster: results show

that cities in this group exhibit high levels of self-employment and low work-

force enrolment in formal firms.

In addition, there is a larger proportion of unionised workers (6.3% vs. 3.4%

national), which can be considered as friction for the equilibrium reaching

mechanism in these labour markets. It has been shown that unionised manu-

facturing firms tend to expand at a lower speed than the non-unionised ones

(Hirsch, 1997; Long, 1993), which might contribute to overall lower economic

performance and a lower labour demand expansion over time. We point out

that these levels are low in comparison to other countries in Latin America

and around the world (Blanchflower, 2006; Visser, 2006).

Another characteristic that they share is that the tertiary sector (i.e. retail,

transport and services) has gained importance in these economies over the past

few years: the average growth rate for the last decade is 6.6%, greater than

the average for the national case (4.3%). This performance has been achieved

in great part due to the dynamics of the financial sector.

Other results suggest that poor labour conditions might exist in these cit-

ies. Both average nominal and real incomes are below the national average,

even if that difference is not statistically significant. In addition, although not

included in the principal axes computations, it is also important to report that

underemployment rates (both subjective and objective) are above average for

urban areas: 33.9% and 15.5% vs. 30.1% and 12.9%, respectively. This leads

us to think that cities belonging to this cluster are characterized by dysfunc-

tional formal labour markets where prevailing working conditions encour-

age self-employment and informality, but are not necessarily reflected in the

unemployment rate itself.

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C. Third Cluster: Barranquilla, Santa Marta and Cartagena

This group is constituted by Barranquilla, Santa Marta and Cartagena and is

the cluster with the lowest unemployment rates in the sample. It is also com-

paratively low in terms of occupation and participation rates (52.3%, 57.8%

vs. 57.2%, 65.5%, respectively).

Global participation rates for under 25s and for women are outstandingly

below the corresponding national averages. Among other reasons, this could

be related to the average household size, which is the highest among the clus-

ters in our sample (4.1 persons vs. 3.7 for the national average). According to

the data, women continue to take over parenting responsibilities and domes-

tic tasks at home, supporting the evidence of lower female participation rates.

Regarding education variables, this group displays the largest number of non-

public institutions per 100,000 inhabitants (43 vs. 30 for the national average).

According to Viloria (2006), education coverage has increased over the past few

years in the Caribbean region but results suggest that coverage efforts have

not been accompanied by quality improvements. In fact, the average number

of years of schooling for the inactive and unemployed populations is higher

than the national averages, supporting the idea of mismatching between sup-

ply characteristics and demand needs in these labour markets.

To confirm the latter, data on educational attainment for the unemployed

population is the highest among the clusters (11.1 years spent in education

vs. 10.1 for the national average). In addition, the number of qualified unem-

ployed people (with college or graduate education) is much higher than the

national average, suggesting that skilled employment absorption in this cluster

is insufficient and much lower than for other cities in our sample.

Summing up, unemployment and participation rates in this cluster are, on

average, the lowest in our sample. Also, young people and women participate

less in the labour market than in other cities. This is consistent with the high

enrolment rates in educational and parenting and housekeeping activities.

However, it is important to pay special attention to the quality of both higher

education and job positions, since low unemployment rates may be due to

the lack of dynamic and inclusive institutions in labour markets, in addition

to the fact that overall participation rates are already low.

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D. Fourth Cluster: Pereira, Armenia, Manizales,

Ibagué, Cúcuta and Cali

The cities belonging to this cluster are Pereira, Armenia, Manizales, Ibagué,

Cúcuta and Cali, and are mainly characterized by their demographic compo-

sition. Such characteristics tend towards an older population and the popula-

tion’s educational attainment, which is biased towards the population having

few years of schooling (less than 10).

These cities exhibit lower gross birth rates than the average for urban areas (15

vs. 22), which suggests that the population pyramid tends to reverse faster in

this group. This feature will lead to a greater proportion of dependent popu-

lation and lower levels of education in the long run, since educational levels

are already low in these cities and, given the progressive ageing of the popu-

lation, incentives for human capital training are decreasing.

On the other hand, remittances per capita, which are three times the national

average, and high hidden unemployment rates suggest a possible discourage-

ment phenomenon that lowers people’s incentives to participate in the labour

market. Data supports this hypothesis: participation rates for adult males and

for over 45s are below the national average. In fact, the unemployment rate

for the latter population segment is the highest in the sample (11% compared

to the national average of 8%).

