Programa de Pasantías InternacionalesBuenos Aires - Argentina

Observatorio New School - SES

“Determinantes urbanos, económicos y estructurales en la generación de empleo en

Argentina”

Director de GPIA de The New School University

Michael Cohen

Coordinador Académico del Programa de Pasantías en Bs. As.

Alberto Minujin

Pasantes del programa 2012 en ACUMAR

Emily Miller

2012

Urban, Economic, and Structural Determinants of Employment Generation in Argentina

Emily Miller

The New School

Argentina IFP, 2012

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Acknowledgements

First and foremost, I would like to thank Martín Abeles at CEPAL for his insight, patience, and

thought-provoking discussions. I deeply grateful for Roxana Maurizio’s help in conceptualizing

the work in its initial stages and for making the work with microdatos possible. Thanks to the

entire Buenos Aires CEPAL office and Alberto Minujín for supporting me in this internship, with

a special thanks to Daniel Vega for his encyclopedic knowledge of data, and to Pascual

Gersetenfeld for ensuring a smooth transition. Finally, I am indebted to Michael Cohen for

planting the seed both of this work and of the relationship with CEPAL.

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INTRODUCTION

Employment has become an increasingly important topic for both developing and

developed nations. Developed nations such as the United States struggle with the effects of

recent financial crises, and debates continue over how to beckon back down elevated

unemployment rates. Developing nations such as Argentina boast relatively more financial

stability compared to past decades, but continue to struggle with generating quality employment

for all sectors of the population. This analysis focuses on employment generation on a macro

level, integrating the fields of macroeconomics and urban policy/planning. While employment

has been discussed in both economic and urban studies, such analyses have largely remained in

their respective silos. In contrast, this study seeks to understand the city as an economic entity,

linking macroeconomic regimes with urban factors in order to provide a more integrated analysis

of the determinants of employment generation in Argentina.

The distinct repercussions in the labor market of the opposing macroeconomic regimes

Argentina, specifically the Convertibility period (1991-2001) and the post-crisis model (2003-

2010), are the focus of the first section of this paper. Illustrating the fundamental link between

macroeconomic regimes and employment generation, this first section outlines the basic tenants

of the two macroeconomic models, the resulting trajectories of the real exchange rate and gross

domestic product (GDP), and the mechanisms of transmission to the labor market. This section

shows that without GDP growth, there cannot be employment, but that growth alone does not

necessarily ensure employment growth as happened in the 1990s. Rather, economic growth must

be accompanied by a favorable set of exchange rate and institutional (labor and social) policies

that help ensure parallel growth in the labor market.

While employment generation was much greater in the post-Convertibility period, such

growth was not homogenous across cities. The second part of this study therefore enters into the

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urban policy dynamic to hypothesize why under a fixed macroeconomic regime, different areas

have been able to generate more employment than others. Urbanization is a salient topic given

the high urbanization rates in Latin America and the Caribbean – at around 80%, they are some of

the highest in the world1.

Argentina is no exception, with an

urbanization rate at 89% of the

population (Prud’homme,

Huntzinger, & Kopp, 2004).

Moreover, there is a positive

relationship between urbanization

and per capital GDP growth,

which shows that while urbanization does not guarantee economic growth, it does go hand in

hand with economic development (Buckley, 2012; CEPAL, 2012). In this second section,

theories of economies of agglomeration are used to trace the transmission mechanisms between

size and density and employment generation.

The third section introduces two additional determinants of employment generation: the

production structure and the provision of infrastructure. Using the discourse of structuralist

economics, it is hypothesized that a more complex production structure (less structural

heterogeneity) should facilitate higher levels of employment generation. The role of

infrastructure is presented as having a facilitating role in the reduction of structural heterogeneity,

and in the realization of benefits from urban size and density.

The fourth section outlines two econometric models used the test the relative weights of

1 When urban areas are measured more liberally as areas over 2,000 people, the urbanization rate in Latin America and the Caribbean has grown from 40% to 80% of the population between 1950 and 2000. Even when urban areas are measured with a more restrictive value of 20,000 people or more, the urbanization rate has still more than doubled, increasing from 32% of the population in 1950 to 62% of the population in 2000 (CEPAL, 2012).

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macroeconomic regimes and urban factors in employment generation. The first model is a macro

level estimation of aggregate labor demand, using time series data on GDP, city size and density.

The second model is a micro level estimation of the probability of being employed, analyzing the

effects of size, density, and production structure complexity, after controlling for demographic

variables such as age, gender, and education. The results are presented in section five.

Section six presents three categories of policy implications as well as topics for future

research. The first deals with the importance of macroeconomic regimes, reiterating the

importance of a competitive real exchange rate which increases the profitability of tradable

sectors and promotes industrialization. The second relates to production structure complexity and

argues for policies which reduce structural heterogeneity via education and technology policies,

and support to small and medium enterprises. The third relates to size and justifies the use of

industrial policies in cities which are too small to benefit from the endogenous employment-

generation capacity of large cities stemming from agglomeration economies. Finally this study

argues that while the models show a positive effect of city size on employment generation, one

must be wary of “too much of a good thing.” While size and density generate positive

employment dynamics, there is a saturation point past which congestion and other negative

effects counterbalance the earlier benefits. This leads into a discussion on regional spatial

planning and argues for the use of policies to encourage the development of medium-sized cities.

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1. THE ROLE OF THE INTERNATIONAL CONTEXT AND MACROECONOMIC

REGIMES

Following Damill and Frenkel (2011), the macroeconomic evolution of Argentina should

be understood as a result of the interaction between changes in the international context, the

particular structural configuration of the local economy, and the main characteristics of the

national macroeconomic policy. This section therefore seeks to link these three areas in a broad

review of the distinct macroeconomic regimes implemented in Argentina between 1990 and 2010.

1.1 INTERNATIONAL CONTEXT

As a consequence of the financial integration implemented in the second part of the

seventies, many Latin American countries had accumulated high and persistent current account

deficits and external debt; Argentina was no exception. However in the 1980s, the erosion of

foreign finance and sudden spike in international interest rates due to contractionary policies in

the US triggered massive balance of payments crises in Argentina, Brazil, Chile, Ecuador, Peru,

Uruguay and Venezuela. Unlike the inflation situation Chile and Colombia in which levels never

rose above 35%, inflation in Argentina stubbornly remained in triple digits, only to be disciplined

by a strict fixed peg exchange rate regime implemented in 1991 (Frenkel & Rapetti, 2012).

Unlike the 1980s, the international environment in the 1990s was more favorable in terms

of capital availability. Due in part to the Brady plan, the 1990s initially featured high liquidity

and low interest rates, a marked change from the “credit rationing” of the previous decade

(Frenkel & Rapetti, 2012). Nevertheless, the nineties were a time of high financial volatility for

emerging economies. Rapid and unrestricted financial and commercial openings, exposure to

international liquidity cycles (and consequent increases in external debt), dismantling of labor

institutions via labor flexibilization policies, and significant real exchange rate appreciations led

to cyclical dynamics, which resulted in frequent financial crisis (Pastrana, Toledo & Villafañe,

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2012). These included the Tequila crisis in Mexico in 1994, and the Asian and Russian crises in

1997 and 1998, the sizable Brazilian devaluation in 1999, and of course the Argentina crisis of

2001.

1.2 DOMESTIC MACROECONOMIC POLICIES

The Convertibility Plan

Following the economic stagnation, triple digit inflation, and massive balance of

payments crisis in the 1980s, in 1991 the Argentine peso was pegged to the US dollar at a ratio of

1:1. This marked the beginning of the Convertibility regime, in which the fixed exchange rate

functioned as the nominal anchor necessary to control inflation. The implementation of a fixed

nominal exchange rate (NER) was done concurrently with an almost complete liberalization of

trade flows and a full deregulation of the capital account (Frenkel & Rapetti, 2012). In addition

to this stabilization program, a variety of structural reforms including the privatization of state-

owned enterprises,2 modification of the pension system, and labor market flexibilization and

deregulation policies were implemented. Important labor contract regulation changes included a

reduction in social security contributions of employers, the authorization of part-time contracts

with lower firing and social security contributions, and a reduction in severance payments, among

other policies (Damill, Frenkel, & Maurizio, 2011). As a result of these widespread market-

oriented reforms for which the World Bank and the International Monetary Fund advocated

heavily, Argentina was dubbed the “poster child” of neoliberalism as embodied in the Washington

Consensus (Cohen, 2012). These bold actions successfully stabilized prices and combated high

inflation.

Nevertheless, under this exchange rate regime, Argentina became ever more dependent

on external capital, making it more prone to financial crises. Moreover, the inflexibility of the

2 Privatizations of public services were wide ranging and included water, electricity, transport, telecommunications, garbage collection, and mail services (Cohen, 2012).

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currency board meant that different exogenous shocks were transmitted directly to the labor

market meaning. As a result volatility in external markets had direct ramifications on the local

credit market and domestic level activity, and consequently employment (Cohen, 2012); yet

policy space remained limited as the fixed exchange rate completely annulled the role of

exchange rate policy in correcting for an appreciated exchange rate (although the use of such

policy later proved to be key in Argentina’s macroeconomic configuration post-Convertibility)

(Maurizio, Perrot, & Villafañe, 2009). Space for countercyclical policy, e.g. expansionary

policies during moments of crisis, was also restricted due to the prioritization of debt reduction

(fiscal consolidation).

Argentina’s Macroeconomic Regime Post-Convertibility

The economics goals outlined by the Argentina government in the post-Convertibility

regime were three-fold: domestic financing for economic growth, re-industrialization (i.e.

supporting non-traditional sectors, ensuring profits for the manufacturing industry), and

employment generation (Pastana et al., 2012). The macroeconomic regime adopted after the

2001-2002 crisis differed fundamentally from the Convertibility regime in its preservation of a

stable and competitive real exchange rate (SCRER). While the Convertibility regime relied on a

fixed peg, the regime in the 2000s was based on a managed floating arrangement. This was

similar to the Chile’s crawling bands strategy used from 1984-1999, and aimed to combine short-

run NER volatility with the preservation of a SCRER in the medium run. Argentina´s Central

Bank followed quantitative money targeting in which targets were announced at the beginning of

every year and money aggregates acted as the nominal anchor. Intervention by the Central Bank

in the FX market (to control the NER and thereby maintain the SCRER) was done using

sterilization operations to prevent the money supply for expanding outside the target range. Such

a managed floating regime provided the same flexibility to absorb unexpected external shocks as

a floating regime, but intervention in the FX market made possible the important prevention of

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distortionary effects of large capital inflows on the NER (Frenkel & Rapetti, 2012). This new

macroeconomic regime allowed for high levels of employment generation, resulting in sustained

falls in unemployment and underemployment. Unemployment decreased from 19.7% in the

fourth quarter of 2003 to 7.6% in the same period of 2008 (Abeles, 2012). Moreover, real wages

recovered, poverty declined, and the income distribution improved (Damill et al., 2011).

