metadata: authors: 0: author: Fernandez Romero,Jaime Estuardo
1: author: Olivieri,Sergio Daniel
2: author: Sanchez Castro,Diana Marcela
count: Latin America
docna: 0: docna: A Methodology for Updating International
            Middle-Class Lines for the Latin American and Caribbean Region
docty: Policy Research Working Paper
abstracts: cdata!: The middle class in Latin America and
            the Caribbean has been a central focus of policy debates in
            the region since the COVID-19 pandemic began. To identify
            and track vulnerable and middle-class populations
            accurately, it is necessary to update the upper and lower
            bounds for the middle class using 2017 purchasing power
            parity exchange rates. This paper contributes with a
            two-step methodology for updating these thresholds. The
            method indicates that updating the $13 lower-bound line in
            2011 purchasing power parity dollars to 2017 purchasing
            power parity dollars results in a vulnerability line of $14.
            The study also finds an upper bound of $81 per person per
            day in 2017 purchasing power parity, compared with $70 in
            2011 purchasing power parity. These thresholds are robust to
            a variety of assumptions and methodologies. The results of
            this study indicate that the proportion of the population in
            Latin America and the Caribbean classified as middle class
            increased from 36.3 percent in 2011 to 37.2 percent in 2017.
            However, there were no significant changes in the
            characteristics of this group.
display_title: A Methodology for Updating
            International Middle-Class Lines for the Latin American and
            Caribbean Region
listing_relative_url: /research/2023/05/34063509/methodology-updating-international-middle-class-lines-latin-american-caribbean-region
topicv3: Poverty and the Middle Class m1327183 1153,Household Surveys m1504071 2761
publishtoextweb_dt: 2023-05-16T00:00:00Z
dois: 10.1596/1813-9450-10447
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1783347
1: author_key: 926510
2: author_key: 1342490
model_extractions: Policy Research Working Paper 10447 A Methodology for Updating International Middle-Class Lines for the Latin American and Caribbean Region _Jaime Fernandez Sergio Olivieri Diana Sanchez_ Poverty and Equity Global Practice May 2023
Policy Research Working Paper 10447

# **Abstract**

The middle class in Latin America and the Caribbean has been a central focus of policy debates in the region since the COVID-19 pandemic began. To identify and track vulnerable and middle-class populations accurately, it is necessary to update the upper and lower bounds for the middle class using 2017 purchasing power parity exchange rates. This paper contributes with a two-step methodology for updating these thresholds. The method indicates that updating the $13 lower-bound line in 2011 purchasing power parity dollars to 2017 purchasing power parity dollars results in a vulnerability line of $14. The study also finds an upper bound of $81 per person per day in 2017 purchasing power parity, compared with $70 in 2011 purchasing power parity. These thresholds are robust to a variety of assumptions and methodologies. The results of this study indicate that the proportion of the population in Latin America and the Caribbean classified as middle class increased from 36.3 percent in 2011 to 37.2 percent in 2017. However, there were no significant changes in the characteristics of this group.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at jfernandezromero@worldbank.org, solivieri@worldbank.org, and dmarce.sanchezc@gmail.com _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
# **A Methodology for Updating International Middle-Class Lines for the Latin American and Caribbean Region**<sup>**1**</sup>

Jaime Fernandez, Sergio Olivieri, and Diana Sanchez<sup>2</sup> JEL code: C53, D31, O15, I32 _Keywords: 2017 PPP, vulnerability line, middle-class, LASSO; Multiple Imputation; Synthetic panel_

> 1 This paper has benefited from discussions with Hugo Nopo, Ximena del Carpio, Carlos Rodriguez Castelan, Dean Jolliffe, Daniel Gerszon, Christoph Lakner, Luis Felipe Lopez-Calva and Eduardo Ortiz-Juarez.

> 2 Jaime Fernandez is Consultant with the Poverty and Equity Global Practice, World Bank, and Associate Professor at Pontificia Universidad Catolica del Ecuador. Sergio Olivieri is a Senior Economist with the Poverty and Equity Global Practice, World Bank (solivieri@worldbank.org). Diana Sanchez  is Research Analyst with the Poverty and Equity Global Practice, World Bank.
# **I. Introduction**

In 2018, for the first time in nearly two decades, Latin America and the Caribbean’s middle class became the largest socioeconomic group. It increased from more than a fifth of the LAC population in 2000 (21.6 percent) to more than a third in 2019 (37.6 percent), based on 2011 PPPs. However, during the pandemic, there was a rapid decline in the size of this group in most countries. As a result, LAC is no longer a middleclass region. This group shrunk by four percentage points in 2020, excluding Brazil, representing 13 million people falling into poverty. Moreover, this decrease reached similar levels as those in 2013. Peru, Colombia, and Argentina drove this significant reduction in 2020.<sup>3</sup> Governments must continue targeting policies to support the most vulnerable populations, particularly after the COVID-19 pandemic, followed by the Russian Federation–Ukraine war, which significantly impacted the region. Thus, it is important to accurately measure the size of the vulnerable population, monitor its evolution, and know where they live and their characteristics.

The major challenge regarding estimating the LAC region’s vulnerable and middle class is the identification of the lower and upper thresholds defined initially by Lopez-Calva & Ortiz-Juarez (2014) and Ferreira et al. (2013). To do so, the principal data that allow for the comparability of different countries’ living standards are purchasing power parities (PPPs). In May 2020, the International Comparison Program (ICP) published new 2017 PPPs. The 2017 PPPs reflect the most recent relative price differences across a wide range of countries around the world. Jolliffe et al. (2022) assessed the impact of the 2017 PPPs on global poverty by updating the three international thresholds: the $1.9 2011 PPP line to $2.15 2017 PPP per person per day, $3.2 2011 PPP to $3.65 2017 PPP per person per day, and $5.5 2011 PPP to $6.85 2017 PPP per person per day.

These changes pose a challenge for how to update the upper and lower boundaries for identifying the vulnerable and middle-class populations in LAC countries. This paper makes three contributions to the literature on global vulnerability and middle-class measurement. First, it proposes a two-step methodology to update these thresholds when new and better rounds of PPPs are estimated. In the past, when 2011 PPPs were published, a simple approach was implemented to update the thresholds. The recent implementation of new PPPs presents an opportunity to revisit and improve previous methodologies used to define middle-class thresholds. However, replicating past approaches would not address the issues with those methodologies. Therefore, this study proposes a new methodology that addresses and resolves some of these problems in some cases and mitigates them in others. Thus, the new lines proposed in this document are determined by i) the use of the revised PPPs, ii) the methodological innovations introduced, and iii) the larger number of countries for which data is available. This novel approach yields a vulnerability or lower-bound line of $14 in 2017 PPP per person per day, compared to $13 in 2011 PPP. The study shows that this rounded value is robust to alternative approaches.

Second, this paper proposes a clear definition of the upper bound. Lopez-Calva & Ortiz-Juarez (2014) and Ferreira et al. (2013) do not provide an equally in-depth discussion of the upper bound as they do for the

> 3  Including Brazil, the middle class declined to 36 percent of the population in 2020, resulting in the loss of 7 million people from this socioeconomic class regionally.

2
lower bound since, firstly, moving it up (down) the income distribution includes (excludes) a small percentage of the population; secondly, an income threshold above $50 in 2005 PPP would limit the representativeness of the upper class in some countries; and, thirdly, household surveys do not collect realistic information on the richest population. Using the proposed methodology in this study with the 2017 PPP, the upper bound for the middle class reaches $81 per person per day, compared with $70 in the 2011 PPP. It is shown that this rounded value is robust to different methodologies.

