DETERMINANTS ON RECOVERIES OF

UNEMPLOYMENT RATES

LATIN AMERICA RECESSIONS 1990 – 2010

Eliana Alvarez. Student, Department of Economics

Faculty Advisor: Joshua Hall, Ph.D., Department of EconomicsSykes College of Business

Research

DETERMINANTS ON RECOVERIES OF UNEMPLOYMENT RATES

LATIN AMERICA RECESSIONS 1990 – 2010

Prepared by Eliana Alvarez

May 2015

Abstract

This paper analyses the determinants of economic recoveries in unemployment rates within Latin America following recessions within the years 1990 – 2010. Okun’s Law describes the relationship between an economy’s unemployment rate and Gross Domestic Product (GDP); when GDP increases, unemployment will fall. However, GDP is not the only variable affecting unemployment, by adding other types of variables, we understand why there are differences between Latin American countries. According to our research within Latin America, four variables aside from GDP also influence unemployment: Size of Government, Share of Agriculture/Rural Population, Rigidity of Employment/Firing Costs, and Exchange Rate/Level of Openness. An IMF analysis (Unemployment in Latin America and the Caribbean) looks at how these categories affect the level of long run unemployment, we, however, utilize cover panel data instead of cross sectional to expand the number of observations. Our analysis will consist on an overall view of the Latin America regression, and how unemployment is influenced by GDP and the four variables described previously. Afterwards, our analysis will study each Latin American country individually and again view each country’s regression, and how unemployment is affected by GDP and its four variables.

Keywords: Unemployment, Latin America

Author’s E-Mail Addresses: eliana.alvarezabasto@spartans.ut.edu, eliana.a94@gmail.com

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I. Introduction

Latin America has experienced four periods of negative average growth affecting all twelve countries in South America between 1990 and 2010. Of the four, the second and third episodes occurred specifically in Argentina and Venezuela with no major effect on the rest of the South American countries. Therefore, only the two latter episodes are of major significance and will be taken into account. The first significant recession occurred within the 1997-1998 period (linked to the Asian crisis), and the second major crisis was provoked by the 2008-2009 global financial crisis and world recession. What will also be taken into consideration, however, is that each single country was affected by its own “crisis” that provoked major impacts in their specific economies.

When studying the economy, growth and jobs are two very important factors that economists must consider. Theory and research concede that there is a reverse relationship between economic growth and unemployment levels; the well-known Okun’s Law notes that a percentage increase in GDP per capita causes a fall in unemployment. IMF Working Paper (Unemployment in Latin America and the Caribbean) concords with the relationship between GDP per capita and unemployment; however, it differs from our study in that it covers all Latin American countries and the Caribbean. Instead our study is set up as 307 observations from12 countries only in South America. A second journal article Economic Impact of Freer Trade in Latin America and the Caribbean: A GTAP Analysis also provides data for all Latin American countries and the Caribbean rather than just South America. It does support Okun’s Law; however, it differs from our study in that it analyses the impacts from the worldwide financial crisis of 2009 only and does not cover other recessions. El Impacto de la Crisis Economica y Financiera sobre el empleo juvenile en America Latina is the third journal analyzed that provides similar results with the previous two journals with respect to Okun’s law. It differs, however, in that it focuses on youth unemployment during Latin American crisis and not unemployment in general.

Even though the IMF, Economic Impact of Freer Trade in Latin America and the Caribbean: A GTAP Analysis, and El Impacto de la Crisis Economica y Financiera sobre el empleo juvenile en America Latina researchers claimed that GDP was the only variable that led to the decrease in unemployment after the recessions, they only looked at the total GDP in Latin America. When examining each country individually, data indicates that the marginal impact of changes in GDP leads to very different results in the unemployment growth rate. Table 1 shows the marginal impact of changes in GDP capital growth rates on unemployment growth rates based on data collected on 307 observations in 12 South American countries. By analyzing this data, one can see that there are vast differences between countries. For example, unemployment growth rate is more sensitive to changes in GDP capital growth rate in Bolivia with a -6.87 than that of Paraguay -0.79. This leads to the following central question: What other variables cause unemployment to decrease if it cannot be solely attributed to GDP growth?

Other variables were considered that could have affected the decrease in unemployment after the recessions. These included Size of Government, Share of Agriculture, Rural Population, Rigidity of Employment, Firing Costs, Real Exchange Rate, and Level of Openness. The purpose of this paper is to show how these different variables have affected the rise of employment following

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recessions, and how these factors need to be considered to elicit economic growth in future recessions.

II. Unemployment Data

We examined one set of unemployment data constructed by the IMF analysis – IMF Unemployment in Latin America and the Caribbean. This type of data set provides longer time series within countries. Our study examines a data set for 307 observations in 10 countries that covers period 1990 – 2007. Although the study itself covers three extra years, the data set found by the IMF only provided us with those years. For a number of countries, we have annual data on unemployment back to the 1970s with a few missing observations. This data is found in the Appendix – Fig. 1.

