ECON 499

Slums and Property Rights Exploring the Effect of the legal Regime on Urban

Poverty

Robbie Hill

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Background and discussion

As of 2001, it is estimated that almost 924 million people worldwide live in what are known as ‘slums’ (UN-HABITAT , 2003). This is an astounding figure, especially when it is conflated with the fact that within the next decade the percentage of global urban inhabitants will have increased to 56%, and the demand for urban dwellings will reach unprecedented levels. Urban areas will absorb all population growth over the next 40 years, as well as receiving some rural migration. Due to the natural population growth of developing urban areas alone, roughly 39,000 new units of housing will be required daily in order to provide appropriate shelter for growing urban populations (UN-HABITAT, 2002). Inevitably it will be the urban poor who find themselves disadvantaged by any potential housing shortage and thereby inhabiting informal or slum settlements. This is a challenge which many governments are ill equipped to face. Urban poverty associated with rapid levels of urbanization requires solutions which instead of impeding the urbanization trend, focus on other means of housing the urban poor.

There has been a substantial amount of research conducted on the subject of urbanization and the returns to productivity (Lucas, 2004) (Kumar, 2012). As economic activity shifts away from agriculture to modern services and manufacturing, the quality of life improves and economic growth is generally attained. Despite the importance of urbanization as a component of economic growth there is a lack of research concerning urbanization’s role in developing countries (Burgess, 2003). Perhaps due to this absence of discussion on the matter, urbanization is commonly seen as a malignant force that is unmanageable and causes several problematic consequences (Annez, 2009) (Cohen, 2006). The fear that negative externalities associated with higher concentrations of populations in urban centres would offset any welfare benefits brought about by productivity gains was widely discussed in the latter part of the twentieth century (Roberts, 1989) (Preston, 1979). This so called overurbanization continues to beleaguer developing nations as a large number of such countries are dissatisfied with the patterns of population distribution and consider changes in the distribution desirable (UN Department of Economic and Social Affairs, 2009). One of the problematic characteristics attached to rapid urbanization is urban poverty. Ravallion, Chen and Sangraula have made noteworthy progress in studying the urbanization of poverty (2007). They have found that during the period from 1993 to 2002, the urban share of the poor rose, indicating that the poor were urbanizing faster than the population as whole, even though the total number of poor decreased. The research of Ravallion et al. supports the idea that urbanization plays a role in overall poverty reduction, yet their findings also suggest that this progress comes at the cost of an increased incidence of urban poverty.

This increase in urban poverty can be attributed in part to most developing county’s inability to manage rapid urban growth. Consequently, slums, shanty towns and other informal housing situations are typical of many of these regions (Tannerfeldt, 2006). In order to combat the urban poverty associated with increased urbanization, many agencies as well as governments have attempted to regulate the urbanization process (Tannerfeldt, 2006). Generally this has not been a successful endeavour. Aside from being ineffective, policies which restrict individual movement are largely detrimental to individual welfare, as they place constraints on particularly the rural share of the population. It is clear that due to

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the inevitability and magnitude of urbanization trends in the developing world today, alternate policy avenues must be explored which curtail the proliferation of urban poverty and thus, the incidence of slums.

Slums are the physical manifestation of urban poverty (UN-HABITAT , 2003). In response to the high costs of formal housing relative to wages, the urban poor substitute out of proper housing and into informal slum dwellings. From the sprawling slum cities of the Indian sub-continent, to the infamous favelas of Brazil, urban poverty may take on different forms, but it is almost always spatially identifiable. Slums are heterogeneous across regions; however they bear several common traits which allow them to be identified scientifically and measured.

Slums as a Measure of Urban Poverty

Poverty is a subjective concept. Therefore there are a number of ways which it can be measures and modeled. Commonly, poverty is measured using either an income or expenditure measure (Ravallion M. , 1996). These measures take on different forms and rely on such benchmarks as poverty lines in order to judge severity or magnitude of poverty in a given country. Although widely used and informative in some respects, there is plenty of contention amongst economists surrounding which variant of this measure is more adequate for which purpose. Ravallion argues that even the best measures of poverty are incomplete and that non-income measures of poverty may provide a better assessment of welfare than traditional measures. This is due in part the assumption that a given income or level of consumption corresponds to a given level of utility which is homogenous for the entire population. This assumption has long been recognised as problematic in development research. What is being used as a measure of poverty should be relevant when considering its purpose. For example, a measure which includes access to irrigation, fertile land and proximity to markets would be useful to include in a measure of rural poverty. If one wished to measure the welfare of urban inhabitants in developing countries, then instead of employing an income based metric, it may be beneficial to utilize a measurement which captures more than just the income component of a welfare function.