Third, this study analyzes the impact on the Latin American and Caribbean regional poor, vulnerable, and middle-class groups estimated using the international poverty lines (IPL), the regional vulnerability and middle-class lines, and the 2017 PPPs. The 2017 PPPs would slightly increase historical estimates of poverty at the $2.15 and $3.65 lines, but significantly at $6.85, compared with the 2011 PPPs. Extreme poverty (measured at the $2.15 line) and poverty at $3.65 would increase marginally by 0.3 and 0.9 percentage points (pp), respectively in 2017, with the 2017 PPPs. The regional count of the extreme poor increased by 1.9 million, which is largely driven by 1.3 million and 300,000 more poor people in Brazil and the Andean region, while poverty increased in all other subregions except the Southern Cone.<sup>4</sup> Considering the upper-middle-income country line, $6.85 per person per day, the incidence of poverty in the region rose around five percentage points in 2017 with the 2017 PPP. The headcount shifted almost five percentage points since 2000, compared with the 2011 PPP series. This represents 27 million more poor people in LAC, driven by 12 million and 5.5 million in Brazil and the Andean region, respectively.

A significant increase in the poor population is mirrored by a decrease in the size of the vulnerable group by around 5.4 pp in 2017 with the revised lower-middle-class bound of $14 in 2017 PPP. The regional vulnerability trend is due to declines in the Andean region and Central America, offset by increases in countries in the Southern Cone, including Brazil, since 2012. The update of the upper-middle-class threshold of $81 in 2017 PPP increases the size of the middle class regionally by around 0.9 pp. This change in lower and upper middle-class limits provides a similar positive trend for this group over the last two decades.

The rest of the paper is organized as follows. Section II presents the methodological details for updating both the vulnerability and middle-class lines in terms of the 2017 PPPs. Section III describes the data used in the study. Section IV presents the estimation results for the middle-class upper and lower bounds and some sensitivity analysis. Section V documents changes observed in regional and country-level poverty, considering the vulnerable and middle-class estimates with the 2017 PPP. Finally, Section VI discusses the final remarks.

> 4 The Andean region is the aggregate of Bolivia, Colombia, Ecuador, and Peru; Central America is the aggregate of Costa Rica, Guatemala, Honduras, Nicaragua, Panama, El Salvador, and the Dominican Republic, and the Southern Cone is the aggregate of Argentina, Chile, Paraguay, and Uruguay.

3
# **II. Methodological approach**

The study proposes a two-step methodology for updating the LAC region middle-class thresholds. The first step consists of constructing synthetic panels given the lack or scarcity of long panels in most countries. The second step defines the lower and upper middle-class thresholds.

## II.1 Constructing synthetic panels:

Ideally, a proper study of welfare dynamics entails following the same observation (household or individual) for at least two—or preferably, multiple—periods. However, in many developing countries panel data sets are not readily available, span few periods, or suffer from “non-random” attrition issues, hindering the capacity to study elements such as the factors that help households escape or remain in poverty (Dang and Lanjouw, 2013; Bourguignon and Moreno, 2015). To overcome the absence of panel data or longitudinal surveys, authors such as Deaton (1985), Deaton and Paxson (1994), and Pencavel (2006) have proposed methodologies to construct pseudo-panels by following similar age cohorts across multiple cross-section surveys. Nevertheless, as argued by Dang et al. (2014), these methodologies typically rely on having several rounds of cross-section surveys but do not allow for analyzing mobility at a more disaggregated level than the cohort. In addition, Fields and Viollaz (2013) argue that pseudo-panel methodologies might not perform well in predicting income mobility in some cases.

Dang et al. (2014) propose both a parametric and a non-parametric approach to construct synthetic panels and estimate an upper-bound (assuming zero correlation between error terms) and a lower-bound (assuming a perfect positive correlation between error terms) for the transitions using two rounds of cross sections. In addition, Dang and Lanjouw (2013, 2016) extend this method and calculate point estimates of poverty mobility based on the synthetic panels. However, this approach relies on the key assumption that the residual terms of the income equations in two periods are distributed according to a bivariate normal distribution.

To avoid the strong distribution assumption for residual or error correlation estimates, Lucchetti et al. (2020) introduce a Least Absolute Shrinkage and Selection Operator method with multiple imputations by Predictive Mean Matching (LASSO-PMM) for constructing synthetic panels. Among the several advantages this method has over those above, a very important one is that it does not require estimating any error correlation terms or assuming a certain distribution of residuals in the underlying regressions. As mentioned by Ñopo (2004), matching avoids any parametric assumptions that may impose restrictions on the behavior of the random variables involved in the analysis. The LASSO-PMM method allows for obtaining point estimates as well as upper and lower bounds of welfare dynamics. This penalized regression method uses regularization and takes advantage of machine learning techniques to minimize the mean square error (MSE) of predictions. As a result, it allows for the estimation of more accurate welfare predictions outside the estimation sample compared to traditional regression models, since variables and model selection are performed automatically by penalizing the coefficients and crossvalidation (Tibshirani, 1996). Lucchetti et al (2020) found that the LASSO-PMM predictions are statistically indistinguishable from actual poverty rates, transitions, and income changes calculated using 4
actual panels.<sup>5</sup> On average, the predicted poverty rates using LASSO-PMM were one percentage point away from the observed rates, when using data from the four validation countries.

To build the synthetic panels required to define the new vulnerability and middle-class lines, this paper follows Lucchetti et al. (2020) as a starting point. and introduces several improvements in its methodology. First, matching is performed only between subsets (donation classes) of individuals with the same time-invariant features. These constrained donation classes are built based on gender, birth year, and educational attainment. Second, sampling weights are used to harmonize the joint distributions of identified common variables across the two surveys (Renssen, 1998). This method (known as Renssen’s procedure) consists of a series of calibration steps of the survey weights (in both the donor and receiver surveys<sup>6</sup> ) implemented to achieve consistency between some aggregate estimates, e.g.: income, gender or age distributions. Third, when several donors are equidistant in terms of the predicted income, one of them is randomly chosen according to their sampling weights. The fourth improvement is a direct consequence of the two previous ones: this method allows to preserve the marginal distribution of income from the donor survey, which is essential not to alter welfare dynamics. In addition, although synthetic panels have their own set of limitations, they represent a significant improvement over using cross-sectional data to estimate the probability of falling into poverty. Without actual panels available, this would have been the case. Starting from two available rounds (i.e., = rounds 1 and 2) of cross-sectional microdata. Let i be the per capita household income for household i, (i.e., ), in survey round with sample size , jj yy and i be a vector of household head characteristics observed for household i in survey round j. These ii= 1, ... , NN jj NN household head characteristics, observed in both survey years, can include time-invariant variables (e.g., XX household head’s gender, ethnicity, place of birth, etc.), deterministic variables (e.g., such as age, education, literacy, etc.), and retrospective questions collected in round 2 about round 1 such as assets (Cruces et al, 2015; Dang and Lanjouw, 2018). Changes in household composition can help identify matching errors, which could lead to spurious measures of change. To avoid this, the estimation samples are constrained to household heads’ age: 25 to 65 in the first cross-section and adjust this age range accordingly in the second cross-section. The linear model where -th household’s log per capita income<sup>7</sup> i is explained solely by household head characteristics i for each survey round is given by: ii yy XX i i i (1) TT where i is an error term and yy = ααii + ββi is a vector of ii XX + εε _K_ regressors, and jj = 1, 2 is the intercept. In this context, + i would represent the portion of log income explained exclusively by deterministic and time-ii ii εε XX αα αα invariant household head characteristics. TT ii ββ XX

> 5 Lucchetti et al (2020) use 36 panels from Argentina, Chile, Peru, and Nicaragua to validate their estimates of mobility using harmonized variables frequently used in many regional and global studies. After the validation exercises, the authors implemented the LASSO-PMM method in 43 countries worldwide.