The post-crisis regime also contrasted with the Convertibility regime with respect to its

healthy dose of social policies. To deal with the skyrocketing unemployment due to the crisis, the

National Economic Emergency Law suspended unjustified layoffs for 180 days and doubled the

severance pay. Plan Jefes y Jefas de Hogar Desocupados (Program for the Unemployed Male and

Female Heads of Housholds) provided an income equal to 75% of minimum wage in 2002 to

about 2 million beneficiaries, equal to 11% of the active population. During the economic

recovery between July 2003 and September 2009, more than 600,000 beneficiaries found jobs in

the formal sector. By September 2009, the program covered less than 400,000 people and was

replaced at the end of 2009 by the cash-transfer program Asignación Universal por Hijo

(Universal Allowance per Child) (Damill et al., 2011).

Labor market policies were steered the opposite direction from the deregulation and

flexibilization trends of the nineties. The institutional configuration in the post-Convertibility

period reconstructed a space for collective and individual labor market relations, reinstigating

workplace inspections, promoting collective bargaining, and recovering state capacity to mediate

labor conflicts. The number of people in collective bargaining agreements rose from 3.5 million

in 2002 to 5.8 million in 2008, and the minimum wage rose by 520 percent between 2002 and

2009 (Abeles, 2012). A new labor law in 2004 reversed some of the severance pay reductions

passed in the 1990s and also simplified the labor registry system to promote the formalization of

workers (Damill et al., 2011).

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1.2.2 GDP GROWTH TRAJECTORIES

The GDP trajectory of the 1990s followed a cyclical pattern which was repeated twice

during the decade; the first cycle took place between 1990 and 1995, resulting in a financial crisis

and recession; the second took place between 1996 and 2001, ending in the collapse of the

Convertibility regime. The stylized pattern is as follows. Initially, capital inflows spurred by

high interest rate differentials begin an expansionary phase. Foreign reserves are accumulated,

and domestic credit and aggregate demand expand. A RER appreciation follows due to increases

in prices and wages for non-tradables caused by demand pressures, and inertial inflation. The

RER appreciation and expansionary effects of capital inflows provoke a current account deficit

and growing external debt (Frenkel & Rapetti, 2012). The worsening of the current account

balance weakens the credibility of the exchange rate rule while the increase in external

vulnerability and probability of devaluation is reflected in a slowdown of capital inflows, and a

rise in domestic interest rates and the country risk premium. Reserve accumulation stops and a

contraction begins; higher interest rates and capital outflows give way to an illiquid financial

scenario ending in a financial crisis and possible collapse of the exchange rate regime as occurred

in 2001 (Frenkel & Rapetti, 2012; Damill et al., 2011).

The Argentine economy underwent expansionary growth periods between 1991 and 1994,

and 1996 to 1998. The recession in 1994-1995 was triggered by the Tequila crisis in Mexico,

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which prompted a sudden stop of capital inflows to Argentina. With the help of a sizeable

financial assistance package by the International Monetary Fund (IMF), Argentina was able to

preserve the Convertibility regime in 1995, however economic activity contracted by 5.6% due to

capital outflows. The currency crisis in Brazil and the contagion from the Asian and Russian

crises in 1997-1998 triggered a prolonged recession in Argentina starting in 1998, which in

conjunction with an unsustainable external debt, culminated in financial and external crises in

2001-2002 (Frenkel & Rapetti, 2012). The collapse of the convertibility regime caused a

substantial 19.9% contraction in GDP and an increase in the unemployment rate to 21.5%,

leaving more than half of the population below the poverty line. This increase in poverty resulted

from the combination of the jump in prices immediately after the devaluation and the already

precarious previous social situation.

After the crisis, GDP continued to fall in the first quarter of 2002, stabilizing in the

second quarter, and increasing starting from the third quarter of 2002. Rapid and sustained

growth was maintained until 2008. Importantly, the post-Convertibility regime showed a marked

reduction in volatility compared to the cyclical nature of GDP in the nineties. In contrast with

international predictions that such a large crisis would essentially wipe Argentina off the map,

Argentina underwent one of its most successful growth episodes in its history (mid-2002 to mid-

2008) during which the economy grew at an average annual rate of 8.5%. By mid-2005, GDP

had already reached the maximum level achieved in 1998 (Maurizio, 2009) and for the first time

in modern history, Argentina boasted simultaneous external and fiscal surpluses for an extended

period (2003-2008) – an important contrast from the twin deficits experienced between 1994 and

2001(Frenkel & Rapetti, 2012). It should be noted that unlike in past growth episodes, a current

account surplus was maintained during the economy expansion, therefore growth did not come at

the expense of increased indebtedness. Nevertheless, the dynamism of output started to slow in

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beginning in late 2007.3 Argentina’s country-risk premium increased and capital flight became an

“almost permanent feature of the Argentina balance of payments” from the third quarter of 2007

to the second quarter of 2010 (Pastana et al., 2012). Still, Argentina was in a relatively strong

position to face the global economic crisis in 2008-2009 given the sizable stock of international

reserves it had accumulated and the liquidity and solvency of its financial system. For the first

time in decades, such a significant external shock did not trigger a balance of payments crisis or

wreak havoc on the local financial system in Argentina (Abeles, 2012). The two channels of

influence were the financial channel and the trade channel, as private capital outflows increased

substantially and exports plummeted by 21% in 2009 (Damill et al., 2011). These effects are

evidenced by the drop in GDP growth rates in 2008-2009.

1.3.1 EXCHANGE RATE REGIMES

As previously mentioned, the nominal exchange rate was fixed at a ratio of 1 to 1 under

the Convertibility plan. The inability of the peso to adjust via devaluation eventually undermined

the sustainability of the currency board rule which was formally abandoned in January 2002,

however it is important to note that the real exchange rate was already significantly appreciated

when the nominal exchange rate was pegged to the dollar (Damill et al., 2011).

After the partial default on the public debt and collapse of the Convertibility exchange-

rate parity, the peso underwent a rapid devaluation reaching levels close to 4 pesos to 1 dollar.

3 Damill et al. (2011) attribute this in large part to the acceleration of inflation, a highly debated topic. According to the official statistics published by the United Nations Economic Commission on Latin American and the Caribbean, inflation has remained within the range of 7 to 10% since 2006. The Ministry of Labor, Employment and Social Security (Ministerio de trabajo, empleo y seguridad social) has made little reference to inflation. In fact, in its report reviewing the post-crisis period published for the bicentennial entitled Trabajo y empleo en el bicentenario: Cambio en la dinámica del empleo y la protección social para la inclusión, the topic of inflation is avoided altogether. Nevertheless, official inflation rates from the provinces denote worrisome signs of signs of accelerating inflation.

Frenkel and Rapetti (2012) propose that inflation was partly due a combination of rising commodity prices, an increase in wages beyond productivity growth, and an uncoordinated macroeconomic policy. While monetary and exchange rate policies were focused on maintaining an SCRER, fiscal policy was not used to moderate aggregate demand when inflationary pressures arose. Instead, public spending reached a level significantly above what was gained via increased tax revenues since 2006, serving to further boost aggregate demand.

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The magnitude of this devaluation is evidenced by the sharp spike in the graph. July 2002 proved

to be an important turning point given the implementation of policies to stabilize prices after the

sharp devaluation and the imposition of a new exchange rate regime, both of which served to

reestablish macroeconomic equilibriums. Controls on foreign exchange were strengthened and

interventions in the exchange market began to form part of a systematic policy intended to

stabilize the foreign exchange market (Damill et al., 2011). After the exchange rate bubble was

stopped, a nominal appreciation began, which by mid 2003 the Central Bank began to counter,

thus beginning the new exchange rate policy.

A strong increase in the level of internal prices, for once not initially due to inflation,

followed the rapid depreciation. However unlike previous instances, this devaluation did not

result in an inflationary process due to i) weak domestic demand which limited the pass-through

to consumer prices, ii) high unemployment which eliminated potential wage indexation, iii) a lack

of liquidity due to bank deposit restrictions, iv) the government decision to freeze prices of public

utilities, and v) taxes on exports which put a ceiling on the domestic prices of exportable goods

(Maurizio, 2009; Damill et al., 2011). After its peak April 2002, the monthly inflation rate tended

to be lower than 1% for the following two years (Damill et al., 2011). The graph below plots the

real exchange rate deflated by wages from 1995 to 2010.

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The exchange rate is a fundamental part of the macroeconomic regime due its wide-

ranging effects. Frenkel and Taylor (2006) outline five channels of influence: resource allocation

(via macroprice ratios), economic development (via an enhancement of competitiveness and

thereby a boost productivity and growth), finance (via control of expectations and behavior in

financial markets), external balance (via ‘substitution’ responses and changes in effective demand

which influence trade and consequently the current account), and inflation (an important

transmission mechanism for monetary policy effects). Out of the five areas of influence of the

RER listed by Frenkel and Taylor, the role of the exchange rate in resource allocation is most

relevant to this discussion of employment as the RER determines macroprice ratios between

tradable and non-tradable goods, capital goods and labor, and even exports and imports (via the

cost of intermediate inputs and capital goods, for example).

Frenkel and Taylor (2006) outline the destruction of employment brought about via an

RER appreciation as follows. Producers of importables will face tougher foreign competition and

cost cutting is often done by shedding labor. If they are forced to close down altogether, jobs will

be destroyed and non-tradable sectors will have to absorb labor displaced from the tradable

sector, however jobs are less likely to open up there, as cheaper foreign inputs (i.e. intermediates

and capital goods) are substituted for domestic labor. Appreciated RERs are also “an invitation to

disaster” as they can provoke destabilizing capital flow cycles that contribute to fiscal and

external crises. It must also be remembered that employment destruction happens much faster

and easier than employment generation. In contrast, a relatively weak RER can help boost

employment by increasing the profitability of the tradable sector and decreasing labor unit costs.

The expansion of exports reduces the balance of payments constraint to growth and also allows

for a systematic accumulation of FX reserves which serves to protect the economy from external

shocks (Damill & Frenkel, 2011).