The incidence of slums can be considered a measure of poverty which is multi-dimensional in its scope. In measuring slum dwelling percentage of an urban area, one indirectly captures not only income characteristics of poverty but aspects relating to the access of non-market goods; adequate living conditions and marginalization including crime and neglect.

Slums are generally formed in areas which are seen as undesirable for formal urban housing development. Thus they are often located in areas which are polluted or at risk from natural disasters. Combine this notion with the fact the slum dwellers often do not have access to a public safety net, and are isolated from electrical and sewer grids, recovering from disasters, illness and other harmful shocks is particularly difficult (Baker, 2008). Slum neighbourhoods are also most often not secured by any legal or enforceable agent. This adds an extra element of risk for the slum dweller: the risk of eviction. Even though the slum dweller may have an income comparable to his or her rural counterpart, the increased probability of eviction or expropriation of their habitation restrains them from investing in improvements in infrastructure as well as adds physiological strain.

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Another disadvantage slums hold over their occupants is the susceptibility to diseases resulting from high population density and inadequate waste management systems, as well as indoor pollution resulting from crude wood burning cook stoves operated indoors. In some African cities, HIV/AIDS infection rates exceed 50%. This number is often higher for those living in urban slums than in poor rural regions (Baker, 2008).

Further challenges slum dwellers face stem from the location of the slum relative to the formal job market, both physically and sociologically. The urban poor are usually forced to settle in areas which lay in the periphery of the city. Consequentially, the distances from where they reside to where formal employment exists near the center of the city, can be cripplingly extensive. For instance, the poorest neighbourhoods in Rio de Janeiro lay 30 to 40 kilometres outside of the city center, resulting in an average commute time of 3 hours per day1. Aside from the physical distance between the formal and informal sectors, there is the perceived one. Vast income inequality typical of urban areas in developing countries manifests itself in frustration and social barriers to education, employment and other aspects of society. Tension and violence may also result from the income disparity, especially considering the weak law enforcement institution in most developing regions. An example of this can be found in the naming of a slum clearing operation in Zimbabwe in 2005. The name of the operation refers to those dwelling in slums as “filth” or “dirt” (International Crisis Group, 2005), adjectives which dehumanise the poor and exemplify the dominant attitude towards the urban poor in many developing countries.

Given the broad array of characteristics and disadvantages of slums, it is clear that slums dwellers are faced with issues beyond merely having an income below the urban poverty line. Slum ratios are correlated with GDP per capita and the poverty gap (illustrated in Figures 1 and 2) yet also contain other unique characteristics which are discussed above that are not directly related to income or expenditure. For example, the national urban poverty head count ratio for India in 2005 was 26% (World Bank databank). That is the percentage of urban inhabitants who live below that national urban poverty line which is considered a minimum expenditure of roughly 20 Rupees a Day. The percentage of the urban population who lived in what are classified as slums in 2005 was 34% (UN MDG indicators).

Slums and Judicial Institutions

The link between urban poverty and slums implies that slum reduction efforts are indeed efforts to alleviate a particular measure of urban poverty. As mentioned earlier, a great deal of these efforts have focused on reducing the rate of urbanization. This response has generally only been effective when applied by authoritarian governments such as China. Apart from being ineffective, the policies are misguided when is come to promoting economic growth. A better approach would be to create an economic and legal environment which can allows for increased elasticity of housing supply in response to increased demand for affordable housing arising from urbanization.

One aspect of the economic environment includes the capacity of institutions to enforce contracts, legitimize property rights, and facilitate land ownership and transfers. The shortcomings of this aspect have been recognised by development economists to cause market failures (Besely, 1995), (Besley & 1 World Bank, 2000 “World Development Report 1999/2000.” Cited in Baker 2008.

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Maitreesh, 2009). The lack of established property rights can be shown to inhibit investment in land and infrastructure by increasing the probability of eviction or expropriation2. In Besley’s 1995 study of the effect of property rights in Ghana, granted land rights were found to have a positive effect on investment. In a related study, Erica Field analysed the effect of granting tenure to urban squatter communities (2005) and found that strengthening tenure security has a positive effect on investment. Increasing the return to marginal investment in residential dwellings and neighbourhoods is one method of improving the living standards of slum dwellers, however it does not rectify the problem in its entirety. The availability of formal affordable housing in the first place would decrease the incentive for households to build informal dwellings in urban areas.

Effective, state sponsored and enforced property rights are crucial for housing development. The ability to trade and transfer land, as well as to securely hold it when entitled, are the basis of a function housing market. Hence established property rights play a central role in the elasticity of affordable housing supply. It is reasonable to assume that developers are unwilling to build on land which they cannot purchase or sell without the risk of expropriation. An oppressive regulatory burden, which is often prevalent in developing countries, would also create a barrier to the housing development in a similar fashion as a lack of property rights would (Lall, Wang, & da Mata, 2006). Altering the legal and judicial environment to improve formal property rights, so trade and transfer of land can be garnered, and removing the regulatory burden may do far more to decrease the incidence of slums than attempting to halt any urbanization which may be occurring in developing countries.