> 6 The donor survey is the one that contains the actual income data from the first-round survey. The receiver is the second-round survey where the income is imputed.

> the second-round survey where the income is imputed.

> 7 To simplify notation, in this document i will be used to refer to the logarithm of household per capita income. yy

5
The objective is to calculate, for household interviewed in round 2, the change in log income between the two survey rounds: ii (2) where and are respectively the first and second round log per capita incomes of household ii ii2 ii1 ΔΔyy = yy −yy surveyed in round 2. Therefore, the log per capita household income in round 1 for household is ii1 ii2 yy yy ii unknown and must be estimated. These changes would be easily calculated with panel data since all ii households are interviewed in both rounds (i.e., is observed for every household interviewed in both rounds and does not require to be estimated). Given panel data is costly and scarce in most countries in ii ΔΔyy ii the region, synthetic panels allow for predicting the first round of “unobserved” incomes of households surveyed in the second. As noted above, to avoid making assumptions about the behavior of the residuals, in this approach the unobserved log income is not obtained directly from an econometric prediction, but through statistical matching between household heads from the two surveys. Thus, the predicted income from model (1) is one of the variables on which the closeness between observations is measured. More specifically, and are predicted using LASSO regression as follows: ii1 ii2 i yy L L yy L L i (3) TT LLjj LLjj 2 yy� = αα� + ββ̂ XX jj = 1, 2 where ( L L L L ) = argmin ) �∑ i i � �1�, is the penalization TT NNjj TT parameter chosen through cross-validation, and αα� LLjj, ββ̂ LLjj (ααjj,ββjj ii=1�yy �−αα1 is the ii −ββlii1XX norm of the coefficient vector + λλ�ββii λλ . ii ii This method estimates the portion of log income that is assumed to be time-invariant, at least when �ββ ββ using cross-sectional surveys close in time. Although this assumption does not always hold, there are several reasons why it may be reasonable to believe that the portion of income explained by timeinvariant covariates remains constant in close periods. First, personal characteristics such as education level, work experience, and skill level tend to be relatively stable over time and may have a persistent effect on income. Second, if individuals have limited mobility and are unable to easily change their geographic location or industry of employment (which is expected to be the case in close periods), this could limit their ability to access higher-paying jobs and result in a relatively constant portion of income being explained by time-invariant factors. Third, if economic conditions (such as the overall level of demand for goods and services) remain relatively constant over time, this could also lead to the portion of income explained by time-invariant factors remaining constant. If a purely parametric approach were used, the full welfare measure in round 1 would be completed by adding some residuals to i . That is not the case in the method presented in this paper. The main assumption behind this semi-parametric approach is that the portion of log income explained by timeyy� invariant covariates remains constant in both periods. As a result, and can be used to find the most similar household heads in both surveys (within the set of observations that share common ii1 ii2 yy� yy� characteristics) and thus build the synthetic panel. For every observation in the second round of data, a set of neighbors (in terms of and closeness) is found in the first-round data by looking for observations with the smallest absolute difference between the two linear predictions. Among these ii1 ii2 yy� yy� neighbors, one of them is randomly selected based on the sampling weights from the first round. In addition, the matching method requires that the closest neighbors are searched only within a pre-defined 6
subset of households’ heads having the same gender, educational attainment and age. The observed income from that neighbor is then imputed to the corresponding observation in the second round, which represents the first-round log income for that household surveyed in the second round: (4) where is actual first-round observed income for the household chosen through the matching process ii ii2 ii1 ΔΔ̃yy = yy −yy� (4) where described above. ii1 yy� Summarizing, the steps to estimate the “unobserved” household per capita income in the first round are the following:

1. Append first and second round of cross-sectional data and create a fused dataset

2. Harmonize the joint distribution of gender and educational attainment in both surveys 3. Take a sample for the statistical learning stage: 80 percent of the fused dataset defined in the first step 4. Estimate the parameters and select the best lambda through cross-validation 5. Calculate the LASSO linear prediction of the first-round log incomes for all households surveyed in the first round as in equation (3) 6. Obtain the LASSO linear fit of the second-round log incomes for all households surveyed in the second round as in equation (3)

- second round as in equation (3)

- 7. For every household head in the second round, obtain the nearest first-round neighbors by minimizing the absolute difference between and for every observation in the first round mm

- that has the same gender, educational attainment and birth year mm2 ii1

- yy� yy� ii

- that has the same gender, educational attainment and birth year

- 8. Randomly select one neighbor from the list of nearest neighbors based on first-round sampling weights ss

- weights

- 9. Take the observed log income of neighbor ( ) chosen in the first round, and assign it to observation surveyed in the second round ( ) ss1

- ss yy

- 10. Estimate movements in-out of poverty and other income dynamics mm1 ss1

- mm yy� = yy

- 11. Repeat 100 times all steps 3 to 10

This process allows for estimating standard errors for the statistics in interest. For every bootstrap sample of the data, a new LASSO is fit and the whole welfare vector is constructed via the improved LASSO PMM process described above. As a result, both point and interval estimates are calculated for every poverty dynamics indicator.

# II.2 Defining the middle-class thresholds

The major challenge regarding the estimation of the LAC region’s vulnerable and middle-class groups is the identification of the lower and upper thresholds. These were defined initially by Lopez-Calva & OrtizJuarez (2014) and Ferreira et al. (2013). These authors focus mainly on defining and computing the lower bound. An individual is defined as vulnerable if the probability of falling back into poverty over a five-year interval is greater than 10 percent. Both references use panel data from three different sets of countries. While Lopez-Calva & Ortiz-Juarez (2014) use Chile, Mexico, and Peru, Ferreira et al. (2013) use Argentina, 7
Colombia, and Costa Rica. Both reach a similar lower bound estimate of $10 per person per day in 2005 PPPs.

The middle-class upper threshold does not follow a thorough definition or computation as the lower bound. Birdsall et al. (2011) show that varying the upper threshold from $50 to $100 a day would move the percentile of the LAC’s elite from the top 2.2 percent to the top 0.5 percent. Thus, both define the upper threshold as $50 per person per day in 2005 PPP which is equivalent to the top 2.2 percent of the population.

More recently, when 2011 PPPs were published, a very simple approach was implemented without applying previous methodologies.<sup>8</sup> Firstly, using each country's 2005 PPP conversion factor, the vulnerable and middle-class lines were converted to local currency units at 2005 prices. Secondly, these values were deflated to 2011 prices using each country's Consumer Price Index (CPI) and converted back to US dollars using their corresponding 2011 PPP conversion factors. Finally, a simple average of the resulting lines was estimated to obtain a regional value. By rounding to the closest unit, the lower and upper middle-class lines in 2011 PPP for LAC were then set at $13 and $70 a day, respectively.

The proposed approach estimates the LAC lower middle-class bound (vulnerability line) in two steps. First, it builds a two-year synthetic panel for each country using every pair of available cross-section years between 2010 and 2019. Then, the vulnerability line is defined as the median per capita household income over households who were not poor in the initial year and became poor in the final year, i.e., a household in moves into poverty if and , where is the international poverty line of 6.85 a day in purchasing power parities (PPP) 2017. ii2 ii1 ii jj = 2 yy < zz yy� > zz zz There are a few reasons why it might be useful to define the lower bound of the middle class as the median of the income distribution of people who have recently moved out of poverty. First, the median income represents the midpoint of the income distribution, so it is less affected by extreme values that could skew the results. This can help to ensure that the middle-class thresholds are fair and representative of the income needs of a typical household.

