Demographic Transition in Latin America: A Heterogeneous Process

Latin America’s population growth will significantly slow down and then fall before the start of the next century. According to UN population projections, by 2050, the population will reach 734 million, a minor increase of 17 percent with respect to the 2015 population level (620 million), with a significantly different age structure. In effect, as the number of children falls and the number of elders increases, the region is experiencing a progressive aging of its population. This process is the result of diminishing mortality rates and reduced fertility rates.

Because of these changes, the population has been aging at an accelerated pace. In 1950, children under fifteen represented 40 percent of the Latin American population; today they account for less than 30 percent, and by 2100 their weight will be reduced to 15 percent. On the other hand, the proportion of elders over sixty-four years of age has gone from 3.5 percent to 7 percent between 1950 and 2015; by 2050, they will surpass the weight of children (Figure 1). These transformations in the age composition will inevitably affect the dependency ratio (CELADE 2014). This decline in the dependency rate may produce economic benefits known as the “demographic dividend” or “demographic window” (Reference Bloom, Canning and GrahamBloom, Canning, and Graham 2003), given the associated increases in per capita income and also in physical and human capital per capita. This means that countries are in a favorable situation to generate savings. Again, there is variation concerning the level of progress in this phase in the region: in some countries, this window is yet to persist for many years, while in others it is close to its end.

Figure 1 Latin American population by sex and age group (in thousands). Data from ECLAC population database, 2017 revision.

The paces of these changes are very different among countries in the region. As presented in Figure 2, the proportion of children under fifteen ranges from 20.8 percent in Chile to 36.2 percent in Honduras. In general terms, the weight of the elderly moves in the opposite way among countries.

Figure 2 Proportion of population under fifteen and over sixty-five by country, 2015. Data from ECLAC population database, 2017 revision.

Demographic transformations have enormous implications in terms of public policy. For example, the state may be able to invest fewer resources in education because of the decline in the number of children, but it will have to invest more in pensions and health (CELADE 2014). Whatever the structure of pension systems, labor scarcity induced by population aging will constitute a serious challenge. Policy changes in terms of the retirement age, the contribution rates, or the way to finance potential deficits in pensions systems are among the aspects discussed at present in the region (see Reference ArenasArenas 2019; Reference Cavallo and SerebriskyCavallo and Serebrisky 2016; among others). Gragnolati et al. (Reference Gragnolati, Apella, Rofman and Troiano2014) find that, with the current policies, in Argentina, public spending would increase from 20 percent of GDP to 27 percent by 2050; spending on pensions would increase from 11 percent in 2010 to 15.5 percent of GDP in 2050. Rofman, Amarante, and Apella (Reference Rofman, Amarante and Apella2016) present similar findings for Uruguay: social public expenditure would remain stable in about 20 percent of GDP even when the public spending in pensions decreases because of the 1996 pension reform that increases dramatically the weight of the private pension spending.

In many countries, the demographic dividend will still last for several years; in others, it is closer to its end. In all cases, it is relevant to analyze the economic influence of changes in the age composition of the population. To consider how changes in age structures affect economic performance in the region, we group countries with similar characteristics and present full results for only one country per group. These groups are not intended as analytical categories but as instrumental ones, to help in the visualization of results. The grouping of the countries was based on seven variables that inform of the stage of the transition: fertility rates and life expectancy for 2010–2015, female and male labor force participation around 2015, female labor participation growth rate between 1990 and 2015, and demographic dependency rate for 2015 and 2050 (proportion of the population under fifteen and over sixty-four to working-age population).

The clusters were created using the nearest-neighbor clustering procedure. This technique creates a hierarchy of clusters by repeatedly grouping pairs or smaller groups of countries to form larger groups. The objective is to find groups whose individual members are as similar as possible within the group, and as different as possible between groups. It operates as an optimization problem of finding the point in a given set that is closest to another point, expressing closeness in terms of a dissimilarity function. The dissimilarity is defined by the distance between countries in the seven variables mentioned before. These variables are standardized for the inclusion because groups are defined based on the distance between countries, and the magnitudes of the variables must not interfere in this measurement. For a review on cluster analysis see (Reference Kaufman and RousseeuwKaufman and Rousseeuw 1990). Based on a graphical analysis of the clustering procedure (dendogram), five clusters were created.

Its main characteristics are illustrated in Table 1. Cluster 1 (Guatemala, Honduras, and Nicaragua) includes the countries with the least developed demographic transition and a very low female labor force participation; cluster 2 (Brazil and Uruguay) countries have a major advance in the demographic transition and high female labor force participation; cluster 3 (Bolivia, Colombia, Paraguay, and Peru) presents lower progress in the demographic transition and high female labor force participation; cluster 4 countries (Argentina, Chile, Costa Rica, and the Dominican Republic) characterized by their advanced demographic transition and low female labor force participation; cluster 5 (Ecuador, Panama, Venezuela, El Salvador, and Mexico) presents an intermediate demographic transition and an intermediate female labor participation rate.

Table 1 Cluster-average value of variables used for clusters.

Sources: (1) UN World Population Prospects and (2) CEPALSTAT, based on household surveys. The reference year may change between countries because of availability of household surveys; the year used is the closest one to 1990 and 2015.