Data and Descriptive Statistics

The United Nations Millennium Development Goals (UN MDG) were established in 2000 by leaders from 189 countries. The resolve was to create a measureable framework with which to gauge development progress in 8 different fields, ranging from combating HIV/AIDS to achieving universal education. In an effort to track progress towards achieving these goals, a large data set has been compiled. This data set includes information on the prevalence of slums in developing countries and is part of the indicators monitoring MDG 7: ensuring environmental sustainability. The data provides the proportion of urban population living in slums for over 59 countries from the years 1990 to 2009.

The UN MDG defines a slum household as a group of individuals living in the same dwelling which does not meet one or more of five different conditions shown in Table 1. Of particular interest is the category pertaining to security of tenure which is a sufficient but not necessary condition for designation as a slum. Besley and Field’s research regarding property rights corresponds to this criterion directly. The implications of their findings suggest that creating an investment friendly environment by granting and enforcing property rights encourages investment which improves other aspects of slum classifications. This is an example of how improvements in the legal and judicial environment of the country can reduce the number of slum households on paper. Another avenue though which legitimate property rights could decrease the number of slum dwelling is by reducing the costs to the housing supply side of the

2 See Besley 2009 for a formal model of the role property right play in limiting expropriation.

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market by allowing developers, either private or public, to confidently build and transfer ownership of low cost housing.

Category Criteria

Access to improved water

20 litres of drinking water a day per family without a high cost or effort involved in obtaining it

Piped connection

Access to improved sanitation

Connection to a sewer, septic tank, or other excreta disposal system

Access to a private or public toilet shared with a reasonable number of people

Sufficient living area (not over crowded) Fewer than 3 family members per room of a minimum size of 4 square meters

Structural quality/durability if tenure House is built on a non-hazardous location Structure is permanent enough to protect

inhabitants from the elements

Security of tenure All individuals are granted the right to

protection by the authorities Protection from forced evictions

Table 1 – UN-HABITAT 2003

The data shows a decline in the global percentage of slum population over the 20 years of data collection (see Figure 3). The was a decline during the 90s, however the gap between 2005 and 2009 shows a smaller decrease of the average slum percentage. This corresponds with a decrease in the world’s average urban population growth rate (Figure 4) and supports Ravallion et al’s findings (2009).

The incidence of slums is not homogenous across the world. Sub-Saharan Africa has the highest average percentage of slums across the years 1990 to 2009, and Middle Eastern/North African countries including Turkey have the lowest (Figure 5). This corresponds to average urban population growth rates for each region across time. Sub-Saharan Africa had an average growth rate 0f 2.09% while Arab countries and Turkey had the lowest average growth rate of 0.25%. Figure 6 shows a relationship between the natural log of the percentage of slum dwellers and the rate of urbanization. From this we begin to perceive a relationship between rate of urbanization and slum incidence which fits with Ravallion, Chen and Sangraula’s work regarding urbanization and poverty. Figure 8 gives a rough illustration of the change in slum percentage per continent over time. We see that Asian, Latin American and South American countries have decreased in average slum percentage over time, while Middle Eastern and North African countries (including turkey) have not.

The indicator used to measure property rights comes from the World Governance Indicators Project (WGI) produced by Daniel Kaufmann, Art Kraay and Massimo Mastruzzi. There are six separate indicators compiled from a variety of sources, including surveys, information from country experts, political and business risk rating agencies, think tanks and other non-governmental organizations (Kaufmann, Kraay, & Zoido-Lobaton, Governance Matters, 1999). The specific indicator used is the “rule of law” indicator which measures “incidents of both violent and non-violent crime, the effectiveness and

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predictability of the judiciary, and the enforceability of contracts (...) these indicators measure the success of a society in developing an environment in which fair and predictable rules form the basis for economic and social interactions” (Kaufmann, Kraay, & Zoido-Lobaton, Governance Matters, 1999). This indicator should gauge some of the states effectiveness regarding the issuance of property rights and contract enforceability which influences the supply of affordable housing in developing countries. Theoretical Basis for Slum Formation

The model proposed in this paper has slum formation as a function of poverty and the inability of urban areas to absorb and house urbanizing populations. The conflation of these two factors result in the informal squatter settlements we recognise as slums. The incidence of urban poverty increases with several factors including the GDP per capita level (Figure 2). It also increases with the inability of urban areas to house new residence. This consists of the pressure on the housing market from increase urbanization, as well as the inadequate legal framework hindering affordable housing production (Figures 6 and 7).