As outlined by Frenkel and Taylor (2006), the exchange rate had an important negative

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impact on the local production structure. Given that the fixed exchange rate prohibited corrective

depreciation, the resulting RER appreciation in conjunction with the radical opening to imports

obligated firms to become more competitive or shut down. Firms responded by increasing the

use of imported inputs, importing products previously produced domestically (as imports were

favored by the exchange rate), and substituting labor with capital – all measures aimed at

reducing costs and increasing productivity. The result was an intense loss of jobs in tradable

sectors, particularly manufacturing (Damill et al., 2011). Industrial sector employment fell from

28% of total employment in 1995 to 23% of total employment in 2000, amounting to the loss of

approximately 57 million manufacturing jobs (Castillo et al., 2002 as cited in Mazorra &

Becarria, 2007). The impact on manufacturing is important given that structural economists

(such as Hirshmann, Rosentein-Rodan, and Gerschenkron) cite the manufacturing sector as the

engine of development via its linkages with productivity increases [which via forward and

backward linkages, knowledge spillovers and technological externalities give way to increasing

returns] (Kaldor 1996 as cited in Cimoli, Novick, & Palomino, 2007). Pieper (2000) (as cited in

Cimoli et al., 2007) that low industrial sector growth results in few virtuous cycles as countries

remain on the track of low-road development in which there is a trade-off between productivity

growth and employment growth. Moreover short-term adjustments via unemployment, lower

salaries and reduced public spending can have long term effects on the “dynamic efficiency” of

the economy via hard to reverse destruction of human capital, thereby inhibiting future growth

possibilities (Van der Hoeven, 2000 as cited in Cimoli, Novick & Palomino, 2007).

1.3 MANIFESTATION IN THE LABOR MARKET

Having reviewed the exchange rate and output trends of the two regimes, the following

section presents their cumulative effects on employment. The main channels of transmission

from the macroeconomic regime to the labor market are via aggregate demand (and consequently

level of activity) and relative price ratios (RER, real wage, etc.) which determine labor utilization.

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The Convertibility period was characterized by a low dynamism of aggregate

employment demand and high levels of unemployment. In general, unemployment rose, labor

market segmentation and informality increased, and wages and employment became more

volatile (Pastrana, et al., 2012). Both the direct transmission of exogenous shocks to labor

markets and the decrease in prices of capital goods prices relative to that of labor served to drive

down the elasticity of labor demand. The employment rate reached its peak for the decade in

1992, showing a marked fall afterwards down to its lowest point in 1996. Between the maximum

of 34.1% and the minimum observed in the second half of 1996, the employment rate fell

approximately 5.2% (Damill et al., 2011). As with GDP, the employment rate showed two clear

cycles.

Strong correlation between GDP and employment rate (R=0.8662)

Initially, capital inflows fed aggregate demand and the economy grew quickly. High

growth rates encouraged job creation, most intensely in non-tradable sectors. Nevertheless, the

trade opening and appreciated exchange rate deterred employment in the industrial sector which

explains why even the rapid GDP growth present in the early nineties was not accompanied by

increased labor demand (Maurizio, 2009). The low elasticity of labor demand (0.15) in

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conjunction with an increased labor supply resulted in increased unemployment, which reached

double-digit levels even before the Mexican crisis of 1994. After the 1995 recession induced by

the Mexican crisis, the economy recovered and employment creation was in tandem with GDP

growth. The employment rate recovered by about 2.4%, reaching a new peak in the first semester

of 1998. The elasticity of labor demand increased to 0.62 which lead to a significant generation

of new jobs. Moreover, the participation rate began to stabilize which helped reduce

unemployment. However during this period, employment creation in the non-tradable sectors

was insufficient to counterbalance the negative effects in employment generation in the

manufacturing sector, thus neither employment nor unemployment rates reached pre-Tequila

levels (Damill et al., 2011). As shown in the previous graph, October 1998 to October 2001 was

a period of severe labor market and macroeconomic worsening. From 1998-2002, GDP

experienced strong contractive phases, dropping around 20% with 60% of this fall in 2001 alone

(Maurizio, 2009). The recession beginning in 1998 induced additional unemployment pressures.

It is important to note that during this period, the increase in unemployment was due fully to the

reduction in available positions, not the participation rate as the latter had actually decreased. At

the end of 2001, the open unemployment rate neared 20% (Maurizio et al., 2009).

Such instability has negative effects on the labor market as it signified a lower use of

productive capacity (restricting labor force potential) and an inferior productivity than in a stable

situation. In other words, liberalization increased financial activity without an increase in

national savings, leading to a very low investment rate in fixed capital and intense fluctuations in

economic activity and employment. Volatility gave way to financial and exchange rate crises

whose recessive effects discouraged the formation of national capital, and resulted in a

deteriorated labor market with increased formality (Ffrench-Davis, 2011). Thus while the

Convertibility plan succeeded at curbing four decades worth of inflation, it did not trigger

significant job growth. The instability caused by the volatility of macro variables – due to a

17

combination of external shocks, capital flows, and domestic policies – and deterioration of

capacity due to the appreciated exchange rate rather served to discourage employment growth

(Ffrench-Davis, 2011; Maurizio, 2009).

Between October 2001 and May 2002, there was a significant contraction in employment.

Between July 2001 and July 2002, according to INDEC 755,000 jobs were lost; by June 2002,

Argentina had the fourth highest unemployment rate in the world (Cohen, 2012). However by the

fourth quarter of 2002, employment growth was enough to stop the fall in net employment.

During the first years after the devaluation of the peso, the creation of formal jobs was so rapid it

even surpassed high GDP growth levels. The implementation and expansion of the Programa

Jefes de Hogar facilitated this rapid employment generation. Nevertheless, the program was

relatively short-lived and a reduction in beneficiaries was observed starting as early as the middle

of 2003, by which time the substantial number of jobs created in the private sector more than

compensated for such reduction. In October 2002, the employment rate (including employment

programs) was already higher than the last observation of the convertibility period one year

before. If the effects of the employment plan are excluded, the full recovery was completed by

the second quarter of 2003 (Damill et al., 2011).

Between the third quarter of 2003 and the same period in 2008, the employment rate rose

substantially from 38% to 42%. By the third quarter of 2002, the employment level had

surpassed than the 1998 level – the maximum level of the nineties. Likewise, the unemployment

rate fell by more than 50%, from 16.3% to 7.8% over the same period. Employment dynamism

started to weaken slowly since mid-2004, nevertheless the response of employment to the

domestic activity level remained very elastic. This is in direct contrast with the 90s in which high

levels of GDP growth were not necessarily accompanied by increasing employment levels

(Damill et al., 2011). As of late 2006, employment generation has stagnated as evidenced by the

relative flattening-out of the employment rate curve in the late 2000s.

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It should be noted that the increase in jobs post-2003 was led by an increase in full-time

employment (78% of total employment in 2003, 90% at the end of 2008) (Damill et al., 2011).

The improved job quality (i.e. social security, health benefits, job safety, job stability, recognition

of collective bargaining agreements, job training and job mobility) was also an important change

(Cohen, 2012). With regard to informality and job precariousness – a key feature of the labor

market in the nineties – registered job positions explained 80% of new employment in the period

2003-2010, increasing by 53% while non-registered jobs only rose by 10% during the same

period (explaining 11% of job creation) (Damill et al., 2011). Nevertheless, precariousness

remains a problem as informal workers (equal to 43.1% of the workforce in the 4 th quarter of

2010) receive an hourly wage equivalent to only 40% that of registered workers.

Since 2003, commerce and construction together with industrial activities and financial

services have made the highest contribution to employment generation, explaining about 70% of

new employment (Maurizio, 2009). The manufacturing sector, to which the fixed exchange rate

and commercial opening were detrimental, was finally able to break out of the downward trend

experienced in the 1990s, growing at an average rate of 9.5% for 2003-2008. This is the highest

growth rate of manufacturing in the previous 60 years. Manufacturing increased its share in GDP

from 17.4% (1993-2001) to 21% (2003-2008), and employment in the sector grew by 42%

between 2002 and 2008 (Abeles, 2012; Cohen, 2012). Nevertheless due to the sustained

contraction of the nineties, employment in manufacturing activities remained 22% lower in 2008

than it was at the beginning of the nineties.

The negative effects on labor demand due to the global economic crisis further served to

weaken employment generation in the late 2000s, however government-sponsored programs such

as the Productive Recovery program (REPRO4) that preserved some jobs in the formal sector and

4 This program established government-provided subsidies to companies (government pays part of workers’ wages to maintain employment levels). In 2009, the program covered 143,000 workers (equivalent to 1 percentage point of the unemployment rate) belonging to 2750 companies (Damill et al,

19

an increase in public employment have helped to mitigate falls in employment rates (Damill et

al., 2011). Unemployment rates increased and employment rates fell as a result of the global

financial crisis in 2008-2009. Nevertheless due to its growth strategy which maintained the

competitiveness of Argentine products and therefore allowed for sizable reserve accumulation,

Argentina confronted this crisis with much greater ability and space to implement countercyclical

policy (Pastana et al., 2012). As a result rises in unemployment were nowhere near the

magnitude of that experienced in the 2001 crisis.

1.4 CONCLUSION

The chart below presents the averages for the real exchange rate level and GDP growth in

the two periods discussed. On average GDP growth was much greater in the post-Convertibility

regime, which was facilitated by a depreciated real exchange rate. This boosted the

competitiveness of the tradable sector and allowed for greater employment generation.

Convertibility Post-Convertibility1995-2001 2002-2010

RER 1.331 2.314GDP growth 0.896 5.576

Overall, the economic regime in the 90s with its single focus on nominal stability and full

reliance on market mechanisms to promote growth and employment (rapid trade opening,

currency appreciation and structural reforms) negatively affected job creation. Employment

generation stagnated even before the first recession in 1994. The manufacturing industry was hit

particularly hard as the change in relative prices due to the appreciated RER prompted a

substitution of labor with capital, leading to the destruction of many jobs and firms. The deep

crisis in 2001 caused a severe contraction in the level of domestic activity, with lasting negative

2011).