A second advantage of using the median income as the lower bound of the middle class (upper bound of the vulnerable group) is that it highlights the fact that the vulnerable population is not static, but rather constantly changing. Additionally, using a more reliable statistic like the median allows for a more precise estimate of the minimum income needed to be considered middle class. It is important to note that it does not imply that the probability-based approach (Lopez-Calva & Ortiz-Juarez, 2014) fails to fulfill these conditions; both methods acknowledge the dynamic nature of the vulnerable population.

Finally, employing the median income as the lower bound of the middle class can contribute to making the middle-class thresholds more responsive to economic changes and shifts in income distribution. As the economy grows and more people rise out of poverty, the middle-class thresholds can be adjusted accordingly to reflect these changes. This responsiveness allows for a more inclusive definition of both the vulnerable and middle-class populations, preventing the exclusion of individuals who are making

> 8 World Bank (2021).

8
progress yet may still be considered poor or vulnerable by current standards. Formally, the vulnerability line for each country in a given two-year synthetic panel (v ) is defined such that: 1(v ) = 0.5 (5) YY FF where 1(. ) is the empirical cumulative distribution function of per capita income in year 1, restricted to the subset of people who went from not being poor in year 1 to being poor in year 2. YY FF This estimation process is repeated 100 times for each two-year synthetic panel, which generates a distribution of vulnerability lines for each country.<sup>9</sup> This allows for both point estimates and confidence intervals for the lower middle-class bound. Finally, to estimate the line at the regional level, the method averages out all these medians across countries and over time.<sup>10</sup> To draw the upper-middle-class threshold (middle-class line), the study follows the same methodological approach presented for the lower-middle-class bound. Conceptually, it is proposed to define the middleclass line as the maximum per capita income of households that went from being non-poor to being poor in each two-year synthetic panel. However, because this measure can be very volatile, the 99th percentile of the income distribution of those who went from not being poor in year 1 to being poor in year 2 is used instead.

It has been suggested that the boundaries for the middle class should be defined using quantiles because the factors that influence the likelihood of moving from non-poverty to poverty may vary between countries. Otherwise, the specific criteria used to determine the boundaries would depend on the model being used and the data available. Thus, using a consistent definition across countries, it becomes easier to compare the size, stability, and characteristics of the middle class in different Latin American countries. This can help to identify patterns and trends and inform regional policy development. Formally, the middle-class line for each country in a given two-year synthetic panel (m ) is defined such that: vv 1(m (6) YY FF vv) = 0.99 where 1(. ) is once again the empirical cumulative distribution function of per capita income of those who experienced downward mobility at the 6.85 line (PPP 2017). Lastly, it averages these statistics across YY FF countries and over time after repeating the process 100 times for each country and each synthetic panel. Thus, point estimates and confidence intervals are obtained for the upper-middle-class threshold.

> 9 The approach considers the median instead of the mean due to not being affected by extreme values and being consistent with the international poverty threshold methodology (see Jolliffe and Prydz, 2016).

> 10 It is relevant to point out that results do not significantly vary when weighted averages based on population are considered.

9
# **III. Data**

This paper uses harmonized cross-section microdata for 15 countries in Latin America and the Caribbean (i.e., Argentina, Bolivia, Brazil, Colombia, Costa Rica, Chile, Dominican Republic, El Salvador, Ecuador, Honduras, Mexico, Panama, Paraguay, Peru, and Uruguay) for the series starting in 2010 and ending in 2019.<sup>11</sup> These data are from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), a joint effort of the World Bank and the Center for Distributive, Labor, and Social Studies (CEDLAS) at the National University of La Plata in Argentina.<sup>12</sup> The selection of these countries is based on the availability of at least two comparable data points between 2010 and 2019 within a two-year interval and on the accessibility of the necessary variables to conduct the estimation. More than 85 synthetic panels were constructed for the selected countries with a two-year length following Balcazar et al (2018). Table 1 summarizes the countries, initial and end years, household-survey names in SEDLAC, and the total number of synthetic panels. This study considers information for small and large economies in the region; lower-middle, upper-middle, and high-income countries, and represents 94 percent of the LAC population. The countries excluded from the exercise are those whose data is non-existent or not available circa the end year of the interval.

Table 1: Available cross-section surveys

|Country|Years|Survey|Total #<br>SP|Classification|
|---|---|---|---|---|
|Argentina|2010,2011,<br>2012,<br>2013,<br>2014,2016, 2017, 2018, 2019|Encuesta Permanente de<br>Hogares- Continua|6|H|
|Bolivia|2011, 2012, 2013, 2014, 2015,<br>2016, 2017, 2018, 2019|Encuesta Continua de<br>Hogares|7|LM|
|Brazil|2012,2013, 2014, 2015, 2016,<br>2017, 2018,2019|Pesquisa Nacional por<br>Amostra de Domicilios|6|UM|
|Colombia|2010,2011, 2012, 2013, 2014,<br>2015, 2016, 2017, 2018, 2019|Gran Encuesta Integrada de<br>Hogares|8|UM|
|Costa Rica|2010,2011, 2012, 2013, 2014,<br>2015, 2016, 2017, 2018, 2019|Encuesta Nacional de<br>Hogares|8|UM|
|Chile|2006, 2009, 2011, 2013, 2015,<br>2017|Encuesta de Caracterización<br>Socioeconómica Nacional|3|H|
|Dominican<br>Republic|2017,2018, 2019|Encuesta de Fuerza de<br>Trabajo|1|UM|
|Ecuador|2010,2011, 2012, 2013, 2014,<br>2015, 2016, 2017, 2018, 2019|Encuesta de Empleo,<br>Desempleo y Subempleo|8|UM|
|El Salvador|2010,2011, 2012, 2013, 2014,<br>2015, 2016, 2017, 2018, 2019|Encuesta de Hogares de<br>Propósitos Múltiples|7|LM|

> 11 Guatemala, Nicaragua, and Haiti were not included in the analysis due to the lack of microdata circa 2019.

> 12 The SEDLAC project consists of more than 400 household surveys in more than 25 LAC countries to provide statistics on poverty and other distributional and social variables. See Bourguignon (2015) for a detailed description of the SEDLAC project.

10
|Honduras|2011, 2012, 2013, 2014, 2015,<br>2016, 2017, 2018, 2019|Encuesta Permanente de<br>Hogares Propósitos<br>Múltiples<br>Encuesta Nacional de|7|LM|
|---|---|---|---|---|
|Mexico|2016, 2018, 2020|Ingresos y Gastos de<br>Hogares|1|UM|
|Panama|2010,2011, 2012, 2013, 2014,<br>2015, 2016, 2017, 2018, 2019|Encuesta de Hogares|8|H|
|Paraguay|2010,2011, 2012, 2013, 2014,<br>2015, 2016, 2017, 2018, 2019|Encuesta Permanente de<br>Hogares|7|UM|
|Peru|2010,2011, 2012, 2013, 2014,<br>2015, 2016, 2017, 2018, 2019|Encuesta Nacional de<br>Hogares|8|UM|
|Uruguay|2010,2011, 2012, 2013, 2014,<br>2015, 2016, 2017, 2018, 2019|Encuesta Continua de<br>Hogares|7|H|

Note: LM: Lower-Middle Income, UM: Upper-Middle Income, and H: High Income Source: SEDLAC (CEDLAS and World Bank) for country classification: <u>https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups</u> The proposed approach estimates a model of household-level income using a set of time-invariant and deterministic harmonized variables as controls to provide estimates of welfare dynamics. The harmonization procedure requires cleaning and processing each cross-section to ensure that all definitions and variables are identical in each country and year. The full list of available covariates for each country’s data is shown in Table 2.