An unbalanced panel data set is constructed which allows us to eliminate any country specific effects which are time invariant. The data provides some limitations as the data for sums are only available for the years 1995, 2000, 2005, 2007 and 2009. The baseline model employs a fixed effects estimation process. This is due to the fact that there is only a 14 year span in which are observations take place, and any serial correlation issues are likely to be minimal in this shorter time period (Wooldrige, 2009).

The parsimonious baseline model is as follows:

퐿푁(푝푒푟푐푒푛푡 ) = 훿 + 훽 퐿푁(푎푣퐺퐷푃 , ) + 훽 푔푣푡푙푎푤 + 훽 푢푟푏푎푛푝푐푔 + 휀

Where 푝푒푟푐푒푛푡 is the percent of the urban population which lives in slum dwellings in country 푖 and year 푡. 훿 is the country specific fixed effect, 푎푣퐺퐷푃 , is the average GDP per capita in years 푡 and 푡 − 1, 푔푣푡푙푎푤 corresponds to the measure of ‘rule of law’ taken from the WGI, were -2.5 is the lowest score, and 2.5 is the highest possible score, corresponding to a more effective legal environment. Lastly, the rate of urbanization is given by 푢푟푏푎푛푝푐푔 . The natural log is taken of the 푝푒푟푐푒푛푡 variable in order to make interpreting the coefficients easier. The natural log is taken of the 푎푣퐺퐷푃 , variable in order to transform the variable in a linear relationship and to control the skew of the average GDP variable (Benoit, 2011).

We expect both the 훽 and 훽 coefficients to have a negative signs. If we assume slum formation to be a factor of poverty and imperfect housing markets, then reducing the poverty by increasing the GDP per capita would reduce the percentage of slum dwellers, and improving the legal aspect of the economy would result in improvements in the enforcements of contracts, and legitimacy of property rights thereby reducing inefficiencies in the housing supply. The 훽 coefficient is expected to have a positive sign, as we anticipate that higher urbanization rates result in increased strain the urban area’s absorption capacity.

Methodology and Results

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The results of this regression are shown in column 1 of Table 2. As expected, the estimates for rule of law and GDP per capita are negative, they are also very significant. Surprisingly, the rate of urbanization is not significant. The coefficient for 푔푣푡푙푎푤 is negative and significant which suggests a decrease in percentage of slum dwellers associated with an increase in the rule of law indicator.

1 2 3

Fixed Effects First Differencing Random Effects

(Intercept) - -0.03603 . 5.406965***

- (0.01935) (0.24093)

log(avGDP2y) -0.22397*** -0.05107 -0.26348***

(0.037173) (0.064921) (0.030896)

gvtlaw -0.21213** -0.18257* -0.22315***

(0.075134) (0.070929) (0.060268)

urbanpcgr -0.00841 -0.00723 0.008303

(0.016803) (0.012573) (0.01599)

R-Squared 0.18673 0.039652 0.36889 Table 2 – Standard errors in parentheses

In order to test the model with different estimation methods, first differencing and random effects processes have been employed and the results are displayed in columns 2 and 3 of Table 1. The estimation methods also yield a significant and negative coefficient for 푔푣푡푙푎푤. The first differencing and the random effects estimations produce similar estimates for 푔푣푡푙푎푤 however they are likely not the correct specification. Due to the shorter time span of the observations, the effects of any serial correlation would likely be minimal and therefore fixed effects are preferred over first differencing (Wooldrige, 2009). For a random effects model to be more efficient that a fixed effect model, the country specific fixed effects must be uncorrelated with the control variables in all time periods. This is likely not the case since the regressors are expected to be influenced by the characteristics of the country which are captured by 훿 , thus making the 퐶표푣(훿 ,푋) ≠ 0, where 푋 is any of the explanatory variables. If we conduct a Hausman test, which is used to distinguish between random and fixed effects models, the random effects estimation hypothesis is rejected.

The data in Table one is very rudimentary as it only includes the minimum number of repressors. It is appropriate to add more controls in order to verify the result. The same fixed effects method is used, however other variables are included. The newly introduced variables are: the average 2 year GDP per capita growth rate (avGDPg2y), the log of the percentage of the urban population (urbanpoppc) and three additional controls from the WGI project. The WGI variables are, a measure for government effectiveness (gvteff), corruption (gvtcor), and regulatory burden (gvtreg). The government effectiveness indicator measure perceptions of the quality of public service, bureaucracy, competence of civil servants , as well as other aspects of public institutions. The regulatory burden measure focuses on the measure of market-unfriendly policies, including price controls, inadequate banking supervision as well as perceptions of oppressive regulation in areas of foreign trade and business development. The final additional WGI indicator measures the perceived level of corruption in the county. The results of this regression are found in Column 1 of Table 3.