Another remarkable fact is that living costs (both in levels and annual variations)

are, on average, lower than the rest of the economy for the 2008-2010 period, as

deduced from both the food CPI inflation (1.1% vs. 1.9% national average) and

total CPI inflation (2.3% vs. 2.6%). Cheap living costs lower incentives for people

to improve their income levels and to participate in the labour market by increas-

ing the average reservation wage (Arango, Montenegro and Obando, 2013).

Migration variables play an important role in this cluster as well. The average

net migration rate is negative, revealing qualified workforce migration to other

places with better economic and labour conditions. This “brain drain” leads

to, for example, low economic development, low productivity and low wages,

which cause second round effects on labour market performance (Eggert et

al., 2010). In fact, the average share of qualified working age population in

these cities is the lowest among the clusters.

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Figure 4. Cluster Projections Over the First and Second Principal Planes

Dim1 (32.80%)

Factor map

Dim2 (15.96%)

-2

-4

-6

-8

-15 -10 -5 0

Quibdó Riohacha

Valledupar

Florencia

cluster 1

Sincelejo Montería

Villavicencio

Neiva

Pasto

Popayán

cluster 2

Santa Marta Cartagena

Barranquilla_AM

cluster 3

Ibagué

Cúcuta_AM

Cali_AM

Armenia

Manizales_AM

cluster 4

Pereira_AM

cluster 5

Tunja

Bogotá_AM

Bucaramanga_AM

cluster 6

Medellín_AM

a) City Clusters Representation: First Principal Plane

Factor map

-2

-4

-4 -2 0 2 4 6

Dim3 (12.90%)

Dim4 (8.11%)

Quibdó

cluster 1

Valledupar

Riohacha

Florencia

cluster 2

Montería

Villavicencio

Neiva

Pasto Popayán

Sincelejo

Cartagena

Barranquilla_AM

Santa Marta

cluster 3

cluster 4

Cali_AM

Pereira_AM

Cúcuta_AM

Ibagué

Manizales_AM

Armenia

cluster 5

Bogotá_AM

Bucaramanga_AM

Tunja

cluster 6

Medellín_AM

b) City Clusters Representation: Second Principal Plane

Source: Author’s own calculations.

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On the labour demand side, we find that this cluster has experienced the low-

est economic growth in the 2000-2010 period (3.1% on average vs. 4.1% for

the Colombian economy). Weak economic performance is generalized for all

sectors, but it is most worrying in the secondary (industry and construction)

and tertiary (commerce and services) sectors, which are labour intensive.

Interestingly, this cluster groups together cities that exhibit the highest

unemployment rates in the sample (Pereira, 20.5%; Ibagué, 17.6%; Manizales,

17.6%; and Armenia 16.3%), along with Cúcuta (14.0%) and Cali (13.9%).

Our hypothesis is that high unemployment rates in these cities arise due to

the coexistence of high non-skilled labour supply levels, low incentives for

participation, older population predominance, rigidities in terms of human

capital accumulation and an economic structure that is not inclusive for

non-qualified available workforce. Results show that the mismatch between

supply characteristics and demand needs is definitely a major issue in labour

markets in these cities, which can, in turn, determine long run structural

unemployment (Yarce, 2000).

E. Fifth Cluster: Bogotá, Tunja and Bucaramanga

Bogotá, Bucaramanga and Tunja belong to this group, which is characterized

by both higher educational levels and higher wages. The average school years

of the working age population stands out as an important characteristic for

this cluster. On average, 31% of the working age population is qualified, in

contrast to the national average of 22%.

Cities in this group also display the highest average GDP per capita, and

both real and nominal incomes, which are about 30% higher than the rest

of the cities in the sample, only surpassed by Medellín and its metropoli-

tan area (Cluster 6).

In this cluster, the percentage of workers employed in the financial interme-

diation sector is higher than the national average (2.1% vs. 1.3%), as well as

those employed in real estate activities (9.1% vs. 6.3%). These shares reflect

the degree of specialization of these economies in service provision activities.

It also highlights the industry participation and its good performance during

the 2000-2010 decade (5.1% on average vs. 3.5% national average).

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The average global participation rate in this group of cities is also higher than

the national average (67.4% vs. 65.5%), mainly because of increased female

participation in comparison to the rest of cities (61.3% vs. 54%). We point out

that female unemployment rates are the lowest among all the other clusters

(13% on average), and that total unemployment rates are among the lowest

in the country.