As clearly portrayed, there are vast differences between one country and another. Argentina for example has suffered from unemployment starting in the 1990s, decreased for a few years by 1998, and came back up two years later. Since then, it has decreased impressively. Some others, as Colombia, have experienced the reverse. During those years that Argentina experienced increases in unemployment, Colombia suffered none, and when Colombia’s unemployment peaked, Argentina’s was slowing down. Bolivia and Brazil show almost no vast differences from year to year. Both have experienced quite some stable but very low unemployment rates. That’s the question that needs to be solved. Why the vast difference from country to country?

Our analysis will consist on an overall view of the Latin America regression and how unemployment is influenced by GDP. The equations were run for each individual country and as a total group. The regressions are standard OLS with robust standard errors. However, GDP is not the only variable affecting unemployment, by adding other types of variables, we understand why there are differences between Latin American countries. According to our research within Latin America, four variables aside from GDP also influence unemployment: size of government, share of agriculture/rural population, labor rigidity, and other macro economic variables (real exchange rate and level of openness). Therefore, Latin America regression analyses not only the relationships between unemployment and GDP, but also the influence of the four variables described previously.

II. Candidate Explanation

We examine four sets of variables that might influence the relationship between Δ GDPpc and ΔUnemployment. It is essential to examine other types of variables in our context because, as seen previously, the Marginal Impact of GDP per Capital Changes (Table 1) vary greatly across Latin American countries.

Here we briefly describe the variables that we examined. The Appendix - Table 2 gives further detail on Simple Correlation between each of these four variable and Δ GDPpc and ΔUnemployment.

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We examine four variables that are of major influence on employment:

Size of Government: Negative significant relationships between government size and economic growth. Increases in the size of government measured as a percent of national income reduce per capita growth, which indirectly increases unemployment.

Share of Agriculture/ Rural Population: The higher rural population; the lower the unemployment

Rigidity of Employment/Firing Costs: Firing costs increase unemployment. When firing costs increase, firms refrain from hiring since, if a worker turns out to be inadequate, it will be more costly to fire him.

Exchange Rate/Level of Openness: higher trade openness is associated to a lower structural rate of unemployment. Trade liberalization increases job turnover as workers are reallocated from shrinking to expanding sectors.

Size of Government

Size of Government is the first of four variables that is the to be considered as factors that could inversely affect change in unemployment. Quora defines “Size of Government” as government revenue as a share of GDP. Figure 1.1 and Figure 1.2 depicts eleven South American countries and their varying GDP, Size of Government results within the period 1990 – 2010.

Latin America opened to trade in the year 1983. Before trade openness was initiated, GDP was 40%. At the end of the 2000s, the level of GDP increased between 70% and 75% (Samano and Szekely 2012). This can be clearly portrayed in Figure 1.2 and 1.3. Although in different levels, each of the eleven Latin American countries increased drastically from 90’s to the year 2010. Some of these major changes can be seen with Chile, Suriname, and Venezuela. In the year 1990, Chile’s GDP per capita was $2,388.31, but it increased to an average of $12,681.77 in the next two decades. The GDP per capita of Suriname also increased in the two decades prior to 2010, from $954 to $8,321.39. Furthermore, Venezuela’s GDP per capita reached $2,382.25 in 1990, and by the year 2010, it reached the highest GDP per capita in South America with $13,559.13. In general, between the period 1990s and 2008, the average growth rate of GDP per capita tripled in South America (Cornia 2014). In 2009, GDP per capita contracted by 2.9% but rebounded to 4.2% by the next year (CEPAL 2010).

2) Share of Agriculture

Share of Agriculture is the second variables that is the to be considered as a factor that could inversely affect change in unemployment. Agriculture is a component of the GDP of a nation that includes the process of forestry, hunting, fishing, cultivation of crops, livestock production, etc (“Agriculture” 2015).

Data was recollected for all eleven countries except the country of Peru. All countries, with the exception of Argentina and Venezuela portray a decreasing trend in the agricultural sector.

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Argentina experienced a sever recession from mid-1998 to the end of 2001, reason why agricultural output as a percentage of GDP decreased from 8.12% to an approximate of 4.31% (Weisbrot et al. 2011). Exports continued to not be very large during the expansion of 2002 – 2008. By the first quarter of 2005, GDP reached its pre-recession level after three years of constant struggle, and once again the role of exports increased. Figure 2.1 and Figure 2.2 clearly shows that by the year 2010, agricultural output as a percentage of GDP increased to almost the exact amount as pre-recession year 1990 reaching a 8.18%.