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Column 1 Column 2

Fixed Effects Feasible GLS

log(avGDP2y) -0.21217 *** -0.08411*

(0.049505) (0.03638)

avGDPg2y -0.00554 0.002944

(0.005021) (0.00218)

gvtlaw -0.27444** -0.14213*

(0.097881) (0.05841)

gvteff -0.1088 0.018238

(0.094789) (0.0521)

gvtcor 0.108524 0.06559

(0.080552) (0.04885)

gvtreg 0.101846 0.097882 .

(0.079953) (0.05157)

urbanpcgr -0.00659 0.001374

(0.017331) (0.01366)

log(urbanpoppc) 0.028141 -0.33035 .

(0.215938) (0.19437)

R-Squared 0.21048 multiple R-Squared 0.89468

Table 3

Including additional variables in the fixed effects model we find that the results are very similar to those found in the baseline model. The coefficient of the rule of law variable has not changed significantly, and remains between negative 0.2 and 0.3. The WGI indicators as well as GDP per capita are fairly correlated, so they are all capturing some of the same effects. However, they are not nearly perfectly correlated which suggests that there is some uniqueness to the controls which are being accounted for in the regression. Aside from The GDP per capita and the ‘rule of law’ controls, no other variables are significant.

As mentioned earlier, any serial correlation effect is likely to be very trivial due to the short time span. Regardless, a Durbin Watson test for serial correlation was carried out to test for any evidence of positive serial correlation. The conclusion of the test indicates that we con not reject the possibility of potential auto correlation in our fixed effects model. This is a violation of the of the auto regressive assumption (AR1). In response to this, feasible generalized least squares (FLGS) estimation is conducted.

The Feasible General Least Squares regression is used when we do not know the degree of correlation between the error terms over time. FGLS regresses 푢 on to 푢 for all 푡 = 1,2, . . ,푛 where u is the composite error term (푢 = 휌푢 + 휀 ) in order to find an estimate for 휌. Afterwards OLS is applied to the original model in order fin find estimates which are asymptotically valid. The FGLS estimator is not

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unbiased, however it is consistent when the error terms are weakly correlated over time, and it is more efficient than an OLS estimator when serial correlation exists (Wooldridge 2005). The results of the FLGS process are shown in column 2 of Table 3. Here we continue to see a significant negative relationship between the rule of law measurement and the log of the percentage of slum dwellers.

The data so far has not been separated and all countries in the sample remain in the data set. This may result in a weaker fit as there are no doubt large variations amongst different regions when it comes to the percentage of slum dwellers.

In order to establish coefficients for countries in specific geographical areas, the sample has been divided in to 4 continental regions. Sub-Saharan Africa (SSA) is the largest contributor to the sample with 30 countries included. Ten Asian counties are included, ranging from Mongolia and Pakistan to the Philippines. Latin American and South American countries (LACSA) are included together as a single region and include 11 countries. The smallest region in the sample are Arab and North Africa countries (ArabNA), including turkey. This group only consists of seven countries. The same fixed effects regression is used as our baseline model, except we include the added control variables as per Table 3.

The results of the fixed effects estimations on the separate regions are shown in Table 4. The rule of law coefficient remains significant to at most the 10 percent level of significance for all continents except for Asia. The effect of the rule of law indicator is particularly strong in the Arab and North African region. This region is examined more closely in Figure 6. The strong relationship is taken from Iraq, Syria and Turkey, where increasing the rule of law measure is associated with a dramatic drop in the percent of slum dwellers. Iraq is the sources of most of this association. This region is anomalous due in part to the fact that there are very few countries in the sample, and that the countries involved are affected by exogenous factors. Apparent examples of this are the wars which have taken place in Iraq of the course of the observation period. Turmoil caused by conflict can affect the slum dwelling population, as the number of internally displaced people increase, as well as the deterioration in the rule of law. The Arab, North African region is also unique in that it is the only region where the GDP per capita does not have a significant effect on the percentage of urban population who live in slums. This may have something to do with the style of the political economy in most of those countries. For instance, Syria, Egypt, Jordan and partially Iraq have authoritarian governments which control most of the economy.

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Fixed Effects Regressions

SSA ASIA LACSA ARABNA

log(avGDP2y) -0.26439*** 0.048623 . -0.34027** -0.37252

(0.062783) (0.024568) (0.107838) (0.228298)

avGDPg2y -0.00132 -0.00293 -0.00071 -0.00447

(0.005839) (0.003147) (0.008428) (0.010186)

gvteff -0.18107 0.038692 -0.00882 0.966836

(0.114108) (0.045691) (0.174526) (0.554564)

gvtreg -0.29943** 0.002941 0.339039* 0.853079*

(0.100352) (0.045687) (0.123055) (0.359483)

gvtlaw -0.36449** 0.002971 -0.31925 . -1.5973*

(0.124528) (0.046424) (0.166746) (0.51032)

gvtcor 0.224497* 0.002796 0.242186 . 0.861511 .