Table 2: Harmonized variables for LASSO-PMM

|Variable|Definition|
|---|---|
|Welfare aggregate|Per household capita income for international poverty estimations. It is<br>expressed daily basis and deflated using 2017 PPPs|
|Age|Age of the household head (level and squared)|
|Gender|Gender of the household head|
|Education Level|Education of the household head in four education levels: (i) No education<br>(ii) Primary (complete or incomplete) (iii) Secondary (complete or<br>incomplete) (iv) Tertiary (complete or incomplete)|
|Literacy|Literacy status of household head|
|Weight|Surveyweights|

Source: SEDLAC (CEDLAS and World Bank) When estimating regional middle-class thresholds, income distributions are converted into a common, internationally comparable currency unit using exchange rates. PPP conversion factors are preferred to market exchange rates since it incorporates both the relative prices of tradable goods as the market exchange rates and non-tradable services (e.g., getting a haircut) across countries (Jolliffe et al. 2022). PPPs measure how much it costs to purchase a basket of goods and services in one country compared to how much it costs to purchase the same basket of goods and services in terms of the currency units of a reference country, typically the United States. The estimates of PPP and market exchange rates are from the ICP, and the study uses the PPP estimates from the 2011 and 2017 rounds.

11
# Robustness check: Expanding the set of countries

Lopez-Calva and Ortiz-Juarez (2014) and Ferreira et al. (2013) only use a particular set of high and uppermiddle-income countries in LAC to define the regional middle-class bounds. The limitation of the selection of countries depends on the availability of longitudinal data. Given that this is not a restriction in the proposed approach, expanding the number of countries, including high, upper-middle, and lowmiddle income, enhances the estimation results by increasing regional representativeness. Following Jolliffe et al. (2022) to check the robustness of results, both lower and upper middle-class bounds were calculated cumulatively by ranking countries from lowest to highest GDP per capita. Figure 2 shows that both vulnerability and middle-class lines estimates are robust to using fewer countries. Each point in the figure corresponds to the line estimated using the synthetic panels available for that country and all those to the left.

**Figure 2** : Cumulative middle-class bounds for LAC countries in 2017 PPP ordered by country GDP

<!-- Start of picture text --> 15 85<br>80<br>14<br>75<br>13<br>70<br>12 High income (H) 65<br>11 60<br>Upper middle income<br>(UM) 55<br>10<br>Lower middle 50<br>9<br>45<br>8 40<br>hnd bol slv ecu per pry col bra dom mex cri ury arg chl pan<br>Country<br>Vulnerability line Middle-class line (right axis)<br>Vulnerability line Middle-class line<br><!-- End of picture text --> Source: Own estimations based on SEDLAC (2022) Note: World Bank Analytical Classifications using data for 2017. Results were obtained using 38 synthetic panels built with available surveys within +/- 2 years circa 2017 and 100 bootstrap repetitions.

# Robustness check: Increasing the time span

When estimating the International Poverty Lines, Jolliffe et al. (2020) select for each country one survey that was conducted in 2017 or the closest year. The proposed analysis in this paper followed the same principle by choosing a two-year interval around 2017 (i.e., 2015 and 2019) for LAC countries to capture their income and poverty dynamics.<sup>14</sup> However, expanding the time span - including more rounds- might increase the statistical support by embracing changes that are not necessarily assigned to a specific period. To check the robustness of both thresholds, new two-year synthetic panel rounds were constructed for the 2010–2015 interval. Figure 3 shows that when using more than 90 synthetic panels (2010-2019), variations in bounds estimates are not significant: the median lower-middle class line

> 14 Lopez-Calva & Ortiz-Juarez (2014) exploit the longitudinal data for 3 or 4-year intervals: Chile (2001-2006), Mexico (20022005), and Peru (2002-2006).

13
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# ett Wella
**Figure 4** : Cumulative middle-class bounds for LAC countries in 2017 PPP ordered by country GDP

<!-- Start of picture text --> 15 85<br>80<br>14<br>75<br>13<br>70<br>12 High income (H) 65<br>11 60<br>Upper middle income<br>(UM) 55<br>10<br>Lower middle 50<br>income (LM)<br>9<br>45<br>8 40<br>hnd bol slv ecu per pry col bra dom mex cri ury arg chl pan<br>Country<br>Vulnerability line Middle-class line (right axis)<br>Vulnerability line Middle-class line<br><!-- End of picture text --> Source: Own estimations based on SEDLAC (2022) Note: World Bank Analytical Classifications using data for 2017. Results were obtained using 92 synthetic panels built with available surveys from 2010 to 2019 and 25 bootstrap repetitions.

# Robustness check: Implementing other methodologies

For completeness, the analysis estimates the middle-class lines using previous methodologies. If the Lopez-Calva and Ortiz-Juarez (2014) methodology is applied only to Chile, Mexico, and Peru -as in the original study- or to Argentina, Colombia, and Costa Rica -as in Ferreira et al. (2013)-, the lower bound would be $12 and $16.1, respectively, and the upper bound $85.8 and $109.3, correspondingly. If this method is expanded to all countries over the same period, the lower bound would end up being $13.6 and the upper bound $89.1. Moreover, replicating the simple method applied, the lower and upper bounds would be $14.7 and $79.3, respectively.

# Identifying the drivers of change in the thresholds

While the update of PPPs from 2011 to 2017 contributed to the observed changes in the middle-class thresholds ($ 14 and $ 81) compared to the previous lines ($ 13 and $ 70, respectively), it is not the only factor. One way to initially assess the impact of updating purchasing power parity (PPP) rates is to calculate the ratio ( ) of the relative change in the consumer price index (CPI) between 2011 and 2017 to the relative change in PPP between those same years: δδ C �<sup>C</sup> C<sup>CC2017CC2017</sup> δδ=<sup>C</sup> CC2011 CC2011 15
By averaging the values of across all 15 countries, it is possible to estimate the impact of the PPP update on the lower and upper thresholds: = 1.13 . On the other hand, the update in the lower threshold from δδ $ 13 in 2011 PPP to $ 14 in 2017 PPP implies a 7.7% increase, and the change in the upper threshold results δδ̅ in a 15.7% increase. In conclusion, the combined effect of all other factors is -5.3% in the lower threshold and an additional 2.7% in the upper threshold.

The methodological approach presented in this paper differs from previous work in three ways: i) the use of synthetic panels instead of actual panels, ii) the definition of the thresholds, and iii) the inclusion of more countries (15 rather than 3 in the original study). To assess the impact of these differences on the real value (aside from the effect of the PPP update) of the new lines, some additional calculations were done. When applying the proposed methodology to the three original countries (Chile, Mexico, and Peru) using 2011 PPPs, the lower and upper bounds would be 3.8 and 11.4% higher, compared to the $ 13 and $ 70 thresholds, respectively (Table 3). This is the combined effect of working with synthetic panels and introducing a new definition of thresholds. On the other hand, when this same analysis is conducted across all 15 available countries, the lower and upper bounds would be 3.8 lower and 3.4% higher, respectively. As a result, it could be concluded that the overall effect of expanding the universe of analysis from 3 to 15 countries is a decrease of 7.4 and 7.2% in the lower and upper thresholds, respectively.

