Globalization and the Urban Informal Economy in Developing Countries

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Globalization and the Urban Informal

Economy in Developing Countries

Msc Thesis International Economics and Business

University of Groningen – Faculty of Economics and business

Abstract

In this paper, the relationship between a country’s openness to trade and the size of its informal sector is researched. Results indicate that a country’s openness is negatively related to the size of its informal sector, indicating that an increase in foreign trade leads to a decrease in the size of the informal economy.

Key words: informal economy, developing countries, trade openness,

urbanization, globalization

Author Roselien Boonstra

Student ID number 1904434

Date 31 August 2016

Supervisor S. Brakman

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1. Introduction

Activities carried out outside the formal framework have long been neglected in economic research (Gherxani, 2004). In recent years, the informal sector has received increasing attention from businesses and economists. According to the International Labour Organization (2012), the informal economy includes more than half of the global workforce and more than 90 percent of small and medium sized enterprises. Especially in emerging economies, the informal sector is larger than previously thought and businesses are starting to realize the potential of this market (London & Hart, 2004).

The informal sector entails all market-oriented activities related to production or services that are carried out outside the reach of state regulation (Hart, 1973). Previous studies have already established the relation between urbanization and the emergence of an informal sector in developing countries (Elgin & Oyvat, 2013; Ghani & Kanbur, 2013; Duranton, 2015). With technological development, the rural sector becomes less labour-intensive and people start migrating to cities looking for jobs in production or services. The urban formal sector does not develop fast enough to absorb all the extra labour, and an informal sector starts to emerge (Moreno-Monroy, 2012; Elgin & Oyvat, 2013).

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workers are likely to end up in the informal sector. At the same time, formal enterprises start looking for input in the informal sector, which can lead to an increase of the informal sector (Goldberg & Pavcnik, 2003). To date, this relationship has not much been studied and is definitely a topic of interest since the informal sector seems to be so persistent in developing economies. This research is an attempt to shed more light on the relationship between a country’s openness to international trade and the size of its informal sector. Since the informal sector is mainly present and persisted in developing economies, emerging countries will be the focus of this study.

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2. Literature review

2.1 The informal sector

This study is focused around the urban informal sector (UIS). Schneider (2010) defines the urban informal sector as those activities that entail market-oriented production or services, but are hidden from state authority in order to avoid payment of taxes or social security contributions, complying with labour market standards, and dealing with other administrative procedures. Similar definitions are provided by the International Labour Organization (2015), Gherxhani (2004), and Hart (1973). The International Labour Organization (ILO) makes the distinction between informal employment and informal enterprises. In this study, this distinction will not be of particular importance, and therefore the terms ‘informal sector’, ‘informal employment’, and ‘informal economy’ will be used interchangeably and refer to all economic activity happening outside the reach of state regulation.

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At the same time, large exporting industries are employing informal workers in their commodity chains. In the manufacturing sector, informal activities like sweatshops, and unlicensed factories are still quite common (Carr & Chen, 2002). As a result, economic growth and globalization can go hand in hand with an expanding informal economy.

Besides factors pulling workers to the city, there are also reasons why workers migrate away from rural areas. As a result of technological development and improved access to these technologies in the agricultural sector, the sector as a whole becomes less labour-intensive and puts large-scale farms at an advantage when prices fall. This diminished demand for agricultural labour and diminished returns for small-scale farming lowers earnings in rural areas and pushes workers into the urban informal sector (Elgin & Oyvat, 2013).

Even though it is hard to measure the size of the informal economy precisely, there exists a consensus that the informal economy is large in developing countries, and a major employer of labour outside the agricultural sector (Ghani & Kanbur, 2013). When considering developing countries as a group, more than half of all jobs (900 million workers) are found in the informal sector (Jutting & Laigesia, 2009). The percentage of non-agricultural jobs in the informal sector does, however, differ largely between developing economies. India tops the list with a share of informal jobs around 80%, and Central and Eastern Europe at the bottom (Ghani & Kanbur, 2013).