(0.094694) (0.03778) (0.141953) (0.399926)

urbanpopgr -0.00168 -0.00369** 0.00294 0.080525*

(0.001437) (0.001083) (0.005772) (0.032316)

log(urbanpoppc) 0.736644** -0.3142* 0.046265 3.581777

(0.253165) (0.11898) (0.272621) (3.489907)

R-Squared 0.40508 0.47231 0.69713 0.93703

observations 131 46 48 23 Table 4

The effect of the ‘rule of law’ may be prevalent amongst countries which share similar macroeconomic traits. This is especially true when we consider the heterogeneity of characteristics across countries located in the same geographical region. Asia is a good example of this. Pakistan and the Philippines have very little in common apart from the fact that they are located in the same broad Asian region. Therefore, separating the sample countries by income per capita and their equality may reveal exhibit information regarding the relationship between the explanatory variables and slums in difference economics situations.

The 59 countries are divided in to three categories determined by their relative GDP per capita. The categories are crudely based on the distribution of GDP per capita (Figure 7). There first third percentile contains countries with GDP per capita less than 4633. The second group contains countries with GDP per capita greater than or equal to 463 and less than 1308. The last third of contains countries with GDP per capita greater than or equal to 1308. There are cases where countries are counted in one income group in one year, and in a higher or lower income group in the next if the cross the boundary between groups in terms of GDP per capita. The results from the fixed effects regression are shown in Table 5 and show that changes in the rule of law measure are significant for countries with a GPD per capita between 463 and 1308, or the middle income countries of the sample.

3 All GDP per capita is measured in current US$

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Lowest Third Middle Third Highest Third

log(avGDP2y) -0.00709 -0.21083 -0.40004***

(0.03989) (0.082405) (0.105076)

avGDPg2y 0.003023 0.010267 . -0.00422

(0.004055) (0.005973) (0.006528)

gvteff -0.15268** -0.25381 . 0.244592

(0.052305) (0.150294) (0.161468)

gvtreg 0.06619 0.660328*** 0.180789

(0.043112) (0.138857) (0.129623)

gvtlaw -0.02184 -0.46269** -0.05438

(0.049018) (0.170924) (0.146153)

gvtcor 0.105343* 0.216332 0.070192

(0.043489) (0.130714) (0.171221)

urbanpopgr 0.000433 0.002322 0.002912

(0.000553) (0.005643) (0.403134)

log(urbanpoppc) -0.45351*** 0.285099 0.35831

(0.098921) (0.642609) (0.006676)

R-Squared 0.56268 0.51513 0.58762

obervations 90 81 81 Table 5

Lastly, the sample countries are divided in to two groups based on the level of inequality given by their respective Gini index for each year. The boundary between high-inequality .and low-inequality is at a Gini coefficient of .43, with the relatively more equal countries’ coefficients less than or equal to 0.43. The usual fixed effects estimation is applied to the two sample groups in order to eliminate any time invariant effects. The results of the regressions are presented in Table 6. From these results we find that there appears to be a significant negative effect of ‘rule of law’ for the countries with relatively higher inequality. For the more equal half of the countries, the legal environment does not appear to have a relationship with the percentage of urban dwellers that live in slums.

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Gini > 0.43 Gini ≤ 0.43

log(avGDP2y) -0.21449 -0.21998 .

(0.084135) (0.109286)

avGDPg2y -0.00043 -0.00861

(0.008244) (0.010647)

gvteff -0.01885 0.023539

(0.188581) (0.173078)

gvtreg 0.366834* 0.062383

(0.132958) (0.174878)

gvtcor 0.398017* -0.152

(0.15695) (0.151961)

gvtlaw -0.41907* 0.25217

(0.17971) (0.212761)

urbanpopgr 0.00123 -0.00144

(0.001675) (0.007127)

log(urbanpoppc) 0.167738 -0.48761

(0.382768) (0.580903)