In sum, this cluster has very high levels of skilled workforce supply and a higher

demand for this kind of labour than that observed for the rest of the cities.

Results show that mismatching is low in these cities9, since labour supply

responds to the demand for a skilled and productive workforce. This scenario

has recently driven good labour market performance, which in turn allows the

average unemployment rate for this group to be lower than that reported for

urban areas (11.4% vs. 12.4%).

F. Sixth Cluster: Medellín

Medellín and its metropolitan area form a cluster by themselves, mainly char-

acterized by market potential variables associated to population density, very

high industrial density and the lowest average distance to major markets. Such

factors would, in principle, yield lower levels of unemployment (because of

the matching and higher labour demand, as explained in section 2). Also, the

average household income (both nominal and real) for this cluster is above

the national average by about 30%.

However, this city does not exhibit an unemployment rate below the national

average (13.9% versus 12.4%). Despite the fact that the industrial sector

absorbs a greater percentage of the working population than the national

average (21.2% vs. 12.2%) and even though services oriented sectors count

for almost 50% of the economic activity, mismatching exists in this city as

well. It is noteworthy that although educational attainment levels are above

the national average, demand for a qualified workforce seems to be just par-

tially fulfilled: the skilled unemployed population share is just 28%, lower than

30% for our sample, and below 40% in cluster 5. Our hypothesis is that the

demand is presumably requiring more skilled workforce than labour supply

9 Except for Tunja, where the unemployment rate is somewhat higher than the urban areas average

(12.9% vs. 12.4%), despite of the very high levels of skilled workforce in this city.

A Statistical Analysis of Heterogeneity on Labour Markets

180

DESARRO. SOC. NO. 75, BOGOTÁ, PRIMER SEMESTRE DE 2015, PP. 153-196, ISSN 0120-3584

can provide in Medellín. This may be due to the low incentives that many

young people have to invest in human capital, given the violent environment

in which they live, as argued by Medina, Posso and Tamayo (2011).

V. Conclusions

The heterogeneity found in urban labour market indicators in Colombia has

not been widely studied. This paper aims to explore the relations between

variables that have been theoretically and empirically assessed to deter-

mine the differentials in regional unemployment. Following Elhorst (2003),

we studied a large dataset in order to establish similarities and differences

between Colombian cities based on principal axes methods (MFACT, Bécue-

Bertaut and Pagès 2004, 2008), clustering techniques and statistical cri-

teria (Husson et al., 2010). Our results suggest that there is evidence of

disparities in structural variables that define the performance of regional

labour markets. Particularly, our most relevant result is that cities that dis-

play high unemployment rates do not necessarily share the same charac-

teristics; that is, frictions that give rise to unemployment are not the same

across Colombian cities.

Clustering results give an important insight into the Colombian labour mar-

ket structure. For example, we find that high unemployment rates in cluster

4 obey primarily to the mismatch between labour supply and demand result-

ing from the lack of educated workforce and the need for qualified workers,

and also from low participation incentives due to high levels of per capita

remittances; while unemployment problems in cluster 2 originate in the high

levels of self-employment and the risks associated to this type of work. As

suggested in the influential work of Overman, Puga and Hylke (2002), bear-

ing in mind that not all cities or regions share the same structural problems

and that they do not react to the same national-based labour institutions

allows for policy makers to propose and execute better local policies focused

on unemployment and inequality reduction. Therefore, this type of analysis

matters and provides arguments for a better national and local government

policy formulation. It is worth noting that, in many cases, clusters are made

up of cities located near each other, suggesting that regional effects are also

influenced by geographical positions, as mentioned in Overman et al. (2002)

and Garcilazo and Spiezia (2007).

Camilo Cárdenas, Alejandra Hernández y Jhon Torres 181

DESARRO. SOC. NO. 75, BOGOTÁ, PRIMER SEMESTRE DE 2015, PP. 153-196, ISSN 0120-3584

In sum, Cluster 1 is made up of cities where poverty and a lack of strong insti-

tutional background prevail, while Cluster 2 is characterized by high rates of

labour informality, low average income and high underemployment rates.

Cluster 3 is statistically different with low participation rates, especially for

the female working age population. This situation yields low unemployment

rates, but average wages and income suggest low quality of job positions.