Table 3. Decomposition of changes to middle-class thresholds

|**Threshold**|**s in 2011**<br>**PPP**|**Original**|**Proposed**<br>**approach with**<br>**3 countries as**<br>**in Lopez-Calva**<br>**& Ortiz-Juarez**<br>**(2014)**|**Proposed**<br>**approach**<br>**with all 15**<br>**countries**<br>**available**|
|---|---|---|---|---|
|**Lower**||13|13.5|12.5|
|**Upper**||70|78|72.4|

Source: Own estimations based on SEDLAC (CEDLAS and the World Bank).

# **V. Impacts of 2017 PPPs on poverty, vulnerable, and the middle class**

In this section, regional, sub-regional and country-level poverty, vulnerable, and middle-class estimates are presented over time with the 2017 PPPs, in comparison with the 2011 PPPs. This analysis helps understand how incidences and the geographic distribution of the poor, vulnerable, and middle-class populations would change when using 2017 PPPs and higher thresholds. Profiles for these populations are presented over time with the 2017 PPPs, relative to the 2011 PPPs. It is important to assess whether this update of PPPs and thresholds significantly impacts the characteristics of these populations. Marked changes in what the poor and the vulnerable look like affect policy design.

16
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To understand the sub-regional movements further, Table 4 shows the three countries with the largest absolute changes in vulnerability. The decrease observed in the Andean region is driven by Colombia, where the vulnerable population falls by 6 pp (equivalent to 3 million fewer vulnerable people). About 31 percent of the change in Central America is driven by Guatemala, where vulnerability decreases by 6.8 pp (equivalent to 1 million fewer vulnerable).

Table 4. Countries with the largest absolute changes in vulnerability. Millions of people

||**2015**|**2016**|**2017**|**2018**|**2019**|**2020**|
|---|---|---|---|---|---|---|
|_LAC_|_-29_|_-30_|_-31_|_-30_|_-30_|_-30_|
|**Brazil**|-8|-9|-9|-9|-9|-7|
|**Mexico***|-8|-8|-8|-9|-9|-9|
|**Andean Region**|||||||
|Colombia|-3|-3|-3|-3|-3|-3|
|Peru|-2|-1|-2|-2|-2|-2|
|Ecuador|-1|-1|-1|-1|-1|-1|
|**Central America**|||||||
|Guatemala*|-1|-1|-1|-1|-1|-1|
|Dominican Republic|-1|-1|-1|-1|0|-1|
|El Salvador|-1|-1|-1|-1|-1|-1|

Source: Own estimations based on SEDLAC (CEDLAS and the World Bank).

*For Mex: Projections for 2015, 2017, and 2019. For Guatemala: Projections from 2015 onwards

The middle class has slightly increased in the region and remains the largest socioeconomic group in the LAC region since 2010. This group is measured as the share of the total population between $13 and $70 a day or $14 and $81 a day expressed in 2011 PPP or 2017 PPP, respectively. Between 2010 and 2020, changes in the middle class at the LAC level are relatively small as increases in the three subregions (i.e., Andean, Central America, and Southern Cone) and Mexico are offset by Brazil. In 2017, the middle class in the region grew by less than 1 pp (0.9 pp), or 4.8 million more middle-class people, with the 2017 PPPs. The largest changes in millions of middle-class people in 2017 are observed in the Southern Cone (2.2 million more people) and Brazil (2 million fewer people). The change in the Southern Cone is mainly driven by Argentina.

# Regional and country-level profiles

This section focuses on assessing regional profiles of the poor at the upper-middle-income line ($6.85 in 2017 PPP), the vulnerable, and middle-class populations. Even though a significant shift in poverty impacts poverty profiles, these movements are not big enough for inducing changes in policy choices (see Table 5). For instance, the poor are still more concentrated in urban areas, belong to the cohort between 15 and 64 years old, are slightly more educated, work in services, and are less self-employed. Similarly, vulnerability decreases markedly with the new thresholds, but the characteristics of vulnerable people remain the same.<sup>16</sup> The majority of the vulnerable are settled in urban areas, a little bit older by

> 16 Variations are not statistically significant.

19
slightly increasing the share of the 15 to 64 y.o. cohort, somewhat more educated, work mainly in services, and a little bit more as salaried workers. Finally, the profiles for the middle-class population were not statistically affected by these changes.

Table 5. Profile of the Poor

|||**Po**|**ver**|**ty $ **|**5.**|**5**|**(2011**|**PP**|**P)**||**Po**|**verty**|**$6.**|**85**|**(2 **|**017**|**P**|**PP)**||||**Dif**|**f**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||**2015 2**|**016 **|**20**|**17**||**2018 **|**201**|**9 **|**2020**|**2015 **|**2016 **|**201**|**7 **|**201**|**8 2**|**01**|**9 2**|**020**|**2015 201**|**6 **|**2017 **|**2018 **|**2019 2020**|
|**% Population**||25.1|25.3|24|.6||24.2|23.|8|24.0|29.9|30.0|29.|5|28.|9 2|8|.4|28.6|5|5|5|5|5<br>5|
|**Area**|Urbano|59|59||59||59|6|0|62|61|61|6|1|6|1|6|3|64|2|2|3|2|2<br>2|
||Rural|41|41||41||41|4|0|38|39|39|3|9|3|9|3|7|36|-2<br>-|2|-3|-2|-2<br>-2|
|**Gndr**|Males|48|48||48||48|4|8|48|48|48|4|8|4|8|4|8|48|0|0|0|0|0<br>0|
|**ee**|Females|52|52||52||52|5|2|52|52|52|5|2|5|2|5|2|52|0|0|0|0|0<br>0|
||0-14 Years Old|39|39||38||38|3|7|37|38|38|3|7|3|7|3|6|36|-1<br>-|1|-1|-1|-1<br>-1|
|**Age Group**|15-64 Years Old|56|57||57||57|5|8|59|57|58|5|8|5|8|5|9|59|1|1|1|1|1<br>1|
||65 and older|5|4||5||5||5|5|5|5||5||5||5|5|0|0|0|0|0<br>0|
||Years of education|5|5||5||5||5|6|5|5||5||5||5|6|0|0|0|0|0<br>0|
||Less than primary|57|57||57||56|5|4|48|56|56|5|6|5|5|5|3|48|-1<br>-|1|-1|-1|-1<br>0|
|**Education**|Primary & less than secondary|32|31||31||31|3|2|34|31|31|3|1|3|1|3|1|33|0|0|0|0|0<br>-1|
||Secondary|10|11||11||12|1|3|15|12|12|1|3|1|3|1|4|16|1|1|1|1|1<br>1|
||Tertiary|1|1||1||1||1|2|1|1||1||1||1|2|0|0|0|0|0<br>0|
||Agriculture|39|39||39||38|3|7|38|35|35|3|5|3|5|3|4|36|-3<br>-|3|-3|-3|-3<br>-3|
|**Sectors**|Industry|20|19|1|9||19|1|9|19|20|20|2|0|2|0|2|0|19|1|1|1|1|1<br>0|
||Services|42|42|4|2||42|4|4|43|45|45|4|5|4|5|4|6|45|3|3|3|3|2<br>2|
||Employers|5|5||5||5||5|6|5|4|4|||5||5|5|0|0|0|0|0<br>0|
||Salaried workers|42|40|3|9||39|3|9|37|44|43|41||4|1|4|1|39|3|2|3|2|2<br>2|
|**Type of worker**|Self-employed|30|30|3|0||30|2|9|29|29|29|29||2|9|2|9|28|-1<br>-|1|-1|-1|0<br>-1|
||Not salaried|11|11|1|1||11|11||12|10|10|10||1|0|1|0|11|-1<br>-|1|-1|-1|-1<br>-1|
||Unemployed|12|14||16||15|1|6|17|11|14|1|5|1|5|1|6|17|0|0|0|0|0<br>0|
|**Household**|Average hh size|5|5||5||5||5|5|5|5||5||5||5|5|0|0|0|0|0<br>0|
|**characteristics and**|Access to electricity (%)|97|98||98||98|9|8|97|98|98|9|8|9|8|9|8|98|0|0|0|0|0<br>0|
|**access to services**|Accessto improved water(%)|87|89||89||90|9|0|90|88|90|9|0|9|1|9|1|90|1|1|1|1|1<br>1|

Source: SEDLAC (World Bank and CEDLAS).