For centuries, the agricultural sector was the main engine of the Venezuelan economy, employing 60% of the labor force and accounting for more than 21% of GDP (Encyclopedia of the Nations 2015). However as for the year 1999, agriculture is one of Venezuela’s weakest sector employing 13% of the labor force and accounting for only 5% of GDP. This can be clearly depicted in Figure 2.1 and Figure 2.2, where Venezuela is placed with the lowest agricultural output as a percentage of GDP compared to the rest of the Latin American countries. Aware of such difficulties, the Venezuelan government decided to approach it in two ways. Subsidization of agriculture is a constant tool used by the Venezuelan administration to aid the agricultural sector. In 1999, the right of farmers to receive government subsidies became part of the Venezuelan Constitution. Subsidies or government payments to farmers are a powerful tool to aid farmers in improving their inefficient and obsolete production techniques and machineries and ineffective management strategies (Encyclopedia of the Nations, 2015). Since then, the country’s agricultural output as a percentage of GDP raised from 4.21% in 2000 to an approximate of 5.79% a decade later.

On their research “Did Trade Openness Affect Income Distribution in Latin America?” both authors Szekely and Samano affirm that overall Latin American did experience a declining trend in Agricultural GDP as % of total GDP (Szekely and Samanao, 2012). Figure 8 the declining trend in the period 1990 – 2008.

2) Rural Population

Rural Population is combined with Share of Agriculture as the second variables that is the to be considered as a factor that could inversely affect change in unemployment. Rural Population refers to people living in rural areas. It is calculates as the difference between total population and urban population (Quora).

According to a research done by the IMF “Unemployment in Latin America and the Caribbean”, GDP per capita and rural population have a negative correlation of -0.80, meaning that higher-income countries are less rural. Figure 2.3 clearly depicts this negative trend. According to the IMF, rural population is the primary development variable that influences unemployment The higher the rural population, meaning citizens do not live in urbanized areas, the lower their productivity and contribution to GDP per capita. With the exception of Guyana, the rest of the eleven Latin American countries do show this negative trend and for each one of them, GDP per capita increases as depicted in Figure 1.1, assuring that both are inversely correlated. Although Guyana does experience an increasing trend in GDP per capita, the country has the highest proportion in South America of people who live in rural areas that continue to rise year by year (Encyclopedia of the Nations).

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Figure 2.4 and Figure 2.5 show 71.76% of its population lives in rural areas, leaving a small amount of its people in urban areas. This percentage has stayed constant since the year 1990. Half of its population is descended from Indian workers of the Dutch West Indian Company who came to settle the country in the 1620. Also, one-third of the Guyanese are direct descendants of Africans who were brought as slaves in the 18th century (Encyclopedia of the Nations).

3) Rigidity of Employment

The rigidity of employment index measures the regulation of employment, specifically the hiring and firing of workers and the rigidity of working hours (Nation Master). This index is the average of three sub-indexes: a difficulty of hiring index, a rigidity of hours index, and a difficulty of firing index. The index ranges from 0 to 100, with higher values indicating more rigid regulations (Nation Master).

Data was only obtained for years 2003 – 2006. Within this period, all twelve Latin American countries experience a somehow constant Rigidity of Employment, with the exception of Bolivia that experienced a drastic change from 40 in 2005 to 75 in 2006. In Bolivia, term contracts are used only for term tasks while in other countries, such as Chile, Colombia, and Ecuador, these restrictions do not exist. A reason why the difficulties of hiring index is much higher than in other Latin American countries. Also, term contracts in Bolivia are limited to three years; Colombia and other Latin American countries do not impose limits.

A second reason is that in Bolivia, a formal employer must pay a minimum of 100 weeks of salary to dismiss a worker – one of the highest firing costs in the region. Moreover, the employer must give a 90-day notice before a redundancy termination. If not done so, for workers with 20 years of service must receive 21 months of wages. The rigidity of hours index is also one of the highest in the region. Vacations are longer in comparison with regional standards. An employee with 20 years of service has 30 days of paid vacation in Bolivia, 18 in Chile, and 15 in Colombia.

In 2006, socialist president Evo Morales came to power with a complete different set of rules that were imposed on private companies (Orihuela, 2007). He started by nationalizing several private companies, and these, due to the fear of a bureaucracy coming to power and having to follow changes, started to cut off employment. These unemployed will then be selling in the streets without paying taxes. Therefore to counteract such reaction, President Morales increased rigidity of employment in order for the few private companies to reconsider such action (Orihuela 2007).

3) Firing Costs (2003 - 2006)

Trading Economics defines Firing Costs as the cost of advanced notice requirements; severance payments; and penalties due when terminating a redundant worker; expressed in weekly wages. One month is recorded as 4 1/3 weeks.