20

impacts on employment and income. In contrast, 3 million jobs have been created since 2003 and

2010. The maintenance of the SCRER has led to the expansion of the whole tradable sector,

turning the domestic/internal market into engine of growth.5,6 As a result the economy has

exhibited high GDP growth rates and an elastic labor demand. Positive trade balances have led to

the accumulation of voluminous amounts of reserves and a reversal of current account deficits to

surpluses, thereby providing greater room for countercyclical policies that have served to mitigate

the transmission of external shocks to the labor market. Nevertheless, concerns over inflation

have prompted many to argue that the macroeconomic regime has been losing coherence since

2006. Due to the global financial crisis in 2008 and the recent RER appreciation trend,

employment generation in the late 2000s has lost its former dynamism. Still in terms of

employment generation, the macroeconomic setting in the 2000s remains a marked improvement

over the cyclical boom-and-bust nature of employment generation in the 1990s.

5 Other important factors commonly cited in explaining Argentina’s recovery include the renegotiation of the debt and the re-institutionalization of labor policies. 76% of the debt was accepted in default, which reduced the public debt to US$ 63,700 million, while 40% of new titles were expressed in national money which was significantly more favorable to Argentina given the depreciation. By the end of 2005, Argentina was able to play the full debt of US$ 10,000 million to the IMF (Maurizio et al, 2009). Labor policies such as the minimum wage, and collective bargaining facilitated an improvement in the income distribution, which was a key factor in the growth of domestic demand (Ministerio de Trabajo, Empleo y Seguridad Social, 2011).

6 While commodity prices certainly played a role in Argentina’s dynamic growth, Frenkel and Rapetti (2012) argue it was the expansion of the whole tradable sector, facilitated by the SCRER, that put the economy on the path towards rapid growth. Maurizio et al. concur that the initial recovery was demand led (i.e. due to domestic factors), which was reinforced by an increase in export prices and a decrease in international interest rates.

21

2.1 URBAN FACTORS IN EMPLOYMENT GENERATION

While the macroeconomic regime is of fundamental importance, this paper argues that it

is not the only factor. Rather, employment generation is also contingent upon urban factors such

as size and density. This is evidenced by the heterogeneity of employment rates in various cities

as illustrated in the graph below.

Ideally, this graph would show elasticities of employment as employment rates show a static

picture rather than a dynamic ability of larger cities to generate more employment under a given

macroeconomic regime. Nevertheless, elasticity of employment graphs by city size when not

estimated econometrically include much noise such as widespread privatizations in the 1990s as

well as the effects of other

variables such as the

production structure. Still,

in a snapshot view presented

in the accompanying scatter

plot, the positive relationship

between size and elasticity

of employment is noticeable.

22

2.1.1 SIZE

Why is it that larger cities have shown higher levels of employment? The benefits of city

size stem from economies of agglomeration. This is the notion that “spatial proximity of

activities makes resources more efficient than if such activities are spatially dispersed” (Goldstein

& Gronberg, 1984); this entails an outward shift in the production function as industry size

increases. Unlike economies of scale which are internal to the firm (large fixed costs and smaller

variable costs that create increasing returns to scale at the firm level), agglomeration economies

have been termed “external economies of scale” and are most relevant to this discussion of

employment. Quigley (1998) explains, “The economies are external in the sense that the firm

obtains them from outsiders, and they are economies in the sense that the firm can satisfy its

variable or part-time needs in this manner more cheaply than it could satisfy them from within.

The outsider, in turn, can afford to cater to the firm’s fractional needs because he also caters to

many other firms.” Marshall is arguably the best known and most influential of the early analysts

of agglomeration and is credited with the exposition of the concept of external economies.

Nevertheless Smith (1776) can be credited with the first analysis of the benefits from

agglomeration, albeit with a more narrow argument focusing on the division of labor (Duranton

& Puga, 2003).

There are two types of external economies of scale: localization and urbanization

economies. Localization economies refer to economies of scale arising from the spatial

concentration of activity within industries; they are external to a firm at a given location but

internal to the industry at that location (Goldstein & Gronberg, 1984; Rosenthal & Strange,

2001). Urbanization economies accrue to a firm from the level of overall economic activity in the

area, and are therefore external to the firm and to the specific industry at a given location

(Nakamura, 1985). They reflect the benefits from operating in a large, urban environment in

which there is a large overall labor market, and a large service sector interacting with the

23

manufacturing sector (Henderson, 1986). Using case studies of Brazil and the US, Henderson

finds much stronger evidence of the productivity effects of localization economies than

urbanization ones, but nevertheless concludes that urbanization remain important for city growth

(Murphy et al., 1989 and Krugman, 1991 as cited in Glaeser, Kallal & Scheinkman, 1992).7 In

this study, I include density as a third factor (the first two being spatial concentration within an

industry and the overall level of economic activity in the area) which should facilitate greater

employment generation in urban areas. While similar to localization economies, the benefits of

density arise from the spatial concentration of people and activity within an area as opposed to

only within an industry. Density is defined here as the intensity of labor and capital relative to

physical space. The following section outlines the specific links between localization and

urbanization economies, and density on employment generation.

Localization economies

Examples of localization economies include increased communication between firms,

decreased search costs, greater specialization, and scale in public intermediate inputs (Henderson,

1986). With localization economies, firms benefit from a closer flow of information to suppliers

and a proximity to final customers, therefore choosing to locate in a city in part because demand

is higher there (Dumais, Ellison & Glaeser, 2002). It should be noted that while producers prefer

to concentrate to economize on transport costs and prefer production sites that are close to large

markets, markets are large precisely where large numbers of producers have chosen to site their

facilities. Thus the location of production and location of demand are interdependent (Krugman,

1991), which suggests self-sustaining growth past a certain threshold.

A larger labor market reduces search costs and increases the probability of matches

between differentiated skilled workers and differentiated job requirements (Hesley & Strange,

7 The existence and considerable magnitude of localized scale externalities is well documented empirically, see Henderson, 1988; Ciccone and Hall, 1995; and Glaeser et al, 1992, as cited in Henderson, 2000.

24

1990). Concentration of several firms in a single location offers a pooled labor market for

workers with industry specific skills (Krugman, 1991).8 This ready availability of workers for

firms alleviates hold-up problems, as asset specificity is less likely to be an issue where the

number of potential partners is large (Duranton & Puga, 2003). Proximity to specialized labor

such as workers in accounting, law, advertising, and other technical fields, can also reduce costs

for businesses (Quigley, 1998).

In addition, greater industry size permits greater specialization among firms (Hesley &

Strange, 1990). This is illustrated by Adam Smith’s famous pin factory example in which an

expansion in a firm’s workforce increases output more than proportionately because it allows

existing workers to specialize. There are resulting productivity gains due to workers’ increased

dexterity at a particular task, savings on fixed costs by not having workers switch tasks, and

labor-saving innovations made possible by simpler tasks that can more easily be mechanized

(Duranton & Puga, 2003). Given that the division of labor is limited by the extent of the market,

which is determined by transportation efficiency, cities are the “natural” market where division of

labor takes place (Duranton & Jayet, 2011).

Concentrating industrialization also allows the economy to conserve on “economic

infrastructure” that can be shared among firms. Examples include physical infrastructure capital

such as transport and telecommunication – inputs that are not costlessly mobile – and managerial

resources (Henderson, 2000; Glaeser, Kallal & Scheinkman, 1992). The ability to share

transportation infrastructure is able to transform production with constant returns to scale to one

with increasing returns to scale via transport costs that rise with distance. Firms therefore save on

transport costs both due to proximity to input suppliers, and due to the ability to sell some of their

output without incurring transport costs.

8 As agglomeration economies improve the expected quality of matches between job requirements and skills of workers for all workers, there is a positive ex-ante relationship between wages, productivity, and city size (Hesley & Strange, 1990).

25

Urbanization economies

As previously mentioned, urbanization economies reflect the benefits of operating in a

large, urban environment. These are particularly relevant as giving the rise in large cities, as

illustrated by the accompanying map. Currently, one in three Latin Americans lives in a city of 1

million or more (CEPAL, 2012).

Large markets facilitate the existence of highly specialized products. As Adam Smith

initially explained using the example of a porter, there are various specialized businesses and

consumer services for which the per business or per capita demand is so small that a large city is

needed to support even a few suppliers (Mills, 1967). Thus, not only are there gains from

narrower specialization sustained by larger production, but there are also gains from a wider

variety of input suppliers that can be sustained by a larger final-goods industry (Duranton &

Puga, 2003). In general it is found that a higher degree of differentiated intermediate inputs leads

26

to a higher productivity in the production of final goods (Ciccone & Hall, 1993). Thus large

cities provide public intermediate inputs that can be tailored to the needs of a particular industry

(Hesley & Strange, 1990), such as specialized local producer services like repair and

maintenance, legal support, transportation, and financial resources (Abdel-Rahman & Fujita,

2006).

Additionally, large urban markets are likely to smooth employment fluctuations, giving

way to more stable aggregate level employment.

Mills argues that the most important aspect of agglomerative economies “is statistical in nature, and is an application of the law of large numbers. Sales of output and purchases of inputs fluctuate in many firms and industries for random, seasonal, cyclical, and secular reasons. To the extent that fluctuations are imperfectly correlated among employers, an urban area with many employers can provide more nearly full employment of its labor force than can an urban area with few employers” (Mills, 1967 cited in Goldestin & Gronberg, 1984).

Thus, employment can be stabilized as some firms are hiring while others are not. This extends

to sales of output as well meaning firms can carry less inventory (Quigely, 1998), which amounts

to real savings for businesses. Those producing specialized products subject to widely fluctuating

demand but more stable industry demand will also benefit from agglomeration (Glaeser, Kallal &

Scheinkman, 1992).

Density

Arguably the most important benefit from density relates to the diffusion of knowledge.

Knowledge spillovers are particularly effective in cities as communication between people is

more extensive. Jacobs argued that the cramming of individuals, occupations, and industries into

close quarters provides an environment in which ideas flow quickly between people, and that

these interactions between people in cities facilitate learning and innovation (Glaeser, Kallal &

Scheinkman, 1992). With respect to firms, the greater “communication” enables the adoption of

new technologies/innovations. These informational spillovers therefore give clustered firms a

27

better production function than isolated producers (Krugman, 1991).

While innovation has been found to be lower in low-dense areas (Carlino, Chatterjee &

Hunt, 2006 ), various studies have illustrated a positive link between innovation and employment.

Novick et al. (2009), Roitter, Erbes & Trajtenberg (2010), and Robert et al. (2010) as cited in

Barletta et al. (2012) have found that those firms which have generated the most employment in

the upward phase of the cycle and lost the least in downswings were firms with high levels of

technology and organization capacity. Barletta et al. (2012) confirms these results, finding that

firms with complex innovation strategies and links to other firms and services with the goal of

developing technical and organizational capacity have had a superior performance in terms of

employment. Innovative activities are defined by Schumpeter as those relating to the creation

and improvement of products and processes, and organizational change (Novick et al, 2009 as

cited in Barletta, Castillo & Yoguel, 2012).