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An explanation for the persistence of the informal economy in developing countries could be self-employed entrepreneurs that deliberately choose to work in the informal sector in order to capture the benefits of tax evasion as well as flexible employment relationships (Yamada 1996; Carr & Chen, 2002; Maloney, 2004). When the existence of the informal sector was first recognized, it was considered to entail marginal or residual activity (Tokman, 1989). The popular view of the informal sector was one of involuntary employment for low wages as a temporary source of income until a formal job could be attained (Yamada, 1996; Gerxhani, 2004). Entrepreneurs are able to earn competitive wages and illustrates that working in the informal sector is not necessarily a transitional solution for all informal workers.

2.2 Trade Openness

Openness to trade is often linked to economic growth. The prevailing idea is that an open economy experiences more and faster economic growth, as open economies reap the benefits of gaining knowledge from foreign firms. This learning mechanism is strongest when it is operated through FDI (De Jong et al., 2006). Moreover, an open attitude to foreign trade enlarges potential markets, making it possible to gain more benefits from scale economies. At the same time, firms that have to overcome foreign competition are forced to increase their productivity, making them more efficient and competitive.

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Openness is much related to governmental decisions on foreign trade policy and their attitude to international collaboration. The attitude of a country towards international trade is quite broad, as Baldwin (2003) states: ‘One can interpret openness in narrow terms to include only import and export taxes or subsidies as well as explicit nontariff distortions of trade or in varying degrees of broadness to cover such matters as exchange-rate policies, domestic taxes and subsidies, competition and other regulatory policies, education policies, the nature of the legal system, the form of government, and the general nature of institutions and culture’. In this study, both a broad measure of openness as well as measures limited to openness to trade and FDI are used.

2.3 Effects of trade openness on the informal economy

Extant literature on the relationship between the size of the informal sector and trade openness provide ambiguous results (Davalos, 2012; Fugazza & Fiess, 2010). Some literature suggests a positive relation, while other research finds evidence for a negative influence. Moreover, most research focuses on a limited number of countries or is conducted in the form of a case study. Especially South American countries have been extensively studied in relation to the informal economy (Davalos, 2012; Maloney, 2004; Goldberg & Pavcnik, 2003). Few researches have been conducted using a large set of countries including multiple years. An exception is a study conducted by Fugazza & Fiess (2010), who conduct a research to the sign of the relationship between trade openness and informality for a large set of countries in three different manners. However, they find ambiguous results as well.

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circumventing legislation. Following this argument, economic growth and globalization can be compatible with an expanding informal sector. More demand for (cheap) export products and increased international competition will put pressure on the formal sector to produce more efficiently and cheaply (Milner & Rudra, 2015). In order to save costs, formal enterprises will lay off formal workers and search for cheaper inputs in the informal economy (Fugazza & Fiess, 2010). Both forces result in growth of the informal sector, since laid off formal sector workers are expected to look for informal sector jobs and the demand for inputs from the informal sector grows (Goldberg & Pavcnik, 2003).

On the other hand, a reversed effect might also be plausible. When trade liberalization measures are implemented, trade costs decrease. Large MNEs from advanced economies that penetrate developing economy markets bring distinctive products that are commonly very popular. Brand names and other distinctive product features are advertised and preferred over seemingly homogeneous product of local (informal) enterprises (Elgin & Oyvat, 2013). As a result of this competition, the least productive local manufacturing firms are driven out of the market, leading to a decline of the informal sector. The informal firms that are able to survive the increased competition have more incentive to formalize their business, as this presents them with an opportunity to start both importing inputs and exporting final products (Aleman-Castilla, 2006; Fugazza & Fiess, 2010). In addition, formalizing makes these businesses eligible for governmental trade promotion programs, subsidies and local and foreign financial resources. As a result, the most productive informal firms are likely to enter the formal sector, while at the same time the least productive informal firms will be outcompeted and exit the market (Fugazza & Fiess, 2010), diminishing the size of the informal sector.

