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# **Declining Inequality in Latin America in the 2000s: The Cases of Argentina, Brazil, and Mexico**<sup>**1**</sup>

# **Nora Lustig**

Tulane University

**Luis F. Lopez-Calva** The World Bank

# **Eduardo Ortiz-Juarez**<sup>**2**</sup>

UNDP, Regional Bureau for Latin America and the Caribbean

**JEL classification:** D31, I24, H53, O15, O54

**Keywords:** Income inequality, skill premium, government transfers, progressivity, Latin America.

**Sector Board** : POV

> 1 Forthcoming in _World Development_ . This article draws from the original UNDP-sponsored project “Markets, the State and the Dynamics of Inequality in Latin America” coordinated by Luis F. Lopez-Calva and Nora Lustig. The authors are grateful to Francisco Ferreira for his comments on an earlier draft, participants at the workshop organized by Augusto de la Torre, Latin America Chief Economist at The World Bank, and to Andres Castañeda, Samantha Greenspun and Sean Higgins for their valuable research assistance. The authors are also very grateful to anonymous referees for their very helpful comments.

> The views expressed here may not represent that of the institutions with which the authors are or were associated. Errors and omissions remain the authors’ responsibility.

> 2 Nora Lustig (nlustig@tulane.edu) is Samuel Z. Stone Professor of Latin American Economics at Tulane University and non-resident fellow at the Center for Global Development and the Inter-American Dialogue. Luis F. Lopez-Calva (lflopezcalva@worldbank.org) is Lead Economist at the Poverty and Gender Unit, Latin America and the Caribbean, The World Bank. Eduardo Ortiz-Juarez (eduardo.ortiz@undp.org) is Economist at the Regional Bureau for Latin America and the Caribbean, UNDP and Consultant at the World Bank.

1
internationally comparable value and perception surveys, with a relatively good coverage of high, middle, and low income countries. However, the World Value Surveys are not dedicated labor force surveys and many of the employment categories used in this analysis are not as carefully defined and tested as in other data bases on labor market outcomes. More importantly, the surveys used in this paper do not enable us to present causal support for the association between labor market outcomes and individual experiences of social cohesion (in particular we cannot distinguish whether individuals who are out of work or in lower quality job report lower levels of social and subjective wellbeing because of their employment situation, or because they had poorer subjective and social outcomes to start with). Nonetheless, we hope that non-causal statistical associations presented in this paper can offer initial indications or trigger future research on how labor market outcomes influence social cohesion in different parts of the world.

# **II. Jobs and social cohesion**

The literature has employed multiple, often loosely defined concepts of social cohesion, incorporating, to various extents, notions of social inclusion and exclusion, participation, and social and political rights. Yet, one theme that runs through most contributions involves concerns about the undermining of the social fabric by economic and social transformations. In particular in the last decades these concerns have often been related to rapid economic globalization and its potential consequences for job security, wage inequalities, and social and cultural distances between groups with uneven ties to the labor market (Norton and De Haan 2012, OECD 2011).

This paper follows the WDR’s framework on social cohesion in that it emphasizes the way societies and groups manage possible collective action problems arising from economic and social transformations in and around national labor markets.<sup>1</sup> In addition, we emphasize the distribution of social wellbeing outcomes within societies.<sup>2</sup> We believe that this definition provides a useful starting point for highlighting different manifestations and experiences of social cohesion among various groups and individuals with different levels of attachment to the labor market.

With this definition of social cohesion in mind it is possible to think of several ways in which labor market outcomes may contribute to social stability. Starting from

> 1 This follows Woolcock (2011) who defines social cohesion as “the capacity of societies (not just groups, networks) to peacefully manage collective action problems.”

> 2 This brings our definition of social cohesion closer to that of the OECD (2011), which describes a society as cohesive if it “works towards the well-being of all its members”.