Note: Note: Since the numbers presented here are based on SEDLAC, a regional data harmonization effort that increases cross-country comparability, they may differ from official statistics reported by governments and national statistical offices. The LAC aggregate is based on 18 countries in the region for which microdata are available. In cases where data are unavailable for a given country in a given year, values have been interpolated using WDI data or using microsimulations to calculate regional measures. Type of employment and sector limited to working individuals ages 15–64.

20
# Table 6. Profile of the Vulnerable

|||**Vuln**|**era**|**bl**|**e$5**|**.5-$**|**13(2 **|**011 P**|**PP)**|**Vulner**|**able**|**$6.85-**|**$14(2 **|**017 P**|**PP)**||||**Dif**|**f**|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||**2015 **|**201**|**6 **|**201**|**7 2**|**018 2**|**019 **|**2020**|**2015 2**|**016 **|**2017 2**|**018 **|**2019 **|**2020**|**2015 **|**20**|**16 **|**2017 **|**201**|**8 2**|**019 2020**|
|**% Population**||36.7|36.|5|36.|5|36.6|36.1|37.7|31.4 3|1.1|31.1|31.2|30.8|32.6|-5.2|-5|.4|-5.4|-5.|4<br>-|5.3<br>-5.1|
|**Area**|Urbano|80|8|0|8|0|80|80|80|82|81|81|81|80|81|1||1|1||1|1<br>1|
||Rural|20|2|0|2|0|20|20|20|18|19|19|19|20|19|-1||-1|-1|-|1|-1<br>-1|
|**Gender**|Males|48|4|8|4|9|48|48|48|48|48|48|48|48|48|0||0|0||0|0<br>0|
||Females|52|5|2|5|1|52|52|52|52|52|52|52|52|52|0||0|0|0||0<br>0|
||0-14 Years Old|28|2|7|2|7|27|26|26|27|26|26|26|26|25|-1||-1|-1|-1||-1<br>-1|
|**Age Group**|15-64 Years Old|66|6|6|6|6|66|66|66|67|67|67|67|67|67|1||1|1|1||0<br>1|
||65 and older|7||7||7|7|8|7|7|7|7|7|8|8|0||0|0|0||0<br>0|
||Years of education|7||7||7|7|7|7|7|7|7|7|7|7|0||0|0|0||0<br>0|
||Less than primary|42|4|2|4|1|40|40|39|41|41|40|40|39|39|-1||-1|-1|-1||-1<br>0|
|**Education**|Primary & less than secondary|32|3|2|3|2|32|32|30|32|32|32|32|31|29|0||0|0|0||0<br>-1|
||Secondary|22|2|3|2|3|24|25|26|23|24|24|24|25|27|1||1|1|1||1<br>1|
||Tertiary|3||3||4|4|4|5|4|4|4|4|4|5|0||0|0|0||0<br>0|
||Agriculture|13|1|3|1|3|14|14|15|12|12|13|13|13|14|-1||-1|-1|-1||-1<br>-1|
|**Sectors**|Industry|25|2|5|2|4|24|24|24|25|25|24|24|24|24|0||0|0|0||0<br>0|
||Services|62|6|2|6|2|62|62|61|62|63|63|63|63|62|1||1|1|1||1<br>1|
||Employers|3||3||3|3|4|3|3|3|3|3|3|3|0||0|0|0||0<br>0|
||Salaried workers|63|6|1|6|1|60|59|57|64|62|62|61|60|58|1||1|1|1||1<br>1|
|**Type of worker**|Self-employed|22|2|3|2|3|23|24|23|22|22|23|23|23|22|0||0|0|0||0<br>0|
||Not salaried|4||4||4|4|4|5|4|4|4|4|4|4|0||0|0|0||0<br>0|
||Unemployed|8||9||9|9|10|13|7|9|9|9|9|13|0||0|0||0|0<br>0|
|**Household**|Average hh size|5||4||4|4|4|4|4|4|4|4|4|4|0||0|0||0|0<br>0|
|**characteristics and**|Access to electricity (%)|99|9|9|9|9|99|99|99|99|99|99|99|99|99|0||0|0||0|0<br>0|
|**access to services**|Accessto improved water(%)|96|9|7|9|7|97|97|97|96|97|97|97|97|97|0||0|0||0|0<br>0|

Source: SEDLAC (World Bank and CEDLAS).

Note: Note: Since the numbers presented here are based on SEDLAC, a regional data harmonization effort that increases cross-country comparability, they may differ from official statistics reported by governments and national statistical offices. The LAC aggregate is based on 18 countries in the region for which microdata are available. In cases where data are unavailable for a given country in a given year, values have been interpolated using WDI data or using microsimulations to calculate regional measures. Type of employment and sector limited to working individuals ages 15–64.