Figure 5. Average Unemployment Rates for 23 Colombian Cities (2010)

Unemployment Rate

(2010 Average)

23 cities: 12,4%

Quibdó

Florencia

Valledupar

Riohacha

Popayán

Pasto

Montería

Neiva

Villavicencio

Sincelejo

Cartagena

Santa Marta

Barranquilla_AM

Pereira_AM

Ibagué

Armenia

Manizales_AM

Cúcuta_AM

Cali_AM

Tunja

Bucaramanga_AM

Bogotá_AM

Medellín_AM

Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 23 cities

Percentaje

Source: DANE (GEIH), author’s calculations.

Cluster 4 is perhaps the most interesting and complex group, since it is made

up of cities that displayed very high unemployment rates in 2010. Statisti-

cal tests suggest that cities in this cluster are different from others insofar as

their frictions in participation and in skilled workforce demand, but also their

migration and opportunity vulnerabilities, are quite particular. For example, the

coexistence of low educational attainment and a population pyramid biased

towards an ageing population pose challenges to the successful implementa-

tion of conventional public policy programs.

A Statistical Analysis of Heterogeneity on Labour Markets

182

DESARRO. SOC. NO. 75, BOGOTÁ, PRIMER SEMESTRE DE 2015, PP. 153-196, ISSN 0120-3584

Cluster 5 is made up of cities with high levels of educational attainment, wages,

productive population and a prosperous economic structure. We believe that

human capital accumulation is the main factor driving the good dynamics of

labour markets and economic growth in this cluster. We claim that particular

characteristics in these cities have fostered human capital accumulation over

the past few decades, as in Díaz (2013). Finally, Cluster 6 shares some labour

market and economic characteristics with cluster 5. However, there are still

some unresolved social and cultural issues in Medellín that influence labour

market performance and yield a higher unemployment rate than the average

for the metropolitan areas (Medina et al., 2011).

Our results provide a useful insight into labour market structures in Colom-

bia. However, there are still some minor differences in unemployment rates

between cities belonging to the same cluster (as shown in Figure 9) that are

not fully captured by differentials in variables used in this paper. We encour-

age future works to give a deeper insight into each one of these clusters in

order to explore such inner heterogeneities. Our findings suggest that some

cities share common structural characteristics that allow for variety in unem-

ployment rates in Colombian urban areas. However, it is clear that unemploy-

ment rates will likely decline over time with the implementation of city-based

actions designed to encourage participation, local incentives for low-skilled

labour intensive sectors, and regional youth educational programs.

Acknowledgments

The authors are currently working as economists at Banco de la República.

A previous version of this peer-reviewed paper was published in the work-

ing paper series Borradores de Economía, issue 802. The opinions, statements,

findings and interpretations presented in this paper are responsibility of the

authors and do not represent those of Banco de la República nor of its Board of

Directors. Usual additional disclaimers apply. We thank Daniel Quintero Castro

(dquintca@gmail.com), who participated actively in the early stages of this

paper. Comments from Luis Eduardo Arango, Adolfo Cobo and two anonymous

referees were very helpful, appreciated and acknowledged. Valuable assistance

was received from Jackeline Piraján and Natalia Solano.

The research undertaken to write this paper did not have any kind of institu-

tional funding.

Camilo Cárdenas, Alejandra Hernández y Jhon Torres 183

DESARRO. SOC. NO. 75, BOGOTÁ, PRIMER SEMESTRE DE 2015, PP. 153-196, ISSN 0120-3584

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Appendix 1

Table A1.1. Dataset Description

Group Variable Type of

var. Description Source

Demographic

Structure

TCPP q Population Growth Rate (Average 1986–2010)

2005

census

(DANE)

POBT_0–25 f Pop. under the age of 25 (y.o) (2010 estimate)

POBT_25–45 f Pop. between 26 and 45 y.o (2010 est.)

POBT_45–65 f Pop. between 46 and 65 y.o (2010 est.)

POBT_65+ f Pop. older than 65 y.o (2010 est.)

POBM_0–25 f Male under the age of 25 y.o (2010 est.)

POBM_25–45 f Male pop. between 26 and 45 y.o (2010 est.)

POBM_45–65 f Male pop. between 46 and 65 y.o (2010 est.)

POBM_65+ f Male pop. older than 65 y.o (2010 est.)

POBF_0–25 f Female pop. under the age of 25 y.o (2010 est.)

POBF_25–45 f Female pop. between 26 and 45 y.o (2010 est.)