In summary, agglomeration economies arising from size and density engender a plethora

of benefits: reduced search costs, greater specialization, scale in intermediate inputs, more stable

aggregate employment, and increased knowledge and innovation. All of these suggest that size

and density give larger cities an increased ability to generate employment under a given

macroeconomic regime. However, it is worth noting that there is not a one-size-fits-all optimal

city size. Rather, the efficient city size depends on the particular set of interrelated activities and

goods produced within its bounds, otherwise various city-sizes would not exist in a market

economy (Goldstein & Gronberg, 1984). As Henderson (1986) explains, “if these goods involve

different degrees of scale economies, cities will be of different sizes because they can support

different levels of commuting and congestion costs.” In general, the equilibrium city size will

increase as the degree of localization economies of the industry the city is specialized in increase,

nevertheless there is a limit to benefits from size as will be discussed in the policy implications

section.

28

3.1 PRODUCTION STRUCTURE AS A DETERMINANT OF EMPLLOYMENT

GENERATION

The production structure also has important consequences on employment generation. In

general, regions with a lower productive diversification are those that registered a larger negative

impact in terms of production and employment in the recessive phase at the end of the 1990s and

a slower recovery in the subsequent upwards cycle (Tomada, 2007). The role of the production

structure has predominantly been the domain of structuralist economics (Hirschmann, Rosentein-

Rodan, Gerschenkron, Chenery y Sirquin) which argues that development implies the

reassignment of factors (capital and labor) from low-productivity sectors to high-productivity

sectors, leading to productive convergence. Productive structure heterogeneity therefore refers to

large productivity differences both between and within economic sectors. This produces a

segmentation of the production system and labor markets, with asymmetrical technological

conditions and wages (Porcile, 2011). Structural heterogeneity stems from a variety of factors,

including unequal access to technology, the structural organization of firms, the nature of the

product's demand, and unionization (Cimolo, Novick & Palomino, 2007). Prebisch famously

outlined the problem of slow and unequal technological diffusion, in which technological

progress is mainly absorbed only by sectors related to exports while other sectors remain

unchanged (Porcile, 2011). Unfortunately many sectors with high productivity such as those

related to natural resources (ex. copper) account for a relatively small quantity of total

employment. Moreover, the expansion of natural resource production generates limited positive

externalities given the large reliance on imported products, thus presenting less opportunities for

contracting out, learning processes, etc.

Structural heterogeneity reflects negatively on employment generation. With high levels

of structural heterogeneity, externalities and positive spillovers cannot be as widespread given the

absence of linkages between sectors. Investment levels also tend to be low and incentives for

29

productivity are weak. Moreover, economies with high levels of structural heterogeneity have a

lower capacity to respond to demand changes via diminished innovation. This implies a loss of

market share and less competitive exports, which taken farther, can pose risks for exchange rate

crises due to worsening of the trade balance, and can lead to more volatile growth patterns

(CEPAL, 2010). Moreover, structural heterogeneity consequently leads to the creation of

“enclave sectors” in which only the formal sector benefits, as it bases its modernization on the

adoption of innovations from the exterior thereby inhibiting endogenous development of

technological capacity. With this production structure, productivity increases do not lead to

higher wages and greater employment, but rather lead to the expulsion of workers into

informality.

Over the past 50 years, the high productivity share of firms has increased its contribution

weight to GDP without a corresponding increase in employment. Due to the insufficient growth

of the intermediate strata in terms of production and employment, the majority of the labor force

has been absorbed by lower productivity sectors, which register a negligible contribution to GDP

(Porcile, 2011). Importantly, structural heterogeneity is reproduced even under conditions of

rapid economic growth as high-productivity firms drive the economy (Infante & Sunkel, 2009).

Crises are thus likely to hit hardest on the low-productivity sectors, which do not greatly

influence growth yet absorb the bulk of the workforce, causing rises in unemployment and

informality.

30

4.1 INFRASTRUCTURE

Finally, it is worth spending a moment on infrastructure. While the exact impact of

infrastructure on employment is hard to measure, this paper argues that infrastructure is likely to

act as “grease on the wheels,” facilitating the benefits of size, density, and a complex production

structure on employment generation. Infrastructure’s relationship with employment is therefore

mediated through its ability to elevate productivity and lower production costs. I use

infrastructure in this paper to refer to “economic infrastructure,” namely public services (i.e.

energy, telecommunications, water and sanitation, solid waste management), public works (i.e.

highways, drainage, etc.), and transportation (i.e. railways, urban transportation systems, ports,

airports).

With respect to urban factors, infrastructure can play an important role in the realization

of benefits of agglomeration economies. For example the benefits of shared transport costs

cannot be realized without an adequate highway and/or railway system. Increased

telecommunications infrastructure decreases search costs and increases the flow of information.

In general, increased infrastructure provision decreases costs for businesses which facilitates

increases in employment. With respect to the productive structure, a lack of urban services and

spatial segregation can give way to vicious cycles of production segregation in which large

proportions of the population remain in areas of low productivity. Low productivity sectors have

a harder time innovating, adopting new technology, and inciting learning processes (CEPAL,

2010). The presence of low productivity sectors then feeds back negatively on infrastructure

provision as the concentration of informal (or less dynamic) activities in low-income areas

reduces the land value which reduces the income from taxes that the municipal government

receives, thereby limiting their ability to provide public services and invest in infrastructure

(CEPAL, 2010). In a more direct relationship, the formation of physical capital generates

employment in the short-term. Moreover benefits extend to the long-term as well, as increased

31

capital formation provides/permits permanent occupations in the higher level of production of

goods and services it sustains. In this way, infrastructure provision may act as a way of reducing

structural heterogeneity.

While infrastructure may play the role of a helping hand in economic growth, it is also

likely to be an effect of increased economic growth as increased tax revenues allow for the

realization of more infrastructure projects.9 According to the 1994 World Development Report, a

one percent increase in infrastructure capital is associated with a one percent increase in GDP

(World Bank, 1994). For example, a quick look at water access data for the period 2003 to 2010

as measured by the EPH household databases (access to an in-house water source),10 shows that

all cities with the exceptions of Gran Parana, Rio Gallegos and Ushuaia-Rio Grande exhibit

improvements in water access. The decreases in these three cases are minimal: 1.1% or less.

However such positive trends are to be expected given the high levels of GDP growth and

employment growth experienced in the 2000s. Given that increases in infrastructure provision

are likely to occur in times of economic growth, and economic growth is often correlated with

employment generation, it is hard to clarify/distill out the relationship between infrastructure and

employment. Furthermore, infrastructure is difficult to measure. While some inventory stock

data exists - such kilometers of roads, number of airports and ports, etc. – the benefits of

infrastructure are not always realized in the same area where the infrastructure is located. A

bridge for example may have a larger impact on the towns they connect, rather than the area in

which it was built. Due to the complexity, multidirectionality, and measurement difficulty of

infrastructure, this study recognizes the indirect effects of infrastructure via the production

9 Nevertheless, even in periods of growth in the 1990s, infrastructure spending was notoriously low due to the fiscal discipline imposed by the IMF. Moreover, the widespread privatizations weakened the national government’s capacity to provide basic services. During the most vigilant Washington Consensus years, the investment rate was near the lowest rate registered in the lost decade of the 1980s (Ffrench-Davis, 2011).

10 As this question is only included in the household, not the individual survey, water access data is not included in the micro model estimation of the probability of being employed.

32

structure, growth, and urban factors but does not include infrastructure as a direct variable in the

models that follow. In general, such linkages between infrastructure and employment are

complex and difficult to trace, nevertheless they are an important area of future research.

33

5.1 ECONOMETRIC MODELS

This study uses two models to measure the respective weights of macroeconomic

regimes, the production structure, size, and density on employment. The first model is a “macro”

level model which estimates aggregate labor demand. The second model is a “micro” level

model which estimates the probability of being employed. The following section presents the

design and results of each of the two models.

5.1.1 Macro model

The macro model uses time series data to estimate aggregate labor demand at they city

level. The number of employed workers is used as the dependent variable, and the explanatory

(independent) variables include gross domestic product, size, complexity, density, wages, and

region. The model is run twice, once for the period October 1995 to May 2001, and the second

for the period 2003 to 2010. This distinction is necessitated by the methodological change in the

household survey (EPH) in 2003, yet is also useful given the very distinct nature of the two

macroeconomic regimes.

5.1.2 Variable descriptions

NUMBER OF EMPLOYED PERSONS

This variable was calculated using EPH city-level estimates of the total number of

employed people. After re-categorizing people participating in employment plans as

unemployed, employment rates were re-calculated and then multiplied by the population

estimates to get the total number of employed workers. Quarterly population estimates were

calculated using the 2010 4th quarter urban agglomeration population totals from the EPH

database, and applying the provincial population growth rate published by the census agency (one

growth rate for 2010-2001, and a second from 2000-1995). While an imperfect measure of

34

population as provincial growth rates may differ from city growth rates, it was more important to

avoid short-term fluctuations present in the EPH quarterly population data stemming from the

expansion of sample results to population estimates. This variable enters into the regression in

log form to measure the change in employment over time. As with all variables based on data

from the EPH, data reflects October and May values for the period 1995 to 2001, and quarterly

values from 2003 to 2010.

TAX COLLECTION

Ideally gross domestic product would be used to proxy the effects of the macroeconomic

regime. However, such a measure is not available on a city nor provincial level for the entire

duration of the series. National GDP cannot be used in this regression given that it is attempting

to account for differences between cities (thus putting the same values for all cities will explain

little). Total tax collection by province is used instead. Gross geographic product does exist by

province until 2005 which

allows a comparison of gross

geographic product with the

tax collection proxy. When

tested in the 1995-2001

regression, the coefficient for

tax collection is only 0.03

lower than that of gross

geographic product and the

adjusted R-square change

falls minimally (by 0.0017).

This shows that tax

collection is an excellent

35

proxy of GDP. This variable enters into the regression equation in log form to reflect growth

rather than level effects.

SIZE

Argentina has a very unequal distribution of city size, as illustrated by the graph on the

previous page. Apart from the metropolitan area of Buenos Aires, most cities in Argentina are on

the order of 300,000 to 400,000 inhabitants. Population estimates are taken from the 4th quarter

of the 2010 EPH.