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economic environment should be as attractive as possible for foreign investors and MNEs. This might be an incentive for governments to put formalization of the economy on their agendas. When bureaucratic quality, legislation and proper infrastructure are up to the standards of advanced nations, international investment of and trade with advanced nations becomes more likely (Larsen et al, 2013). Increased attention for formalization from the local government forms another push for informal firms to formalize, as the probability of getting caught and receiving fines for tax evasion and breaking other laws will increase when supervision on rules and regulations is tightened (Aleman-Castilla, 2006).

Elgin & Oyvat (2013) bring the argument that the informal economy in developing countries grows faster in early stages of development, and growth will level off and decline in later stages of development. They argue that when a country modernizes, a capitalist mode of production will start to dominate the market and cause a collapse of traditional activities related to the informal economy. In this study, data from 1999-2007 are used. In this time period, most developing economies would probably have passed the ‘tipping point’ of informal sector growth as a result of development. Expansion of the informal economy will have levelled off, and is likely declining in this period of time due to exposure to foreign influences and further economic advancement. Therefore, the hypothesis for this study is:

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3. Empirical Analysis

An empirical analysis will be performed in order to gain a better understanding of the relationship between a country’s attitude to foreign trade and the size of its informal sector. In this section, measures of the informal sector and trade openness as well as the selection of control variables and data sources will be discussed. Furthermore, a model based on these variables will be introduced.

3.1 Variable selection

In order to shine as much light as possible on the relationship between a country’s openness to trade and the size of the informal sector, various control variables are used. These variables are commonly used in empirical research using the size of the informal sector as a dependent variable. The control variables used in this research are GDP per capita, population density, unemployment rate, urbanization, tax burden, and corruption.

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their expected effect on the size of the informal economy can be found in Appendix 1.

3.2 Data

3.2.1 Developing economies

Since the informal sector is mainly a topic of interest in the case of emerging economies, the focus of this study will be on developing nations.

The World Bank classifies countries on the basis of Gross National Income (GNI). Incomes are measured as GNI per capita in U.S. dollars. It makes the distinction between low-income (GNI of 1,045 or less), lower-middle income (GNI of 1,046 to 4,125), upper-middle income (GNI of 4,126 to 12,735) and high-income (GNI of 12,736 or more) economies. According to the World Bank, all countries classified as low-income and lower-middle income countries can be seen as developing economies. However, a closer look at the countries in these income categories reveals that countries generally perceived as developing economies mainly in Europe and Latin America are excluded.

The IMF publishes the World Economic Outlook (WEO) report and databases for all member countries twice a year. In this report, countries are divided into two major groups: advanced economies and developing economies. In the statistical appendix to the WEO database (2013), the IMF states that: ‘this classification is not based on strict criteria, economic or otherwise, and it has evolved over time’. As a result, all countries that are not marked as advanced, are classified as emerging or developing. For this study, the classification of emerging economies by the IMF was chosen in order to ensure a large and diverse set of countries. The 153 countries classified as emerging and developing by the IMF will be taken as a starting point of this analysis. Interestingly, this has as a result that all income groups of the World Bank classification are represented in this dataset.

3.2.2 Data collection

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economy was retrieved from Schneider et al. (2010). Schneider et al. developed a measure to estimate the size of a country’s informal economy by integrating indicators of and influences on the informal sector. The dataset contains an estimation of the informal economy for the years 1999-2007. Unfortunately, no data on the informal economy for a large set of countries exits for years after 2007. Therefore, the years 1999-2007 are included in this research in order to ensure a balanced panel data set.