POBF_45–65 f Female pop. between 46 and 65 y.o (2010 est.)

POBF_65+ f Female pop. older than 65 y.o (2010 est.)

PPIN q % Indigenous population (2010 est.)

PPAF q % Black population (2010 est.)

TMI q Infant mortality rate (2010)

TBN q Gross birth rate (2010)

Participation

TGP q Labour participation rate (2010)

GEIH

(DANE)

TGPM q Male labour participation rate (2010)

TGPF q Female labour participation rate (2010)

TDO q Hidden unemployment rate (2010)

TGPHJ q Young males labour participation rate

(< 25 y.o) (2010)

TGPH q Male labour participation rate (< 25 y.o) (2010)

TGPMJ q Young females labour participation rate (> 25

y.o) (2010)

TGPM q Female labour participation rate (> 25 y.o)

(2010)

EC_H_UL f Male – Marital Status: Free union (> 10 y.o)

EC_H_C f Male – Marital Status: Married (> 10 y.o)

EC_H_SE f Male – Marital Status: Divorced (> 10 y.o)

Camilo Cárdenas, Alejandra Hernández y Jhon Torres 191

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Table A1.1. Dataset Description (continued)

Group Variable Type of

var. Description Source

Participation

EC_H_V f Male – Marital Status: Widowed (> 10 y.o)

GEIH

(DANE)

EC_H_SOL f Male – Marital Status: Single (> 10 y.o)

EC_M_UL f Female – Marital Status: Free union (> 10 y.o)

EC_M_C f Female – Marital Status: Married (> 10 y.o)

EC_M_SE f Female – Marital Status: Divorced (> 10 y.o)

EC_M_V f Female – Marital Status: Widowed (> 10 y.o)

EC_M_SOL f Female – Marital Status: Single (> 10 y.o)

TH q Average size of household

EPN q Women’s median age on the first birth Profamilia

ENDS

REM q Remittances per capita BanRep

Migration

TMN q Net migration rate (2010) DANE

PREC q Incoming (displaced) people (per 100K hab.)

DPS

NPDEP q Displacement Register (Arrivals–people) (per

100K hab.)

NHDEP q Displacement Register (Arrivals–households)

(per 100K hab.)

Commuting

ICVSP q Public transport use Index (Author’s

Calculations) (2010)

ETUP

(DANE)

PSSP q # of pass. using Buses (per 100K hab.) 2010

PVSP q # Public use veh.in service (2010) (per 100K

hab.)

IPCVIV q Housing CPI (2008–2010 av.)

DANE

IPCTR q Transport services CPI (2008–2010 av.)

INFVIV q Housing CPI change (2008–2010 av.)

INFTR q Transport services CPI change (2008–2010 av.)

Wages

INP q Av. Nominal Income

GEIH

(DANE)

INM q Median Nominal Income

IRP q Av. Real Income

IRM q Median Real Income

SNPA q Av. nominal wage (waged–salary workers)

SNMA q Median nominal wage (waged–salary workers)

SRPA q Av. real wage (waged–salary workers)

SRMA q Median real wage (waged–salary workers)

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Table A1.1. Dataset Description (continued)

Group Variable Type of

var. Description Source

Wages

IPCALI q Food CPI (2008–2010 av.) – Living cost

DANE

INFALI q Food CPI change (2008–2010 av.) – Living cost

IPCTot q Total CPI (2008–2010 av.) – Living cost

InflaIPCTot q Total CPI change (2008–2010 av.) – Living cost

Regional

Growth

PIBT q Departmental total GDP Growth (2000–2011 av.)

DANE

PIB1 q Departmental Primary GDP Growth (2000–2011

av.)

PIB2 q Departmental Secondary GDP Growth

(2000–2011 av.)

PIB3 q Departmental Tertiary GDP Growth (2000–2011

av.)

PIB_agr q Departmental Agr. GDP Growth (2000–2011 av.)

PIB_min q Departmental Mining GDP Growth (2000–2011

av.)

PIB_ind q Departmental Manufact. GDP Growth

(2000–2011 av.)

PIB_ele q Departmental Energy GDP Growth (2000–2011 av.)

PIB_con q Departmental Construc. GDP Growth (2000–2011

av.)

PIB_com q Departmental Retail GDP Growth (2000–2011 av.)

PIB_tra q Departmental Transport GDP Growth (2000–

2011 av.)

PIB_fin q Departmental Financial GDP Growth (2000–

2011 av.)