Size enters the regression as a dichotomous variable, reflecting large, medium, and small

cities. To create the variable, I divide the urban agglomerations into three categories based on a

“cumulative percent approach” in which category division lines are drawn at 80, 90 and 100

percent11. This is illustrated below. Such an approach illustrates how the population is

distributed. In other words, out of every 80 people that are born, 80 will reside in large cities, 10

will reside in medium ones, and 10 in small ones. Appendix 1 presents more detailed information.

The resulting categorization is as follows:

11 An exception is made for Sante Fe, which is taken in the large city category due to intuition, given that it is the capital city of the province.

36

Large: Área metropolitana de Buenos Aires, Gran Córdoba, Gran Rosario, Gran Mendoza, Gran Tucumán-Tafí Viejo, Gran La Plata, Mar del Plata-Batán, Salta, Gran Santa Fe

Medium: Gran San Juan, Gran Resistencia, Santiago del Estero-La Banda, Corrientes, Bahía Blanca-Cerri, Jujuy-Palpalá

Small: Posadas, Gran Paraná, Neuquén-Plottier, Formosa, Gran Catamarca, San Luis-El Chorrillo, La Rioja, Río Cuarto, Concordia, Comodoro Rivadavia-Rada Tilly, Ushuaia-Río Grande, Santa Rosa-Toay, Río Gallegos

While some may argue that there are only three truly large cities in Argentina (AMBA,

Córdoba and Rosario), including more cities in this first group is important for the regression.

With only three large cities, density as a dichotomous variable becomes highly correlated with the

size dichotomous variables. With nine large cities, there exists heterogeneity with respect to

density within the category, thereby allowing the model to differentiate between the effects of size

and density.

DENSITY

The series on density data is unique to this study as there are no series published by

INDEC which measure density on a city level. To create this series, I calculated size in

kilometers squared of each city by summing the corresponding “departamento” area data

published on the 1991 census page and divided them by quarterly population estimates

(calculated as explained in the number of employed section), to give a measure of inhabitants per

kilometer squared. This is then converted into a dichotomous variable by giving the value of 1 to

cities in which density is greater or equal to the median density for that quarter or year, and 0 if it

is below. Appendix 2 presents density in the year 2005 ranking cities from most to least dense.

COMPLEXITY

To measure the effects of production structure, a complexity indicator was constructed

based on the proportion of city-level employment in sectors with high value added compared to

national employment in such sectors. This indicator not only shows which cities have a more

37

complex production structure than others, but also how each city compares to the national

average as cities with a complexity indicator value greater than 1 are more complex than the

national average. This variable is not constructed as a time series, but rather constructed on the

basis of 2005 data. This is not problematic though as the production structure is not something

that undergoes short-term fluctuations as evidenced by the persistence of structural heterogeneity

over the past 50 years. Employment data comes from the year 2005 as it is a relatively neutral

year, avoiding potential distortionary effects of the crisis years and widespread privatizations in

the 1990s. Appendix 3 presents the equation and indicator values.

REGION

Regional dummy variables are included to control for inter-regional differences based on

the relationship between sectoral production and macroeconomic regime. Agricultural,

manufacturing, and tourism occur in relatively distinct parts of the country, thus it is important to

recognize that which goods and services are favored by the macroeconomic regime and external

conditions (such as high commodity prices) will partly explain different levels of employment

generation. The regions are: Gran Buenos Aires (1), Cuyo (2), Noreste (3), Noroeste (4),

Pampeana (5) and Patagónica (6).

MODEL LIMITATIONS: Excluded Employment Determinants

This model does not seek to incorporate all determinants of employment. Many previous

studies have been embarked on exploratory analyses of the effect of the size of firms - finding the

while small and medium enterprises are fundamental in explaining employment generation, the

majority of employment remains explained by large firms12 - and sector - finding that

employment is inversely related with primary goods production. Local policies also play a key

role in explaining heterogeneous city employment rates (Rafeala is a prime example of this).

12 For more see, Birch, D. L. (1981). Who Creates Jobs? The Public Interest,65 (Fall), 3-14.

38

While further discussion of these themes is outside the scope of this paper, it suffices to say that

some of the unexplained variance in the model is due to such excluded variables.13

The regression equation for the macro model is therefore:

Employed workers = α + β1*tax collection + β2*large + β3*medium + β4*complexity +

β5*density

5.1.3 Macro model results

In the 1990s macro model (1995-2001), all variables are significant at the 1% level

except for density which is statistically significant at the 5% level. The macroeconomic effect is

very important, with a 13% increase in tax collection associated with a 1% increase in

employment. In addition, size and complexity are both significant predictors of employment

generation. In large cities, aggregate labor demand is 65% higher than in small cities. Likewise,

employment demand is 28% higher in medium cities in comparison with small cities. Density

shows a positive coefficient, however its effect is markedly smaller than all other variables. The

model has a good fit, with an adjusted R2 value of 0.74, meaning that 74% of the variance in

number of employed workers is explained by the variables in the model.

13 This model also attempted to include a proxy of wages in order to avoid overestimating the impact of GDP. The proxy was created as follows. The average wage for each city was taken from the EPH microdata and then for each quarter or semester (1990s and 2000s model, respectively), divided by the average/median wage for that quarter or semester. Results greater than 1 were given the value of 1, and results less than 1 were given a value of 0. The creation of a dichotomous variable was important in order to avoid problems with inflation which would occur if the variable was used as a time-series. This was a better option than dividing each city-level wage by the median value calculated across all periods, because the large between-city differences and changes over time forced the majority of values in the first half of the series to be zero, and those in the second half to take the value of one; this would eliminate variation between cities within a given quarter or semester. Thus, this vertical calculation was chosen instead to give an idea of where wages were the highest.

Nevertheless, this measure had many limitations. Most importantly, businesses choose to contract labor where labor is cheap on a sectoral level. In other words, it does not matter if average wages are higher or lower in Bahia-Blanca than Mendoza, but rather how the wages compare for the specific sector the worker is in. Secondly, this estimate includes wages for the informal sector which biases the wage estimate downwards. This is a likely reason why wages and the complexity indicator were not collinear. In the end, wage proxy turns out to have near zero explanatory power and is therefore excluded from the model.

39

Due to collinearity between complexity and region, I ran a second regression including

regional dummies and eliminating the complexity indicator. This model has additional

explanatory power, with an adjusted R2 value of 0.92, however it is important to note that the

regional dummies likely hide other omitted variables. Size continues to be the most important

variable, followed by complexity, tax collection, and lastly, density. The size of the coefficients

for tax collection, size, complexity, and density are slightly reduced, however that is likely due to

some correlation with the regional dummies (i.e. some of their effect is captured instead by the

regional dummy). When Patagonia is taken as the base region, all regional dummies show a

positive value, meaning that employment in every other region is higher than employment in

Patagonia. Region 1 has the largest value, which is to be expected given high rates of

employment in Buenos Aires.

When the model is run for the 2000s (2003-2010), the only difference is that density

becomes significant at the 10% rather than the 5% level. Significance tables for all regressions

can be found in Appendix 4.

5.2.1 MICRO MODEL

The micro model estimates the probability of being employed to test the effects of

structural and urban determinants on employment while controlling for demographic

characteristics of workers. It should be noted that the findings in this section are very preliminary

and should be treated with caution. This model works directly with the EPH databases

(individuals).

5.2.2 Variable descriptions

The dependent variable in this model is a dichotomous one, “estadon,” which takes the

value of 1 for employed persons within the active age and 0 for unemployed persons within the

active age. The active age range is defined as 15 to 65 for men and 15 to 60 for women. The

40

independent variables include gender, education, age, and an interaction between gender and head

of household. Education enters into the regression equation as five dichotomous variables:

incomplete primary education, incomplete secondary education, completed secondary education,

incomplete tertiary education and completed tertiary education. Age is broken up into there

groups, 15-24, 25-49, and 50-65 (60 for women). The interaction between gender and head of

household is important women are more likely to work if they are the head of the household as

well. I also include a dummy variable for public sector employment, given the lower volatility of

the public sector, often used as a countercyclical mechanism. In addition to these demographic

variables, I include the variables size, complexity, and density defined the same way as in the

macro model. The regression equation is therefore:

Probability of being employed = α + β1*gender + β2*ip + β3*is + β4*cs + β5*it + β6*ct +

β7*age2 + β8*age3 + β9*gen*headhousehold + β10*large + β11*medium + β12*publicsector +

β13*density

5.2.3 Micro model results

As expected, all of the demographic variables are significant. Age is the most important

(a greater age meaning a greater probability of being employed), followed by gender, education,

and status as head of household. Production complexity continues to be a highly important

variable with respect to important, showing that a more complex production structure is

associated with a higher probability of being employed. Density in this case is statistically

significant. In contrast with the other model, the coefficient is negative. Although statistically

significant it appears to have little effect in reality given the very small magnitude of the

coefficient.

Both of the dichotomous size variables take a positive value, however only “large” not

“medium” is significant, meaning that once demographic characteristics are controlled for, the

41

benefits of size are not felt in medium-sized cities. This could suggest that the macro results

reflect different compositions of employment (larger cities having more people that have a higher

probability of being employed) rather than an endogenous capability of large cities to generate

employment. However, this is a very preliminary result and requires much additional testing.

The significance table for the second quarter of 2006 is presented in Appendix 4.

In general, the model has a good fit (R2 equals 0.45). However, it is important to mention

that the non-demographic variables add very little to the model in terms of explanatory power. In

the absence of size, complexity, and density, the R2 is only 0.0011 lower. Moreover, the

significance of the complexity and size variables greatly depend on the year used from the EPH.

Thus, the results of this model are not robust and should be taken as an invitation for future

research rather than as conclusive results.