R-Squared 0.61678 0.4717

Observations 59 63 Table 6

Robustness and Interpretation

The theoretical model of slum formation presented above, where the incidence of slums is a function of poverty and the unavailability of affordable housing, gives a framework for interpreting the results of the regression analyses. The original fixed effects model with the limited control variables suggests that a percent increase in GDP per capita lowers the slum dwelling urban population by a fraction of a percent. This is expected, and is seen in the results for most of the models. The GDP per capita variable captures several important aspects of the economy which may influence the number of slums dweller inhabiting urban areas. Firstly, any major investments made in infrastructure or housing would likely be included in the GDP, therefore the GDP per capita measure would be also be affected. Secondly, the GDP per capita is also a measure for how rich the country is. There higher the GDP per capita, the lower the general level of poverty, and consequently, the lower the incidence of slums (Figure 2). The impact of a higher GDP per capita is observed for all continental regions except for Arab and North African Countries including Turkey. This may have to do with the smaller sample size for that region. It also may be indicative of any regional effects that may be common amongst those countries. For example, the authoritarian style of governments for countries like Syria, Iraq and Egypt may influence this. Also, the low incidence of slums in this region (Figure 5) may make slum reduction not much of a priority for the authorities.

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The variable for WGI’s ‘rule of law’ indicator had a significant and negative effect in the baseline model, and was robust for the three different variations of the model. The ‘rule of law’ indicator was also persistent when the control variables were included for the fixed effects estimation as well as for the feasible GLS estimation. This fits within the theoretical framework of slum formation. The ‘rule of law’ WGI indicator includes elements of contract enforcement, judicial rulings and property rights. As the WGI indicator increases, the legal environment in the country improves and there are at least two potential means by which this can reduces slum formation. Firstly, through any improvements made in the field of property rights, including granting tenure to slum dwellers which would decrease the number of slums counted on paper. This is an idiosyncrasy resulting from the nature of the slum data. A second way in which increasing the ‘rule of law’ measure could reduce the number of slum dwellers is by increasing the elasticity of the housing supply, by allowing public and private developers to by land, build cheaper homes, and transfer the property with a decreased risk of expropriation.

The ‘rule of law’ indicator is robust to at most a 10% level of significance for all variations of the model and continental region samples, excluding Asian countries. This may be due in part the fact that all the several other explanatory variables are correlated with the ‘rule of law’ indicator and this may lead to decrease in the significance of the variables resulting from increased standard errors. This may also occur if there are redundant variables included in the model. Another reason why we do not observe the expected effect for Asian countries is that slum settlement in some Asian countries may be less responsive to change in property rights. Also, the country diversity across the region is quite large in comparison to the other regions. Institutions in place in Pakistan and Mongolia are vastly different from institutions in Vietnam and the Philippines and this may cause the expected effects of a change in the legal environment of the country to be washed out.

The lack of a significant effect for the lower and higher income groups could stem from the fact that there are other factors besides the legal environment which may be causing inelasticity of affordable housing supply. For the lower income countries, regardless of changes to the property rights regime in the country, there are likely other factors which create risk for affordable housing developers, or impede any investment in housing. Factors such as natural disasters, conflict and a lack of available civic infrastructure to build on may constrict the effect any changes in the legal state of the nation would have on housing.

The lack of significance in the highest income group may signify an upper bound to the potential of the legal institutions of the country to have any impact on the housing supply. For the richer counties with a higher incidence of urban slums, there may be deeper institutional traits which need to be rectified before further slum reduction can take place. A similar situation may partially explain why there is no observed effect of changes in ‘rule of law’ indicator for countries with lower inequality. Countries with lower inequality may not have the same urgency to reduce the percentage of slum dwellers in the urban areas as areas with higher inequality. In a low income country with low inequality, most people would either live in a slum neighbourhood or be in close contact with one. If most people live in informal squatter neighbourhoods or slums, slum reduction may not seem like a priority.

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Conclusions, Limitations and Improvements

The findings in the paper lend some cautious support to the proposed theoretical model for slum formations. The two factors influencing the share of the urban population who live in slums are poverty and the ability of the urban area to absorb and formally house the impoverished. The level of GDP per capita, which partly accounted for the level of poverty in the country, had a strongly significant and robust effect on the reduction of slums. The ability of the urban areas to absorb people into formal housing is partly captured by the WGI indicator for ‘rule of law’ by measuring the effectiveness of property rights, contract enforceability as well judicial rulings. The results show that this is negatively associated with the incidence of slums for all regions except for Asia, for countries with GDP per capita between 463 and 1308, as well as sample countries with relatively higher inequality. We also see that there this finding is robust in a variety of estimation methods of our standard model, including random effects as well as feasible GLS estimation. The policy implications of these finding suggest that by improving the effectiveness of legal institutions such as contract enforcement and property rights, authorities can reduce the incidence of urban poverty associated with the prevalence of slum households in urban areas. This policy direction is possibly more effective that impeding the rate of urbanization, which had a trivial effect on the percentage of slums as shown by the results.