The key explanatory variable in this study is a country’s openness to trade. The KOF index of globalization (Dreher, 2006) is a weighted index of economic, political and social factors determining a country’s degree of trade and general openness to the rest of the world. The economic dimension of the index indicates the amount of cross-border trade, investment and revenue flows in relation to GDP as well as the impact of trade and capital transaction restrictions. The Social pillar is measured by cross-border personal contacts, cross-border information flows, and cultural affinity to the global mainstream. Lastly, the political dimension is determined by the number of foreign embassies in a country, the number of international organizations the country is a member of, the number of UN peace missions the country has been engaged in, and the number of bilateral and multilateral agreements the country has concluded since 1945. Based on the score of 23 variables within these three dimensions, every country is assigned a ‘globalization score’ from 1 to 100.

Besides the KOF index, two alternative variables to proxy trade openness are introduced. The first is trade relative to GDP, which is the sum of imports and exports of a country in a specific year as a percentage of GDP. The second is FDI relative to GDP, which reflects yearly FDI streams as a percentage of GDP. Both these variables are a more direct measure of a country’s economic openness to the rest of the world and are retrieved from the World Development Indicator (WDI) database by the World Bank.

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published yearly by Transparency International. A summarized explanation of all variables used in this study can be found in Appendix 1.

3.2 Model and methodology

Panel data are two-dimensional as they are both cross-sectional and time. The panel dataset used in this study allows analysis of the relationship between trade openness and the size of the informal sector for the same countries in nine consecutive years. Following the theory and collected data, the following model to test the relationship between a country’s openness to trade and the size of the informal economy is constructed:

(log)‘informal economy’i,t = β0 + β1 (log)‘trade openness’i,t + β2 (log)’GDP’i,t + β3 (log)’urbanization’i,t + β4 (log)’unemployment’i,t + β5 (log)’corruption’i,t + β6 (log)’tax burden’i,t + β7 (log)’population density’i,t +

ε

i,t

In which subscript i stands for the country, and subscript t for the year. ‘Informal economy’ is the size of the informal economy, and ‘trade openness’ is proxied by the KOF index of globalization. ‘GDP’, ‘urbanization’, ‘unemployment’, ‘corruption’, ‘tax burden’, and ‘population density’ are control variables over the period 1999-2007, and

ε

i,t is the error term.

4. Results

Following the outcomes of an estimated baseline model, observations that contained many missing values making the panel unbalanced were removed. This leaves 120 countries in the panel to be further analysed. The list of countries included in this analysis can be found in Appendix 2.

Summary statistics of the variables are available in table 1. Note that the variables are not logged yet. There are clear signs of outliers present in the summary table. Using the assumption that a data point is an outlier when it deviates three times or more the standard deviation from the mean, outliers are present in several of the variables.

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Variable Obs Mean Std. Dev. Min Max Informal 1079 37.6899 10.85082 11.9 68.3 KOF 1080 48.3752 12.73343 20.45 86.46 Trade 1059 81.62543 43.25662 0.31 412.12 FDI 1060 4.025151 6.354494 -16.59 89.48 GDP 1071 2843.479 4444.932 100.69 45159.34 Urbanization 1080 47.24568 21.28764 8.036 98.217 Unemployment 1080 9.213056 6.59364 0.6 38.6 Corruption 806 3.140893 1.100264 0.4 7.5 Tax burden 611 15.47877 8.369144 0.78 95.16 Pop. density 1080 102.3639 157.1789 1.529497 1163.333

In order to ensure reliable results, it is important that the variables are normally distributed. In order to test this, a skewness/kurtosis test for normality is performed. The results are presented in Appendix 3. From the test for normality, we can conclude that for all variables except ‘Informal’, being the size of the informal economy, the hypothesis that the variable is normally distributed can be rejected. For the variable ‘Informal’, it is also impossible to accept the hypothesis that the variable is normally divided at the 5% significance level, although it is close. In order to avoid problems caused by abnormally divided data and outliers, all variables will be logged before proceeding with the remaining part of the analysis.