42

6.1 POLICY IMPLICATIONS

Macroeconomic regimes

This study has illustrated the fundamental importance of macroeconomic regimes with

respect to employment generation. As evidenced by the 1990s, economic growth by itself is a

necessary but insufficient condition for employment generation. The overvalued peso during the

Convertibility plan reduced the competitiveness of Argentine exports, undermined the domestic

economy, and contributed to the vulnerability of the economy to external shocks, all over which

led to low levels of employment generation (Cohen, 2012). In contrast, the post-Convertibility

regime illustrates the positive effects of a stable and competitive real exchange rate which

boosted the profitability of tradable goods and incentivized industrialization, and was

accompanied by an array of social and labor policies which encouraged the formalization of

workers and supported those with low incomes. The exchange rate is arguably the most

important transmission mechanism between the macroeconomic model and the labor market, thus

it must be managed with care. The problem with the fixed exchange rate in the 1990s was not

necessarily that it was fixed (although that became problematic in that it prevented adjustment to

changing external circumstances incited by crises in Mexico and Russia and the devaluation in

Brazil), but rather that it was fixed at an appreciated rate. This gave way to a profound

deindustrialization as businesses substituted capital for labor and many manufacturing firms were

forced to shut down. The competitive real exchange rate on the other hand, in conjunction with

fiscal policies and social policies of redistribution, increased the profitability of the

manufacturing sector and turned the domestic market into the engine of growth.14 This reduced

volatility as the economy became less susceptible to external shocks, and the improving trade

balance via the increased competitiveness of exports allowed for a significant accumulation of

reserves which relaxed the balance of payment constraint and created more fiscal policy space to

14 A devalued exchange rate can also help reduce structural heterogeneity via an enhancement of the profitability of non-traditional tradable sectors.

43

stimulate demand, growth, and employment generation.

Structural heterogeneity

This study has also shown that a more complex production structure has positive effects

on employment. The reduction of structural heterogeneity implies a growth rate in parallel to

productivity growth which guarantees the creation of employment in the high productivity strata.

Such policies include those that increase education access, promote innovation and increase in

technological capabilities via research and development and production linkages that integrating

small and medium enterprises into production chains to incite a rise in productivity in the lower

strata. Industrial policies therefore play a key role in changing predominating patters by altering

prices so that they favor technologically intensive production – sectors most capable of bringing

forth a dynamic competitive advantage due to the presence of high externalities (CEPAL, 2010:

Abeles, 2012). Infrastructure provision also plays a key role in ensuring that the growth of

capacity to provide goods and services. Nevertheless, it must be recognized that structural

change is a process which takes time and requires a high level of coordination between various

actors. Implementing policies in just one area can serve to reinforce rather than reduce productive

heterogeneity.

Size: Industrial policies and regional spatial planning

This study has shown a positive relationship between city size and employment,

illustrating that larger cities have a positive endogenous quality which allows them to generate

more employment under a given macroeconomic regimes. Large, dense urban areas (i.e.

concentration of population and productive activities) reduces costs for businesses, increase the

profitability of investment, encourage knowledge spillovers, and promote the division of labor

(CEPAL, 2012). The policy implication is thus the necessary use of industrial policies in smaller

cities in order to artificially create the benefits afforded by economies of agglomeration in larger

44

cities. Such policies will allow smaller cities to reap a greater employment benefit from

economic growth on the national level.

While there are many benefits of large and dense urban areas, it is important to recognize

that there can exist too much of a good thing.15 Large populations, extensive geography, and

intensive productive activities, all characteristic of large cities, lead to congestion costs, the

generation of increasingly large quantities of waste, the rising cost of territorial management, and

rising prices of buildable land. With larger cities, failures in institutional coordination and

strategic metropolitan governance (structural and functioning inefficiencies) become more

common (Prud’homme et al., 2004). CEPAL (2012) terms the costs of large cities “urban

deficits”, which relate to the themes of housing, infrastructure, public services, transportation, the

environment, security and city management, among others. With respect to employment

specifically, increased migration to cities can lead to an increase in informality if this growth in

the labor force is not accompanied by a growth in available positions. According to CEPAL

(2012), urban deficits result from the historical inability to absorb productively, coherently and

with dignity the rapid growth of the population, the combination of a limited surface area and

intense activity of the cities, scarce and unequally allocated resources, weak urban institutions,

the absence of a strategic vision and the lack of technical and administrative tools for designing

and applying appropriate city policies.

Framed in terms of a cost benefit analysis, Quigley (1998) shows that as city size and the

area devoted to housing increase spatially, the average distance a worker must commute to get to

the central business district for work necessarily increases, as does congestion. Thus, increased

costs arising from higher rents due to competition for space and higher commuting costs due to

more distant residences will eventually offset the production and consumption advantages of

15 Williamson 1965, Wheeton and Shishido 1981, and Henderson 2000 (cited in CEPAL, 2012) have found an inverted U-shape relationship between economic development and urban concentration, finding that economic growth drives urban concentration until a saturation point at a distinct per capital income level, after which urban concentration begins to fall.

45

large cities.

In both cases there appears a saturation point past which increasing per person resource

costs negate the resource savings due to scale economies in good production, or more broadly,

that the costs of congestion and other urban deficits outweigh the benefits of size. Henderson

(2000) finds very robust growth losses from significantly non-optimal concentration, listing

Argentina as having excessive primacy.

This implies a large role for regional spatial planning in incentivizing the growth of

medium sized cities, of which there is an absence as shown initially with the distribution of

Argentine cities. The benefits of the creation of medium sized cities are two-fold. One the one

hand, it would reduce migration to the already saturated cities, thereby slowing the deepening of

urban deficits. On the other hand, it would allow other cities to surpass the necessary threshold of

size and density, beyond which they can reap the benefits of agglomeration. Regional spatial

planning also relates to the coordination of production, given that all cities cannot specialize in

the same thing. Rather, there needs to be some oversight with respect to which industries to

subsidize and where. This leads into the first area of future research, namely the need for work

on specific industrial policies that can be used in small cities to help artificially create the benefits

of large cities. Case studies on policies used in cities whose populations have doubled, tripled, or

quadrupled in the past two decades will be of key importance as they can help specify the

mechanisms of transmission between policies and their effects on city size.

The second area of future research relates to thresholds, beyond which the benefits of

agglomeration economies are realized. Thresholds will depend on a variety of factors, ranging

from the goods produced, proximity to other cities, relationship with the hinterland, provision of

infrastructure, social protection nets, among other things. While it is clear that there is not a one-

size-fits-all threshold, research on the different factors will help give a better idea of how and

46

when to promote such growth in the size of cities in order to realize the benefits of the large

cities, without the costs of megalopolises. Such research will ensure that the theoretical benefits

of size and density do not remain that way, but rather bear fruit in reality, generating employment

and improving the lives of the population.

47

Bibliography

Abdel-Rahman, H., & Fujita, M. (2006). Product variety, Marshallian externalities, and city sizes. Journal of Regional Science, 30 (2), 165-183.

Abeles, M. (2012). Argentina and the 2008-09 international economic crisis: Unconventional resilience. In Cohen, M. (Ed.), The global economic crisis in Latin America: Impacts and responses (59-84). New York: Routledge Studies in the Modern World Economy.

Buckley, R. (2012). The Economics of Cities: Feb. 1, 2012 [PowerPoint Slides].

Carlino, G.., Satyajit, C. & Hunt, R. (2006). Urban density and the rate of invention (Working Paper 04/16-R). Federal Reserve Bank of Philadelphia.

CEPAL. (2012). Población, territorio y desarrollo sostenible. Naciones Unidas: Santiago.

CEPAL (2010). La hora de la igualdad: Brechas por cerrar, caminos por abrir. Naciones Unidas: Santiago.

Ciccone, A. & Hall, R. (1993) Productivity and density of economic activity (Working Paper No. 4313). National Bureau of Economic Research.

Cimoli, M., Novick, M & Palomino, H. (2007). Introducción: Estudios estratégicos sobre el trabajo y el empleo para la formulación de políticas. In Novick, M. & Palomino, H. (Cds). Estructura productive y empleo: Un enfoque transversal. Ministerio de Trabajo, Empleo y Seguridad Social: Buenos Aires.

Cohen, M. (2012). Argentina’s economic growth and recovery: The economy in a time of default. New York: Routledge Studies in the Modern World Economy.

Damill, D., & Frenkel, R. (2011). Macroeconomic policies and performance in Latin America 1990-2010 (Centro de Estudios de Estado y Sociedad). Retrieved from http://www.itf.org.ar/pdf/documentos/84-2011.pdf

Damill, M., R. Frenkel & R. Maurizio (2011). “Macroeconomic policy for full and productive employment and decent work for all. An analysis of the Argentine experience”, Employment Working Paper Series No. 109, OIT, Geneva.

Dumais, G., Ellison, G. & Glaeser, E. (2002). Geographic concentration as a dynamic process. The Review of Economics and Statistics, 84 (2), 193-204.

Duranton, G., & Jayet, H. (2011). Is the division of labour limited by the extent of the market? Evidence from French cities. Journal of Urban Economics, 69 (1), 56-71.

Duranton, G. & Puga, D. (2003). Micro-foundations of urban agglomeration economies (Working Paper No. 9931). Retrieved from National Bureau of Economic Research website: http://www.nber.org/papers/w9931

Ffrench-Davis, R. (2011). Macroeconomia para el empleo decente en America Latina y el Caribe. (Employment Working Paper No. 79). Retrieved from Organizacion Internacional del Trabajo website: http://www.ilo.org/employment/about/news/WCMS_156123/lang--es/index.htm

48

Frenkel, R., & Rapetti, M. (2012). Exchange rate regimes in the major Latin American countries since the 1950s: Lessons from history. Revista de Historia Economica (Second Series), 30, 157-188. doi:10.1017/S0212610911000292 

Frenkel, R. & L. Taylor (2006). “Real Exchange Rate, Monetary Policy, and Employment”, DESA Working Paper No. 19, United Nations, Department of Economic and Social Affairs.

Glaeser, E. L., Kallal, H. D.,& Scheinkman, J. A. (1992). Growth in cities. Journal of Political Economy, 100 (6), 1126-1152.

Golstein, G.. S. & Gronberg T. J. (1984). Economies of scope and economies of agglomeration. Journal of Urban Economics, 16 (1), 91-104.

Henderson, J. V. (2000). How urban concentration affects economic growth (Working Paper No. 2326). The World Bank Policy Research Working Paper Series.

Henderson, J. V. (1986). Efficiency of resource usage and city size. Journal of Urban Economics, 19, 47-70.

Hesley, R. W. & Strange, W. C. (1990). Matching and agglomeration economies in a system of cities. Regional Science and Urban Economics, 20 (2), 189-212.

Infante, R. B. & Sunkel, O. (2009) Chile: Towards inclusive development. CEPAL Review, 97. Retrieved from http://www.eclac.cl/publicaciones/xml/0/36660/RVI97InfanteSunkel.pdf.

Krugman, P. (1991). Increasing returns and economic geography. Journal of Political Economy, 99 (3), 483-499.

Krugman, P. (1991). Cities in space: three simple models (Working Paper No. 3607). National Bureau of Economic Research.