Table 7. Profile of the Middle Class

|||**Middl**|**e C**|**la**|**ss$**|**13-**|**$7 **|**0**|**(2011 P**|**PP)**|**Middl**|**e Clas**|**s$1**|**4**|**-$81**|**(**|**201**|**7 P**|**PP)**|||**Dif**|**f**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||**2015 **|**201**|**6 **|**201**|**7 2**|**0**|**18 **|**2019 **|**2020**|**2015 2**|**016 **|**2017**||**201**|**8 **|**201**|**9 2**|**020**|**2015 2**|**016 20**|**17 **|**2018 **|**2019 2020**|
|**% Population**||35.8|35.|7|36.|3|36|.6|37.6|36.0|36.5|36.6|37.2||37.|6|38.|6|36.8|0.8|0.9<br>0|.9|0.9|1.0<br>0.8|
|**Area**|Urbano|90|9|0|9|0|9|0|90|89|90|90|90||9|0|9|0|89|0|0|0|0|0<br>0|
||Rural|10|1|0|1|0|1|0|10|11|10|10|10||1|0|1|0|11|0|0|0|0|0<br>0|
|**Gender**|Males|50|4|9|4|9|4|9|49|49|49|49|49||4|9|4|9|49|0|0|0|0|0<br>0|
||Females|50|5|1|5|1|5|1|51|51|50|51|51||5|1|5|1|51|0|0|0|0|0<br>0|
||0-14 Years Old|15|1|5|1|5|1|4|14|14|15|15|15||1|5|1|4|14|0|0|0|0|0<br>0|
|**Age Group**|15-64 Years Old|73|7|2|7|2|7|2|72|72|73|72|72||7|2|7|2|72|0|0|0|0|0<br>0|
||65 and older|12|1|3|1|3|1|3|14|14|12|12|13||1|3|1|4|14|0|0|0|0|0<br>0|
||Years of education|9||9||9||9|9|9|9|9|9|||9||9|10|0|0|0|0|0<br>0|
||Less than primary|28|2|7|2|7|2|7|26|27|28|27|27||2|7|2|6|27|0|0|0|0|0<br>-1|
|**Education**|Primary & less than secondary|23|2|3|2|2|2|2|22|20|23|23|23||2|2|2|2|21|1|1|1|1|1<br>1|
||Secondary|33|3|3|3|3|3|3|34|34|32|33|33||3|3|3|4|33|0|0|0|0|0<br>0|
||Tertiary|16|1|7|1|7|1|8|18|19|16|17|17||1|8|1|8|19|0|0|0|0|0<br>0|
||Agriculture|5||5||6||6|6|7|5|5|5|||6||6|7|0|0|0|0|0<br>0|
|**Sectors**|Industry|22|2|1|2|1|2|0|20|20|22|21|21||2|0|2|0|21|0|0|0|0|0<br>0|
||Services|73|7|4|7|4|7|4|74|73|73|74|74||7|4|7|4|73|0|0|0|0|0<br>0|
||Employers|5||5||5||5|5|5|5|5|5|||5||5|5|0|0|0|0|0<br>0|
||Salaried workers|70|6|9|6|9|6|9|68|67|70|69|69||6|9|6|8|67|0|0|0|0|0<br>0|
|**Type of worker**|Self-employed|19|1|9|2|0|2|0|20|20|19|19|20||2|0|2|0|20|0|0|0|0|0<br>0|
||Not salaried|2||2||2||2|2|2|2|2|2|||2||2|2|0|0|0|0|0<br>0|
||Unemployed|4||5||5||4|5|6|4|4|5|||4||5|6|0|0|0|0|0<br>0|
|**Household**|Average hh size|3||3||3||3|3|3|4|4|3|||3||3|3|0|0|0|0|0<br>0|
|**characteristics and**|Access to electricity (%)|99|10|0|10|0|10|0|100|100|100|100|100||10|0|10|0|100|0|0|0|0|0<br>0|
|**access to services**|Access to improved water(%)|99|9|9|9|9|9|9|99|99|98|99|99||9|9|9|9|99|0|0|0|0|0<br>0|

Source: SEDLAC (World Bank and CEDLAS).

Note: Note: Since the numbers presented here are based on SEDLAC, a regional data harmonization effort that increases cross-country comparability, they may differ from official statistics reported by governments and national statistical offices. The LAC aggregate is based on 18 countries in the region for which microdata are available. In cases where data are unavailable for a given country in a given year, values have been interpolated using WDI data or using microsimulations to calculate regional measures. Type of employment and sector limited to working individuals ages 15–64.

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# **VI. Final remarks**

This study analyzes the impacts of adjusting the LAC lower and upper-middle-class regional thresholds when new and better rounds of PPPs are estimated. First, the paper proposes a two-step methodology to update these thresholds. The first step consists in building two-year synthetic panels between 2015 and 2019 for 15 countries in the LAC region. It is based on the paper by Lucchetti et al. (2020) and introduces several improvements in the methodology: matching is done using constrained donation subsets based on gender, birth year, and educational attainment; introduces weights harmonization procedure to preserve the original distribution in terms of relevant variables for the analysis; neighbor selection is based on random selection within the subset of closest neighbors, considering the sampling design of the donor survey; it implements more bootstrap repetitions to achieve more accuracy in estimates, etc. The second step defines the _lower bound_ as the median per capita household income over households who were not poor in the initial year and became poor in the final year and the _upper bound_ as the maximum per capita income (99th percentile) over those who have changed their status. This yields a vulnerability or lower-bound line of $14 in 2017 PPP per person per day and a middle-class or upper line of $81 in 2017 PPP. These thresholds are robust to expanding the set of countries, widening the time interval, increasing the number of repetitions, and implementing previous methodologies.

This study analyzes the impact on the LAC regional poor, vulnerable, and middle-class groups estimate using the international poverty lines (IPL), the regional vulnerability and middle-class lines, and the 2017 PPPs. While the changes at the upper-middle-income line ($6.85 in 2017 PPP) are significantly larger, the 2017 PPPs have small implications for extreme poverty and poverty at the lower-middle-income line ($2.15 and $3.65 lines). Extreme poverty and poverty at $3.65 would increase marginally by 0.3 and 0.9 percentage points (pp.), respectively in 2017, with the 2017 PPPs. The regional count of the extreme poor increased by 1.9 million, largely driven by 1.3 million and 300,000 more poor people in Brazil and the Andean region, while poverty increases in all other subregions except the Southern Cone. Considering the upper-middle income country line, the incidence of poverty in the region rose around 5 percentage points in 2017 with the 2017 PPP. This represents 27 million more poor people in LAC driven by 12 million and 5.5 million in Brazil and the Andean region, respectively.

A significant increase in the poor population is mirrored by a decrease in the size of the vulnerable group by around 5.4 pp with the revised lower-middle-class bound of $14 in 2017 PPP. The regional vulnerability trend is due to declines in the Andean region and Central America offset by increases in countries in the Southern Cone, including Brazil, since 2012. With the update of the upper-middle-class threshold of $81 in 2017 PPP, the size of the middle class slightly increases regionally by less than 1pp and remains the largest socioeconomic group in the LAC region since 2010. Between 2010 and 2020, changes in the middle class at the LAC level are relatively small as increases in the three subregions (i.e., Andean, Central America, and Southern Cone) and Mexico are offset by Brazil. This change in lower and upper middleclass limits provides a similar positive trend for this group over the last two decades. More importantly, these updates in thresholds do not significantly impact the profiles of the poor, the vulnerable, or the middle-class populations, which yield similar policy choices.

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Using the approach based on the risk of falling into poverty, as proposed by Lopez-Calva & Ortiz-Juarez (2014), is conceptually more accurate in defining the lower bound of the middle class for country-specific analysis. Interestingly, it has been found that despite using an alternative approach, this paper’s results align closely with their findings, which is a valuable contribution to the discussion on middle-class thresholds. Moreover, while the risk-based approach is highly relevant and useful for defining the lower end of the middle class, it does not address the upper end of the spectrum. The proposed methodology in this paper simultaneously tackles the lower and upper bounds of the middle class, using a consistent methodological approach. Doing so provides a comprehensive and nuanced understanding of the middle class in Latin America, which can be of great value to policy makers and researchers alike.

Despite offering valuable insights into the socioeconomic structure of the region, this methodology has certain limitations that should be acknowledged. First, the synthetic panel construction using labor market surveys is subject to potential biases and measurement errors inherent in self-reported income data. These biases may impact the accuracy of the estimated bounds for the middle class. Additionally, the synthetic panel data may not capture the full extent of income mobility based on cross-sectional surveys rather than longitudinal data. Second, this approach does not explicitly account for factors such as social protection systems, access to quality education and health care, or other non-income dimensions contributing to an individual's vulnerability or resilience to falling into poverty. As a result, the proposed methodology may not fully capture the complex dynamics that define the middle class in Latin America. It may overlook essential aspects related to social inclusion, economic security, and quality of life. Lastly, this method may need to comprehensively understand the heterogeneity within the middle class. This group is diverse, encompassing households with varying levels of vulnerability. This approach may not adequately capture these nuances, which are essential for designing effective policies to foster inclusive growth.

While the present approach offers a sound perspective on defining the middle class in Latin America, it is important to recognize its limitations and consider complementary methodologies to better understand the complex dynamics of this socioeconomic group. By acknowledging these limitations, this paper aims to contribute to a richer and more informed debate on the region's identification and characterization of the middle class.

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