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To further assess whether correlation will form a problem in the analysis, the Variance Inflation Factors (VIFs) of the variables after regression are inspected. The VIF scores can be found in Appendix 5. The rule of thumb for VIFs is that they should not be above 4.0 in order to avoid multicollinearity, and all VIFs are well below this. The downside of this method is that it ignores the possible correlation between the dependent variable and the explanatory variables. However, from the previously calculated correlation scores, there seems to be no problem with correlation between the dependent variable and the other variables in the model.

Since panel data is used in this thesis, we have to assess whether to use a fixed effects or a random effects model. The random effects model is generally preferred due of its higher efficiency. A Hausman test was performed between the fixed effects and random effects estimations. The results of this test can be found in Appendix 6. When the coefficients estimated by the efficient random effects estimator are the same as the ones estimated by the consisted fixed effects estimator, the random effects model can be safely used. From the results of the Hausman test, we can see that the P-value is insignificant (Prob>chi2 larger than .05), so we proceed to use the random effects model.

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Table 2: Output share of the size of the informal sector and trade openness (all countries)

Dependent Variable: INF (1) (2) (3) (4) (5) (6)

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4.2 Robustness checks

Since the informal sector is a variable that is hard to measure, basically all data available on the size of the informal sector are estimations. In order to perform robustness checks, an alternative measure of the size of the informal sector is desired. As stated in the data section, the ILO does provide numbers on the share of informal employment in total non-agricultural employment, but this data is limited in the number of countries that are included as well as inconsistent in the years that it is measured. There is hardly any data available from the ILO on the years 1999-2007, which are included in this research.

As an alternative, different measures for the explanatory variable trade openness are included. Even though the KOF index is a well-rounded and balanced measure of globalization, this research attempts to find a direct relationship between a country’s openness to international trade and the size of the informal economy. Therefore, two alternative variables to measure countries’ openness are introduced. These are the amount of international trade (the sum of imports and exports) relative to GDP as well as FDI inflows relative to GDP. These variables are a more direct measure of the amount of international trade a country is involved in and a country’s attractiveness for investment. Random-effects linear panel regressions were run with both alternative dependent variables. The control variables that showed to be most significant in the previous analysis are included in the analyses with the alternative measures for openness. These are also the variables including the most observations, ensuring the richest data.

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We observe negative and highly significant relations with the size of the informal economy for all (alternative) dependent variables. As a result, it can be stated with more certainty that more openness to trade is associated with smaller informal economies. Especially since the two alternative measures of openness are more direct measures of economic openness than KOF. Urbanization has a negative effect, and is again found to be highly significant. Unemployment is still highly significant and positively related for all the regressions. Also, population density seems to have a steady negative effect on the size of the informal sector, being significant for all three regressions.

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Table 3: Output shares of the size of the informal sector and trade openness (all countries)

Dependent variable: INF KOF Trade FDI

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Intuitively, it can be expected that countries that are more advanced have developed more stable economies over time. As a result, these economies are expected to have more open attitudes to foreign trade since their markets are strong enough to deal with foreign competition and reap the benefits of exporting. Also, foreign trade could have different effects on the size of the informal economy in more advanced economies, since they are able to adjust to changing circumstances more easily. Since all income groups are represented in the dataset, it can be enriching to perform an analysis including only those countries that are least developed. Growth of the informal sector occurs faster in early stages of development, before it levels off (Elgin & Oyvat, 2013). Those countries that are least developed might still have a fast growing informal economy. Following the definition of developing economies by the World Bank, a second alternative regression is run only including countries with either low or lower-middle incomes. This leaves 66 countries to be analysed, of which a full list can be found in appendix 7. Again, the regression is run with all three dependent variables and the control urbanization, unemployment and population density.

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Table 4: Output shares of the size of the informal sector and trade openness (low and lower-middle income countries)

Dependent variable: INF KOF Trade FDI

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5. Conclusions

The interest in the urban informal economy has been growing in recent years. Especially in developing economies, the informal sector seems to be quite large and persistent. This previously underestimated and overlooked sector provides opportunities and businesses are starting to realize the potential this sector has to offer. As a result, developing economies have increasingly become a subject of interest for businesses looking to engage in FDI and trade in these countries.