3
Policy Research Working Paper 9902 COVID-19 and Economic Inequality Short-Term Impacts with Long-Term Consequences _Ambar Narayan Alexandru Cojocaru Sarthak Agrawal Tom Bundervoet Maria Davalos Natalia Garcia Christoph Lakner Daniel Gerszon Mahler Veronica Montalva Talledo Andrey Ten Nishant Yonzan_ Poverty and Equity Global Practice & Development Data Group January 2022
Policy Research Working Paper 6694

# **Abstract**

Few would contest that teachers are a very important determinant of whether students learn in school. Yet, in the face of compelling evidence that many students are not learning what they are expected to learn, how to improve teacher performance has been the focus of much policy debate in rich and poor countries. This paper examines how incentives, both pecuniary and nonpecuniary, influence teacher effort. Using school survey data from Lao PDR, it estimates new measures of teacher effort, including the number of hours that teachers spend preparing for classes and teacher provision of private tutoring classes outside class hours. The estimation results indicate that teachers increase effort in response to nonpecuniary incentives, such as greater teacher autonomy over teaching materials, and monitoring mechanism, such as the existence of an active parent-teacher association and the ability of school principals to dismiss teachers. Methodologically, the paper provides a detailed derivation of a simultaneous ordinary least squaresprobit model with school random effects that can jointly estimate teacher work hours and tutoring provision.

This paper is a product of the Poverty and Inequality Team, Development Research Group; and Education Sector, Human Development Network. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at hdang@worldbank.org and eking@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **VII. Conclusions**

We have updated the small area estimates of poverty and inequality for rural Vietnam, where existing poverty maps were outdated. These new estimates of province and district level poverty for the year 2006 allow us to examine how poverty has changed in Vietnam over the last seven years.

Vietnam has seen a remarkable reduction in (rural) poverty during the period 1999-2006. Poverty has been declining in virtually all provinces across the country. The largest improvements are observed for provinces with poverty rates close to the national average. It is found, however, that the poorest provinces have shown the lowest rates of improvements, i.e. were least successful in reducing poverty. The areas with some of the highest poverty rates are also more likely to be the areas with higher shares of ethnic minorities, which as a group are seeing below average reductions in poverty. There is found to be a noticeable gap in household endowments as well as returns to these endowments between ethnic minorities and the Kinh/Chinese people in Vietnam (see Baulch et al., 2008).

While national inequality seems to be increasing, our estimates of rural inequality within provinces and districts are relatively low. This seems to indicate that inequality is largely driven by inequality between local areas rather than within local areas. As expected, income inequality is higher than expenditure inequality. Interestingly, inequality tends to be higher in areas with relatively low poverty areas as well as in areas with relatively high poverty rates. Also, we find that provinces and districts which experienced a larger poverty reduction during the period 1999-2006 are more likely to have a lower level of inequality in 2006.

Policies that may benefit from having small area estimates of poverty and inequality include: (a) cash transfers and income support programs; (b) local government support and community development programs investing in e.g. health care, infrastructure, education, labor markets, agricultural productivity and micro finance; (c) food-and-cash for work programs; (d) fund raising and donor coordination; and (e) evaluation of country strategies, and the monitoring of progress towards millennium development goals (MDGs).

To take full advantage of the poverty maps, in particular of their policy relevance, it is key that they are accessible to a wide range of policy makers that include local entities as well as high level officials. It is not uncommon that public institutions, many of which may be potential users, are left largely unaware of the results from the poverty mapping exercise and their potential applications. Also important is that outdated estimates are replaced with up-to-date estimates of poverty and inequality.

23
1995, Burundi’s interim president, Sylvere Ntibantunganya, announced the start of a genocide on Belgian television (Chrétien and Mukuri, 2000). By late 1995, acts of violence took place in the central provinces of Gitega and Muramvya and the northern province of Kirundo. The situation at the end of 1995 is depicted in Figure 2. By then, the conflict had spread to almost all of the provinces of Burundi, with the exception of Cankuzo (in the east of the country) and Rutana and Makamba (in the south of the country).<sup>12</sup> On July 25, 1996, former president Buyoya seized power again in a bloodless coup d’état backed by the army. During the second half of 1996 and the first half of 1997, armed confrontations continued, especially in the provinces of Kayanza, Muramvya, Kirundo and Gitega. Meanwhile in April 1997, the Arusha Peace talks between the principal parties engaged in the conflict began. As of late 1997, insecurity increased again in Cibitoke, Bubanza and Bujumbura rural, provinces which remained unsafe until 1999.