PIB_ssv q Departmental Services GDP Growth (2000–2011

av.)

PIB_tax q Departmental Taxes GDP Growth (2000–2011 av.)

Market

Potential

DDM q Demographic density (2005)

DANE

ESML q Deviation of unemployment rate from its long–

term trend (tightness in labour market, Author’s

own calculations)

SEMD q Av. unemployment duration

AC q Trade Openness (Author’s own calculations)

DIST_MERC q Weighted distance to major markets (Bogotá,

Medellín, Cali and Barranquilla) Ocaribe

–SID

IHHM q Herfindahl – Hirschman Market Index

Camilo Cárdenas, Alejandra Hernández y Jhon Torres 193

DESARRO. SOC. NO. 75, BOGOTÁ, PRIMER SEMESTRE DE 2015, PP. 153-196, ISSN 0120-3584

Table A1.1. Dataset Description (continued)

Group Variable Type of

var. Description Source

Market

Potential

EPE_2009 q Efficiency of Business Processes

Ocaribe

–SID

IDI_2009 q Industrial Density Index

DIST_MERC q Weighted distance to major markets (Bogotá,

Medellín, Cali and Barranquilla)

EPE_2009 q Efficiency of Business Processes

IDI_2009 q Industrial Density Index

CostConst q Av. cost of building a warehouse

Aemp q Starting a business (costs and requirements to

TIT q Total Taxes rate 2010

Economic

Structure

IHHP q Herfindahl – Hirschman Product Index Ocaribe

–SID

PART_agr q Agricultural GDP Share (2000–2011 av.)

Authors’

own

calculations

from

Departmental

GDP (DANE)

PART_min q Mining GDP Share (2000–2011 av.)

PART_ind q Manufacturing GDP Share (2000–2011 av.)

PART_ele q Electricity GDP Share (2000–2011 av.)

PART_con q Construction GDP Share (2000–2011 av.)

PART_com q Retail GDP Share (2000–2011 av.)

PART_tra q Transport GDP Share (2000–2011 av.)

PART_fin q Financial GDP Share (2000–2011 av.)

PART_ssv q Services GDP Share (2000–2011 av.)

PART_tax q Taxes GDP Share (2000–2011 av.)

VAR_PART_agr q Change on agricultural GDP share (2000–2011

av.)

VAR_PART_min q Change on mining GDP share (2000–2011 av.)

VAR_PART_ind q Change on industry GDP share (2000–2011 av.)

VAR_PART_ele q Change on electricity GDP share (2000–2011

av.)

VAR_PART_con q Change on construction GDP share (2000–2011

av.)

VAR_PART_com q Change on trade GDP share (2000–2011 av.)

VAR_PART_tra q Change on Transport GDP share (2000–2011 av.)

A Statistical Analysis of Heterogeneity on Labour Markets

194

DESARRO. SOC. NO. 75, BOGOTÁ, PRIMER SEMESTRE DE 2015, PP. 153-196, ISSN 0120-3584

Table A1.1. Dataset Description (continued)

Group Variable Type of

var. Description Source

Economic

Structure

VAR_PART_fin q Change on financial GDP share (2000–2011 av.)

Authors’ own

calculations

from

Departmental

GDP (DANE)

VAR_PART_ssv q Change on services GDP share (2000–2011 av.)

VAR_PART_tax q Change on taxes GDP share (2000–2011 av.)

AS f # of wage–salary workers (2010)

GEIH (DANE)

NAS f # of unpaid wage workers (2010)

EMPA f # of private employees

EMGOB f # of government employees

EMDOM f # of domestic employees

EMCP f # of self employed

EMPT f # of bosses

EMFSR f # of unpaid family workers

EMSR f # of unpaid workers

EMJ f # of workmen

EMO f # of other workers

NPAI f Ppl. on Real estate activities

NPAG f Ppl. on Agr.