This paper researches the influence of developing economies’ trade openness on the size of its informal sector. An empirical analysis for 120 developing economies with three different dependent variables proxying trade openness was performed for the years 1999-2007. Through all analyses, trade openness seems to be negatively related with the size of the informal sector, indicating that when a country increases its involvement in international trade, the size of its informal economy decreases. This can be interpreted as a sign that economies are formalizing under the pressure of increased international competition.

In addition, consistent negative effects on the size of the informal sector are found for population density, indicating that when a country’s population density increases, the size of its informal economy decreases. At the same time, a consistent positive effect is found for the unemployment rate, indicating that when unemployment increases, the size of a country’s informal economy increases as well.

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A limitation to this, and all researches on the informal economy, is that the informal economy is hard to measure. Therefore, all data available on the size of the economy are mere estimations and can only approximate the true size of a country’s informal sector. Moreover, more control variables can be added to the estimation in order to make the results more reliable. For example, controls for business climate, education and political stability could be introduced. Since both the timespan and resources for this research are limited, these variables could not be obtained and included, but they will undoubtedly add to the understanding of the dynamics of trade openness and informal sector size.

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Appendix 1: Definition of variables and their expected effect on the size of the informal sector

Variable Definition Expected

effect on the size of the informal sector Size of the informal

sector

Estimations of the size of the informal economy

--

Trade openness KOF index of globalization Negative

Trade Sum of exports and imports of goods and

services as % of GDP

Negative

FDI Direct investment capital flows as % of GDP

Negative

GDP GDP per capita in US$ Negative

Urbanization Share of population living in urban areas Positive

Unemployment Share of the labour force that is without

work but available for and seeking employment

Positive

Corruption Corruption Perception Index (CPI) Positive

Population density Midyear population divided by land area

in square kilometres

Positive

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Appendix 2: List of countries (N=120) Albania Algeria Angola Argentina Armenia Azerbaijan Bahamas Bangladesh Belarus Belize Benin Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Brunei Darussalam Bulgaria Burkina Faso Burundi Cambodia Cameroon Cape Verde Central African Republic Chad Chile China Colombia Comoros

Congo, Dem. Rep. Congo, Rep. Costa Rica Cote d’Ivoire Croatia Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Ethiopia Fiji Gabon Gambia Georgia Ghana Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary India Indonesia Iran Jamaica Jordan Kazakhstan Kenya Kuwait Kyrgyz Republic Lao PDR Lebanon Lesotho Liberia Libya Macedonia Madagascar Malawi Malaysia Maldives Mali Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Nicaragua Niger Nigeria Oman Pakistan Panama

Papua New Guinea Paraguay Peru Philippines Poland Romania Russia Rwanda Saudi Arabia Senegal Sierra Leone Solomon Islands South Africa Sri Lanka Suriname Swaziland Syrian Arab Rep. Tajikistan

Tanzania Thailand Togo

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Appendix 3: Skewness/Kurtosis test for normality

--- joint --- Variable Obs Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2

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Appendix 4: Correlation matrix

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Appendix 7: low and lower-middle income countries (N=66) Armenia Bangladesh Benin Bhutan Bolivia Burkina Faso Burundi Cambodia Cameroon Cape Verde Central African Republic Chad Comoros

Congo, Dem. Rep. Congo, Rep. Cote d’Ivoire Egypt El Salvador Eritrea Ethiopia Gambia Georgia Ghana Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras India Indonesia Kenya Kyrgyz Republic Lao PDR Lesotho Liberia Madagascar Malawi Mali Moldova Morocco Mozambique Myanmar Nepal Nicaragua Niger Nigeria Pakistan

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Globalization and the Urban Informal Economy in Developing Countries