Although the Arusha peace negotiations were successfully concluded in August 2000, and a transitional government was established in 2002, it was not until 2003 that Burundi entered a period of fragile peace.<sup>13</sup> By 2005, a new constitution was approved and communal and parliamentary elections were held. In September 2006, the final rebel group, FNL, signed a cease-fire agreement with the government finally ending the civil war. Coupled with this political stability, a series of structural reforms were implemented to stabilize the economy, and post-2000 GDP growth rates averaged 2.7 percent per year. Despite these political and economic improvements after 2000, health issues in general and child health in particular remain a major concern in post-conflict Burundi.

# **3. 1998 Burundi Priority Survey**

# _3.1 Data Overview_

The Burundi Priority Survey took place between October 1998 and March 1999 and was organized by the Burundi Institute of Statistics and Economic Studies in cooperation with the World Bank. The survey’s main goal was to evaluate the country’s socioeconomic situation following five years of civil war in order to design an efficient poverty alleviation policy (Republic of Burundi, 1998). The survey was designed to be nationally representative, but

> 12 By late 1998, Makamba was severely impacted by insurgents operating from neighboring Tanzania. Due to the generalized insecurity in Makamba at the time of the survey, no data were collected (Republic of Burundi, 1998). 13 Since the 2000 Arusha agreements were not signed by the two main rebel groups, CNDD-FDD and FNL, these agreements had no real impact in the field.

5
- 74 -

# Table 25: INTERNAL RATES OF RETURN FOR VTE

||Secondar<br>Level|Post-<br>y Secondar<br>Level|y Teacher<br>Training|Apprenticeships|
|---|---|---|---|---|
|Social Rate of Return|||||
|Base Case|3.86%|4.37%|3.77%|2.34%|
|Costs+25%|2.66%|3.18%|2.50%|1.23%|
|Costs- 25%|5.54%|6.04%|5.54%|3.87%|
|Private Rate of Return|||||
|Base Case|15.84%|21.24%|15.15%|2.34%|
|Costs+25%|13.51%|18.28%|12.89%|1.23Z|
|Costs- 25%|19.35Z|25.71%|18.51%|3.87%|
<!-- Start of picture text --> 1400 100<br>1000 m@ Public. sector 80<br><!-- End of picture text --> <!-- Start of picture text --> 600 40<br>400 :<br>20 | | @ Public sector<br>200 | | TM Private sector<br>0) 0)<br>2012 2016 2012 2016<br><!-- End of picture text -->

<!-- Start of picture text --> 100 : =<br>: preannounced minimum<br>? wage adjustments<br>60 : ——Employees<br>40 : -—Employed persons<br>: except employees<br>20 ?<br>: ——Unemployed persons<br>fe) .<br>DD oO a Nim t+ w wo ~<br>oO a =i odicd a a San | ao<br>0 0 0 0:0 0 0 0 0<br>N N N NN N N N N<br><!-- End of picture text -->
Moreover, with the exception of females, who were generally less affected than males,<sup>3</sup> we find no evidence that the intervention had differential effects across a series of social and demographic indicators, including age, caste, membership in a social organization, and educational achievement of respondents and their parents. The general lack of heterogeneous effects for different social status indicators suggests that social media may be an effective medium for delivering social norms campaigns to vulnerable populations, who may find it harder to participate and have a voice in community events.

Our study contributes to various streams of literatures. Recent work emphasizes the use of marketing techniques to achieve sustainable behaviors (Chandy et al. 2021; Constantinides 2014), particularly changing social norms (Burchell et al. 2013; McKenzie-Mohr 2000). Our findings provide new evidence that social norms campaigns that use edutainment formats can trigger immediate shifts in individuals’ attitudes towards gender norms and VAW. The study also highlights the potential of expanding off-the-shelf social media tools to achieve and assess development impacts through social marketing. Evaluating the effectiveness of social media campaigns with development objectives is often complicated, with recent studies (Shawky et al., 2019) almost exclusively relying on short-term engagement measures (e.g., likes, sharing, website activity). In this study, we use a newly developed survey chatbot called Virtual Lab (Rao et al., 2020) that can be integrated into social media platforms to deliver and measure the impact of online campaigns on outcomes beyond standard engagement measures.