NPCM f Ppl. on Retail

NPCN f Ppl. on Construction

NPEG f Ppl. on Gas and Water Supply

NPFN f Ppl. on financial intermediation

NPIN f Ppl. on manufacturing

NPMN f Ppl. on Mining activities

NPSS f Ppl. on Services

NPTN f Ppl. on Transport sector

Economic &

Social

Barriers

ICV q Life Quality Index (2005) DNP

DHV q % of households w/o housing (2005)

2005

census

(DANE)

DHVC q % of households w/o housing (2005) –

quantitative

DHVQ q % of households w/o housing (2005) –

qualitative

NBI q Unsatisfied basic needs (2010)

Camilo Cárdenas, Alejandra Hernández y Jhon Torres 195

DESARRO. SOC. NO. 75, BOGOTÁ, PRIMER SEMESTRE DE 2015, PP. 153-196, ISSN 0120-3584

Table A1.1. Dataset Description (continued)

Group Variable Type of

var. Description Source

Economic &

Social

Barriers

PIBPC q GDP per capita (2010)

Authors’

computations

from

Dept. GDP

series

PIBPC_SMIN q GDP per capita w/o mining (2010)

C_PIBPC q GDP per capita Growth (2001–2010 av.)

C_PIBPC_SMIN q GDP per capita Growth w/o mining (2001–2010

av.)

NH_V q Number of households per housing

GEIH

(DANE)

COB_ELEC q % of housing w/o electricity

COB_ACU q % of housing w/o Aqueduct

COB_SAN q % of housing w/o sewage system

COB_BAS q % of housing w/o garbage disposal

COB_GAS q % of housing w/o natural Gas

EJ_FBKF q Gross fixed capital formation per capita

Execution (2010)

Authors’

Computations

based on

information

from

MHCP.

REGA q Income from royalties per capita (2010)

TRANS q National transfers income per capita (2010)

NATH q # of requests for Humanitarian Ass. (2010) per

100K habs. DPS

NNICBF q # of children aided by ICBF (2010) – per 100K

habs.

HOM q Murders per 100.000 inhabitants (2010) Ocaribe

–SID

Education

COBP q Primary education Coverage (2010)

MEN

COBS q Secondary education Coverage (2010)

COBM q High school Coverage (2010)

IENO q Non–public Institutions per 100K habs. (2010)

IEO q Public Institutions per 100K habs. (2010)

POBA q % Literate population (2010)

POBAN q % Illiterate population (2010)

HOG_SININT q % of household w/o Internet (2010)

GEIH

(DANE)

HOG_SINPC q % of household w/o PC (2010)

APED_PET q Av. # of school years – Working age pop. (2010)

APED_OC q Av. # of school years – Employed (2010)

APED_DE q Av. # of school years – Unemployed (2010)

A Statistical Analysis of Heterogeneity on Labour Markets

196

DESARRO. SOC. NO. 75, BOGOTÁ, PRIMER SEMESTRE DE 2015, PP. 153-196, ISSN 0120-3584

Table A1.1. Dataset Description (continued)

Group Variable Type of

var. Description Source

Education

APED_DE_0–5

f % of Unemployed between 0 and 5 years of

education (y.o.e) (2010)

GEIH

(DANE)

APED_DE_6–11

f % of Unemployed between 5 and 11 y.o.e (2010)

APED_DE_12–13

f % of Unemployed between 12 and 13 y.o.e

(2010)

APED_DE_14–15

f % of Unemployed between 14 and 15 y.o.e

(2010)

APED_DE_15+

f % of Unemployed more than 15 y.o.e (2010)

APED_OC_0–5

f % of Employed between 0 and 5 y.o.e (2010)

APED_OC_6–11

f % of Employed between 6 and 11 y.o.e (2010)

APED_OC_12–13

f % of Employed between 12 and 13 y.o.e (2010)

APED_OC_14–15

f % of Employed between 14 and 15 y.o.e (2010)

APED_OC_15+

f % of Employed more than 15 y.o.e (2010)

APED_PET_0–5

f % of Working age population between 0 and 5

y.o.e (2010)

APED_PET_6–11

f % of Working age population between 6 and 11

y.o.e (2010)

APED_PET_12–13

f % of Working age population between 12 and

13 y.o.e (2010)

APED_PET_14–15

f % of Working age population between 14 and

15 y.o.e (2010)

APED_PET_15+

f % of Working age population more than 15

y.o.e (2010)

APED_INA_0–5

f % of Inactive population between 0 and 5 y.o.e

(2010)

APED_INA_6–11

f % of Inactive population between 6 and 11 y.o.e

(2010)

APED_INA_12–13

f % of Inactive population between 12 and 13

y.o.e (2010)

APED_INA_14–15

f % of Inactive population between 14 and 15

y.o.e (2010)

APED_INA_15+

f % of Inactive population more than 15 y.o.e

(2010)

Unionisation PSIN q % of Unionised labour force GEIH

(DANE)

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