Mazorra, X. & Beccaria, A. (2007). Especialización productiva y empleo en Areas Económicas Locales. In Novick, M. & Palomino, H. (Cds). Estructura productive y empleo: Un enfoque transversal. Ministerio de Trabajo, Empleo y Seguridad Social: Buenos Aires.

Maurizio, R. (2009). Macroeconomic regime, trade openness, unemployment and inequality: The Argentine experience (Working Paper No. 03/2009). Retrieved from International Development Economics Associates (IDEAs) website: http://www.networkideas.org/working/apr2009/wp23_03_2009.htm

Maurizio, R., Perrot, B., & Villafañe, S. (2009). Políticas públicas y empleo: Desafíos y oportunidades en una economía global. In OIT-Ministerio de Trabajo, Empleo y Seguridad Social, Informe nacional sobre el impacto Social de la Globalización en Argentina (chapter 2). Buenos Aires: Argentina.

Mills, E. S. (1967). An aggregative model of resource allocation in a metropolitan area. The American Economic Review, 57 (2), 197-210. Retrieved from http://www.jstor.org/stable/1821621.

Ministerio de Trabajo, Empleo y Seguridad Social (2011). Trabajo, ocupación y empleo: La complejidad del empleo, la protección social, y las relaciones laborales, Serie de Estudios/10.

49

Nakamura, R. (1985). Agglomeration economies in urban manufacturing industries: A case of Japanese cities. Journal of Urban Economics, 17 (1), 108-124.

Pastrana, F., Toledo, F., & Villafañe, S. (2012). El modelo económico ante la crisis internacional sostenimiento de las condiciones laborales y sociales en Argentina. In Macroeconomía, empleo e ingresos: debates y políticas en Argentina frente a la crisis internacional 2008-2009 (63-108). Ministerio de Trabajo, Empleo y Seguridad Social and Organización Internacional del Trabajo. Argentina.

Porcile, G. (2011). La teoría estructuralista del desarrollo. In Infante, R. (Ed.) El desarrollo inclusivo en América Latina y el Caribe: Ensayos sobre políticas de convergencia productiva para la igualdad. CEPAL, Naciones Unidas: Santiago.

Prud’homme, R., Huntzinger, H., & Kopp, P. (2004). Stronger Municipalities for Stronger Cities in Argentina. Inter-American Development Bank.

Quigley, J. M. (1998). Urban diversity and economic growth. Journal of Economic Perspectives, 12 (2), 127-138.

Rosenthal S., & Strange, W. C. (2001). The determinants of agglomeration. Journal of Urban Economics, 50, 191-229.

Tomada, C. (2007). Prólogo. In Novick, M. & Palomino, H. (Cds). Estructura productive y empleo: Un enfoque transversal. Ministerio de Trabajo, Empleo y Seguridad Social: Buenos Aires.

World Bank. (1994). Informe sobre el desarrollo mundial 1994: Resumen. World Bank: Washington D.C.

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Appendix 1: Size

Population Individual % Cumulative %LARGEPartidos del GBA 10,089,033 41.028% 41.028%Ciudad de Buenos Aires 2,995,050 12.180% 53.207%Gran Córdoba 1,410,132 5.734% 58.942%Gran Rosario 1,264,321 5.141% 64.083%Gran Mendoza 904,771 3.679% 67.762%Gran Tucumán - Tafí Viejo 814,915 3.314% 71.076%Gran La Plata 747,361 3.039% 74.115%Mar del Plata - Batán 620,095 2.522% 76.637%Salta 540,319 2.197% 78.834%Gran Santa Fe 507,908 2.065% 80.900%MEDIUMGran San Juan 470,562 1.914% 82.813%Gran Resistencia 391,962 1.594% 84.407%Santiago del Estero - La Banda 372,639 1.515% 85.922%Corrientes 360,255 1.465% 87.387%Bahía Blanca - Cerri 311,085 1.265% 88.653%Jujuy - Palpalá 310,756 1.264% 89.916%SMALLPosadas 300,200 1.221% 91.137%Gran Paraná 277,902 1.130% 92.267%Neuquén - Plottier 266,733 1.085% 93.352%Formosa 241,801 0.983% 94.335%Gran Catamarca 206,235 0.839% 95.174%San Luis - El Chorrillo 204,800 0.833% 96.007%La Rioja 183,413 0.746% 96.752%Río Cuarto 164,833 0.670% 97.423%Concordia 152,986 0.622% 98.045%Comodoro Rivadavia - Rada Tilly 143,669 0.584% 98.629%Ushuaia - Río Grande 123,193 0.501% 99.130%Santa Rosa - Toay 122,007 0.496% 99.626%Río Gallegos 91,911 0.374% 100.000%

51

Appendix 2: Density

DENSITY (2005)Inhabitants/km2

Ciudad de Buenos Aires 14431.97Mar del Plata - Batán 6416.69Partidos del GBA 2582.95Corrientes 694.65Gran La Plata 598.85Gran Córdoba 428.74Gran San Juan 345.17Gran Rosario 325.02Posadas 289.12Gran Tucumán - Tafí Viejo 238.94Gran Santa Fe 160.57Salta 156.70Gran Catamarca 130.02Bahía Blanca - Cerri 126.37Gran Resistencia 108.09Santiago del Estero - La Banda 62.26Gran Mendoza 58.30Gran Paraná 53.86Concordia 40.04Formosa 37.21Neuquén - Plottier 33.37Santa Rosa - Toay 15.46San Luis - El Chorrillo 14.27La Rioja 12.44Comodoro Rivadavia - Rada Tilly 9.13Río Cuarto 8.59Jujuy - Palpalá 8.13Ushuaia - Río Grande 5.02Río Gallegos 2.26

52

Appendix 3a: Complexity

Complexity Indicator

SAN LUIS - EL CHORRILLO 1.41357412

LA RIOJA 1.36355623

PARTIDOS DE GBA 1.33860146

GRAN ROSARIO (2) 1.19966323

GRAN CATAMARCA 1.12247919

USHUAIA - RIO GRANDE 1.10060513

GRAN MENDOZA 1.02785197

RIO CUARTO (5) 0.99544474

GRAN LA PLATA (1) 0.99515698

GRAN CORDOBA 0.97374744

CIUDAD DE BUENOS AIRES 0.92978562

MAR DEL PLATA, SAN NICOLAS Y BAHIA BLANCA (6) 0.89236583

RAWSON - TRELEW 0.88094691

GRAN SAN JUAN 0.86639664

CONCORDIA (4) 0.86046041

GRAN SANTA FE 0.83351074

GRAN PARANA 0.8221811

SALTA 0.73762679

CORRIENTES 0.72924316

POSADAS 0.71406286

GRAN TUCUMAN 0.70314884

GRAN RESISTENCIA 0.66743791

SGO DEL ESTERO 0.65776745

53

SANTA ROSA - TOAY 0.63547483

RIO GALLEGOS 0.62741332

CDRO RIVADAVIA (3) 0.59530381

NEUQUEN - PLOTTIER 0.57277489

FORMOSA 0.5646834

JUJUY 0.54404319

VIEDMA - CARMEN DE PATAGONES 0.41755434

Aclaraciones sobre algunos aglomerados

(1) Dato aproximado a partir del Empleo declarado en la Zona Fiscal denominada Tercer Cinturon del GBA

(2)Dato aproximado a partir del empleo declarado en la Zona fiscal del Resto de Santa Fe (Sin Ciudad de Santa Fe)(3)Dato aproximado a partir del empleo declarado en la Zona fiscal del Resto de la Provincia de Chubut (Sin Ciudad de Rawson y Trelew)

(4)Dato aproximado a partir del empleo declarado en la Zona fiscal del Resto de la Provincia de Entre Rios (Sin Ciudad de Parana)

(5)Dato aproximado a partir del empleo declarado en la Zona fiscal del Resto de la Provincia de Cordoba (Sin Ciudad de Córdoba)(6)Dato aproximado a partir del empleo declarado en la Zona fiscal del Resto de la Provincia de Buenos Aires (Sin Ciudad de Bs As, Partidos de GBA y Tercer Cordon de GBA)

Fuente: Observatorio de Empleo y Dinámica Empresarial, MTEySS en base a SIPAAppendix 3b: Complexity

Where X is registered employment, and

i = high value added sectors

j = city

Xij= workers in the high value added sectors in the specified city

Xj= total workers in the specified city

Xi= total workers in the high value added sectors in Argentina

X = total workers in Argentina

54

High value added sectors refer to the manufacturing industry, and the service sectors: transportation, communication, and financial intermediation.

55

Appendix 4: Significance Tables

MACRO MODEL

1995-2001

locupados

limpuestos 0.131

(5.20)**

grande 0.649

(14.52)**

mediana 0.278

(6.91)**

complejidad 0.298

(5.16)**

densidadd 0.079

(2.22)*

_cons 4.160

(62.75)**

R2 0.74

N 348

* p<0.05; ** p<0.01

locupados

limpuestos 0.064

(2.44)*

grande 0.613

(22.99)**

mediana 0.245

(9.62)**

complejidad 0.113

(2.94)**

region1 0.954

(19.84)**

region2 0.177

(4.88)**

region3 0.225

(7.60)**

region4 0.085

(2.79)**

region5 0.081

(2.57)*

_cons 4.379

(78.79)**

R2 0.92

N 348

56

MACRO MODEL2003-2010

locupados

limpuestos 0.130

(8.89)**

grande 0.684

(26.77)**

mediana 0.294

(11.97)**

complejidad 0.323

(9.20)**

densidadd 0.039

(1.75)

_cons 4.184

(88.98)**

R2 0.75

N 841

locupados

limpuestos 0.079

(6.24)**

grande 0.615

(41.61)**

mediana 0.247

(15.94)**

complexity 0.142

(5.76)**

region1 0.859

(30.26)**

region2 0.129

(5.33)**

region3 0.166

(8.73)**

region4 0.064

(3.26)**

region5 0.031

(1.73)

_cons 4.422

(118.71)**

R2 0.92

N 841

57

MICRO MODEL

2006 Quarter 2

estadon sexo 0.630(32.58)**

hpi -0.698(27.26)**

si 0.213(9.03)**

sc 0.388(16.77)**

ti 0.309(11.27)**

tc 0.553(17.63)**

mediana 0.019(0.71)

grande 0.088(3.60)**

edadt2 1.586(88.47)**

edadt3 1.381(57.33)**

complexity 0.175(5.10)**

publico 1.868(38.68)**

densidadd -0.071(3.27)**

sex_jef 0.264(11.10)**

_cons -1.769(44.92)**

N 47,646

58