Caution must be exercise in drawing conclusions from these findings. Certainly there a vast number of factors which influence the slum dwelling share of the urban population, only a fraction of which can be controlled for. A shortcoming of this study is the availability of data. Reliable data for developing countries is a scarce resource. Ideally much more variables would be included, for example the number of refugees in the country, the amount spent on improving infrastructure, the natural rate of population increase in slums versus the rest of the urban population and the level of government indebtedness would all be useful in explaining the incidence of slums. Data such as this were available for some countries, however not enough was collected to have a meaningful sample.

As with all meta-data, measurement error is a problematic possibility worth mentioning. Measuring slums in any country is a task rife with potential for errors omissions and contradictions. This paper placed a great deal of trust into those numbers as well at the WGI project’s data. These aggregates likely suffer from similar predicament regarding measurement error.

There is also a risk of endogeneity in the variables, particularly with the GDP per capita measure. A slum containing a large pool of inexpensive labour close to the urban centres would likely have a positive effect on the GDP of a country. This is not corrected for in this paper for two reasons. One is that the inhabitants of slums generally operate outside of the formal economy. They generally do not pay tax and their incomes go unnoticed by the national accounts, thus their contribution to the economy is not directly included in the GDP. Secondly, this paper is focused on the effect of the legal environment on the percentage of slums. It is not a theoretical stretch to consider that slum prevalence decreases with increased GDP per capita and therefore, simply observing the elastic of slum percentage to GDP is adequate.

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Reducing urban poverty is a formidable task in the face of the developing world’s unprecedented urbanization. Tackling a subjective matter such as urban poverty requires an adequate and meaningful means by which to measure such a dilemma. The inhabitation of slums and informal housing situations in urban areas can be detrimental to the occupant’s welfare aside from the associated lower levels of income. Using the percentage of the urban households who inhabit slums as a measure of urban poverty which encompasses a broader scope than income or expenditure alone, one can begin to analyse the effect of various factors through comparative statics. This paper has attempted to delineate a relationship between the effectiveness of the legal and judicial institutions in a country and the prevalence of urban poverty as exemplified by the incidence of slums in its urban centres. Although not conclusive, the findings of this paper will hopefully serve to motivate further study in this critical field of development economics.

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Works Cited Annez, P. C. (2009). Urbanization and Growth: Setting th eContext. In M. P. Spence, Urbanization and Growth (pp. 1-47). Washington DC: World Bank.

Baker, J. L. (2008). Urban Poverty: A Global View. World Bank .

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Besley, T. (1995). Property Rights and Investment Incentives: Theory and Evidence from Ghana. Journal of Political Economy , 903-937.

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Kaufmann, D., Kraay, A., & Zoido-Lobaton, P. (1999). Governance Matters. World Bank Development Reseach Group .

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Preston, S. H. (1979). Urban Growth in Developing Countries: A Demographic Reappraisal. Population and Development Review , 195-215.

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UN-HABITAT . (2003). The Challenge of Slums. London: Earthscan Publications.

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Data Sources

Kaufmann, D., Kraay, A., & Mastruzzi, M. (n.d.). Worldwide Governance Indicators. Retrieved April 23, 2013, from http://info.worldbank.org/governance/wgi/index.asp

UN Statistics. (n.d.). Retrieved April 23, 2013, from Millenium Development Goals Indicators: http://data.un.org/Data.aspx?d=MDG&f=seriesRowID%3A711

World Bank . (n.d.). World Bank DataBank. Retrieved 04 11, 2013, from World Bank: http://databank.worldbank.org/data/home.aspx

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Figures

Figure 1

Figure 2

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Figure 3 – UN-MDG slum data

Figure 4 – World Bank DataBank: Urban Population Growth

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Figure 5 – 1:Sub-Saharan Africa, 2: Arab countries and Turkey, 3: Asian countries, 4: Latin and South American Countries

Figure 6

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Figure 7

Figure 8

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Figure 7

Heterogeneity across Continents and Time

Figure 8 –black line: Sub-Saharan Africa countries, green: Asian, Blue: Latin and South American, red: Middle East

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List of Countries in Sample

Angola China Ghana Lesotho Nicaragua Syria Argentina Colombia Guatemala Madagascar Niger Tanzania Bangladesh Comoros Guinea Malawi Nigeria Thailand Benin Congo, Dem. Rep. Guyana Mali Pakistan Turkey Bolivia Congo, Rep. Haiti Mexico Peru Uganda Brazil Cote d'Ivoire India Mongolia Philippines Vietnam Burkina Faso Dominican Republic Indonesia Morocco Rwanda Yemen, Rep. Cameroon Egypt, Arab Rep. Iraq Mozambique Senegal Zambia Central African Republic Ethiopia Jordan Namibia Somalia Zimbabwe Chad Gambia, The Kenya Nepal South Africa