Data was also only obtained between years 2003 – 2006. Although at different levels, all eleven Latin American countries have maintained their Firing Costs within those three years with the

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exception of Argentina. In the year 2003, Argentina’s firing cost was the highest in comparison of the other ten Latin American countries being analyzed. However by the year 2006, the 182 weeks of wages fell to 138.7 weeks of wages but still ranking first in firing costs amongst emerging markets.

According to the World Bank, Argentina is categorized as number 8 country with the highest firing cost. Reason why levels are so high is because the legislation has been incorporating norms to fight back unregistered work (El Costo del Despido). Unfortunately in 20 years, this clearly failed and has not done much. During the period 2003 – 2007 when President Nesto Kirchner was in power, there were mass layoffs occurring. In order to slow it down, President Kirchner imposed “double compensation” for unfair dismissal when imposed when the unemployment rate has soared to approximately 24% of the total labor force. Through the “double compensation”, this indicator fell to less than 10% as was promised by President Kirchner. “Double compensation” included increase in real wage, growth in the quality and quantity of employment, decrease poverty, and improvement of income distribution.

4) Real Exchange Rate

The Real Exchange Rate as the purchasing power of a currency relative to another (in this case relative to US Dollars). As depicted in Figure 4.1 and Figure 4.2, there is an obvious outlier that differentiates from the rest of 18 Latin America.

The reason why is because Brazil’s currency, the real (R$), was recently introduced on July 1st, 1994 (Encyclopedia of the Nations). Since then till January 14, 1999, the official rate was determined by a managed float. January 15, 1999 is when the official rate floated independently with respect to the US dollar. From the 1970s onward, the country suffered of high inflation due to government spending, service of the public debt, and rise in prices.

In the mid-1990s, President Fernando Henrique Cardoso adopted measures to slow down such chaos by slowing down government spending and renegotiating public debt; all this fighting back inflation. Brazil’s currency was constantly devalued against the US dollar. This can clearly be seen in Figure 4.1 and Figure 4.2 where in the year 1990, the exchange rate was of 0.00002956 R for $1.

However, this devaluation of the dollar generated greater exports causing a decrease in trade imbalances. However, it was much harder to import from abroad since prices were very high compared to those in the home country. Between 1995 and 2000, the real devalued by about 100%, reaching 1.82942312real for $1 in a period of one decade. By 2010, the real appreciated a bit to 1.75922671 real for $1. Devaluation reached its peak in early 1999, when the central bank of Brazil adopted a floating exchange rate system. By that time, all other Latin American countries abandoned the regimes of free floats and fixed pegs, and opted for managed exchange rates to achieve a competitive real exchange rate and for appreciation not to occur (Cornia 2014). The exchange rate fell by 56% in a period of one year (1998 - 1999).

4) Level of Openness (1990/2000/2010)

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If the degree of openness equals to zero, this indicates that the country has a closed economy is total autarky. The higher the degree of openness, the more likely foreign countries have a stronger affect on the economic variables of the home country (Belke 2005).

Figure 4.3 and Figure 4.4 show positive trends for each of the nine Latin American countries. In the late 1980s and 1990s, these Latin American countries opted for trade liberalization with the hope this would lead to greater economic growth and a more equal distribution of resources (Szekely,2012). Trade reductions were greatest during the period 1985 and 2009 that impacted mostly Paraguay, Brazil and Peru. Paraguay was the country that experienced the largest increase in trade flows. Intra-regional trade grew quickly, exporting mostly to Asian countries (Cornia 2014).

Governments attempted to reduce foreign debt. Brazil and Argentina prepaid their debt to the IMF. Between 2002 and 2009, the region’s foreign reserves increased from $150 to $550 billion, and its gross foreign debt declined from 40% to 20.4% of the regional GDP (Cornia 2014).

III. Conclusion

Table 2 depicts Simple Correlation between the relationship between each of the four variables with Δ GDPpc and ΔUnemployment. If correlations are positive, a higher variable indicates a weaker relationship between changes in GDP per capita and changes in unemployment rate. If it is negative, more of that variable indicates a stronger relationship between changes in GDP per capita and changes in unemployment rate, which means the unemployment rate is very sensitive to changes in GDP. The reverse is true.

Since correlations is negative (-0.2197), the larger the Size of Government indicates a stronger relationship between ΔGDP per capita and ΔUnemployment rate which means unemployment rate is very sensitive to changes in GDP. The reverse is true.

As depicted in Figure 2 – Relationship between ΔGDP and Δ unemployment rate vs. level of labor rigidity. A positive correlation depicts Rigidity of employment as not being very sensitive to the relationship between ΔGDP and Δ unemployment rate.

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Abstract

TABLE 1: Relationship between Δ GDP and Δ Unemployment

TABLE 2: Simple Correlation with Marginal Product

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Figure 1: Unemployment Rates 1990 - 2007

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Fig. 2: Relationship between ΔGDP and Δ unemployment rate and vs. level of labor rigidity

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