The study also contributes to the literature on reshaping attitudes towards gender norms and VAW. The importance of social norms in perpetuating gender gaps has received recent attention by scholars (Alesina et al. 2013; Bertrand 2020; Bertrand et al. 2015). Studies emphasize the impact of gender stereotypes on educational outcomes (Alan et al. 2018; Carlana 2019; Lavy and Sand 2015; Terrier 2015) and belief distortions (Bordalo et al., 2019), as well as the importance of correcting gender misperceptions for female labor participation (Bursztyn et al., 2020). We add to this literature by showing that edutainment interventions that question existing social norms can tackle permissive attitudes towards VAW.

Relatedly, and in contrast to Jewkes et al. (2020) and Kerr-Wilson et al. (2020), our study adds to Arias (2019), Banerjee et al. (2019 _a_ ) and Green et al. (2020), and shows that highquality, stand-alone edutainment can be a low-cost approach for reshaping attitudes and preventing VAW in developing countries. Our study also sheds light on the mechanisms through which edutainment works when applied to a new medium. While our design cannot

> 3Attitudinal impacts across genders are mixed in previous VAW edutainment studies, with the Nigeria (Banerjee et al., 2019 _a_ ) and Uganda trials (Green et al., 2020) generally finding stronger impacts for men and women, respectively.

3
# **4 Conclusion**

This paper evaluates the use of three tree-based machine learning techniques and linear empirical best predictor (EBP) models for the purposes of contemporaneous small area estimation of wealth and poverty using household census data and publicly available geospatial indicators from seven countries. In addition to evaluating accuracy, we propose and implement a bootstrap algorithm to estimate the uncertainty associated with machine learning predictions and evaluate its performance using real-world data. Two of the three treebased machine learning methods evaluated – Cubist Regression and Extreme Gradient Boosting (XGBoost) significantly outperformed the empirical best predictor model traditionally used for small area estimation (Molina and Rao 2010; Tzavidis et al. 2018). A third machine learning method, BRF, generated estimates comparable to EBP. Both point estimates and uncertainty estimates generated using Cubist regression models are generally a bit less accurate than those generated using XGBoost.

The results make a strong case for the use of XGBoost or Cubist regression in cases where the sample includes a sufficiently large number of subareas, those subareas comprise a small share of subareas in the population, and transparency and parsimony are not first-order concerns. In general, in these contexts, it does not appear that the benefit of EBP, in terms of conditioning on the sample data, outweighs the restrictions of its linear function form. In addition, the acccuracy of BRF estimates in this context likely suffers from using random subsets of predictors and different subsets of the data for training and prediction. Nonetheless, appropriate diagnostics, such as estimating linear models, interpreting the results of machine learning models, and evaluating results using cross-validation techniques, remain crucial in practice. Furthermore, accuracy is not always the most important factor when selecting methods. In cases where transparency and parsimony are important, or the number of sampled subareas is small, linear mixed EBP models or Cubist regression with a small number of rules are viable options. We do not claim that these results hold for all types of samples and settings. While recent research has made some progress towards combining tree-based machine learning with conditional random effects (Krennmair and Schmid, 2022; Messer and Schmid, 2024), these have yet to gain widespread acceptance and use. Our results highlight potential benefits from this line of research.

One notable finding, which has also been observed in other contexts, is the significantly greater accuracy of estimates in sampled areas than non-sampled areas. However, XGBoost appears to be markedly more accurate than other methods in predicting out-of-sample estimates. These differences diminish when comparing accuracy estimates within the same area across simulations. This shows how the tendency for samples to under-represent less densely populated areas can lead to less accurate predictions out of sample, even when taking sampling probabilities into account. This finding underscores the benefits of including all target areas 41
36

# **Appendix Figure 1 (continued). Global Trade Reduction Indexes, 1960 to 2004**

(percent)

# (c) Tropical crops

<!-- Start of picture text --> 160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>‐20<br>1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04<br>Sugar Cotton Coconut Coffee Rubber Tea Cocoa<br><!-- End of picture text -->

# (d) Livestock products

<!-- Start of picture text --> 160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>‐20<br>1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04<br>Pigmeat Milk Beef Poultry Egg Sheepmeat Wool<br><!-- End of picture text --> Source: Authors’ calculations based on NRA and CTE estimates in Anderson and Valenzuela (2008).