It is important to note that these clusters are only based on demographic dimensions, although country heterogeneity in the region goes beyond demography. Institutional contexts may mediate in the potential effects of demographic changes on labor participation and income and affect the results of our estimations. However, these demographic clusters are similar to groupings elaborated in the literature on social protection in Latin America, which has highlighted the heterogeneity in the development of social institutions. In a myriad of previous papers (e.g., Reference Ocampo and Gomez-ArteagaOcampo and Gomez-Arteaga 2017; and Reference FranzoniFranzoni 2008, among others) the region’s countries are grouped in three categories that mirror our cluster 2 and cluster 4 (advanced or comprehensive social protection systems), cluster 2 and cluster 5 (intermediate), and cluster 1 of our grouping (limited or weak systems). Four countries are differently classified in the demographic approach followed in this article: the Dominican Republic, which is included in the first group but is generally included in the intermediate cluster; and Bolivia, El Salvador, and Paraguay, included in the intermediate group, while they are classified as limited or weak social protection systems.

The countries selected to represent each group are Chile, Mexico, Brazil, Guatemala, and Peru. We footnote those cases where important differences are found between the selected representative country and another member of that cluster.

Latin America: Population Projections and Present Activity and Productivity

Before considering our results regarding labor supply, dependency ratios, and productivity, it is useful to illustrate the underlying population projections. As explained above, we present results for only one country in each cluster (Chile, Mexico, Brazil, Guatemala, and Peru).

Projections by the United Nations, used in scenarios 1 and 2 (Figure 3), are very similar to the ones adopted by the Wittgenstein Centre for scenario 3 (see Figure A.1 in the online appendix). Both present significant differences by country. Among the five selected countries, Guatemala has the highest growth rate: its population will grow by 66 percent between 2015 and 2050. Peru and Mexico have intermediate growth rates of approximately 30 percent. Chile, and especially Brazil, will grow at a slower pace (17 percent and 13 percent, respectively).

Figure 3 United Nations population projections by age groups, 2015–2050. Data from United Nations (2017).

Guatemala is the least advanced country in terms of the demographic transition. Currently, 36 percent of its population is under fifteen and elders (over sixty-four) represent 5 percent of the total population. Although the elder population will grow over the next thirty years, its weight in 2050 will still be less than the weight of children (11 percent vs. 23 percent). Mexico and Peru show a similar pattern than Guatemala, though they are slightly more advanced in the transition. Children under fifteen account for 27 percent of the population, the working-age population accounts for 65 percent, and elders over sixty-four are close to 6 percent. By 2050, the weight of the working-age population will remain virtually unchanged, though the proportion of children will fall to 18 percent, while elders will also represent close to 18 percent of the population. Finally, in Brazil and Chile, the transition is the most advanced: children represent less than a quarter of the population and elders approximately 10 percent. Note that the starting point for these two countries is similar to the distribution of Guatemala in 2050. By 2050, the elderly population in Brazil and Chile will amount to 25 percent of the population, while children will decrease to 15 percent (Figure 3).

Important differences among countries also emerge when considering educational levels in 2015 and their future evolution (Figure 4). When we consider population projections by the Wittgenstein Centre, used in scenario 3 to reflect changes in educational attainment, we find that in Guatemala the educational level is significantly lower than the other countries, with two-thirds of its population having primary education or less. Brazil and Mexico show a similar proportion of their population with low education (around 40 percent) but differ in the distribution of the intermediate levels, with a higher proportion of incomplete secondary in Mexico. The Peruvian and Chilean populations are the better-educated ones, with more than 50 percent of their population finishing high school. All the countries considered would experience an important increase in their educational level by 2050 if the global and national trends continue. The differences among countries would still be relevant, but the ordering remains unchanged: Chile and Peru with better educational results, followed by Brazil and Mexico, and Guatemala last with an important difference with the rest. Except for Guatemala, the proportion of the population with primary education or less would be under 20 percent and the population with complete tertiary education would almost reach 20 percent, peaking at 35 percent in Peru. Again, the situation of Guatemala in 2050 would almost mimic the one of Brazil in 2015.

Figure 4 Population structure by educational levels, 2015 and 2050. Data from Wittgenstein Centre (Reference Lutz, Goujon, Samir, Stonawski and StilianakisLutz et al. 2018) and UN population projections.

Turning to present labor force participation, Figure 5 shows activity rates according to sex and age groups in 2015, while additionally illustrating the Swedish female rates. As mentioned before, in scenario 2 we project rates as if women’s participation in Latin America would equal the participation of Swedish women. This implies a substantial change in activity rates. In 2015, 83 percent of Swedish women between the ages of twenty-five and sixty are active in the labor market; female activity rates do not reach this figure in any Latin American country. Swedish women tend to stay very active until they reach sixty-four (the retirement age in Sweden is sixty-five years for the guaranteed pension scheme and flexible between sixty-one and sixty-seven for earnings-related pensions), the age at which their activity decreases drastically. From this age on, Latin American female activity rates are higher than those of Swedish women, who have already retired from the labor market.

Figure 5 Labor force participation rates by sex and age group, 2015. Data from national household surveys, around 2015.

The lowest rates of female participation in the labor market are observed in Mexico (45 percent) and particularly Guatemala (41 percent). In these countries, the present gap with Swedish women is extremely large. For example, even when considering the highest level of participation in Guatemala (53 percent in the forty to forty-four age group), this would have to increase by 39 percentage points to reach the activity rate of Swedish women in that same age group. On the other end, Peru has the highest rates of female participation in the region (63 percent). Therefore, the difference with Swedish women is reduced to less than 15 percentage points among those under sixty-five. Although the Chilean total female participation rate is lower than in Brazil (48 percent vs. 54 percent), this difference is explained mainly by the elevated rates among young Brazilians. When comparing women over twenty-four years old, the rates between these two countries are very similar. Compared to Swedish rates, the differences increase with age (until sixty-four), reaching up to 35 percentage points (Figure 5).

The activity rates used for scenario 3 require further detail, by age group, sex, and education level. In every country, the gender differences persist for all the educational levels but become smaller as the education level increases. In addition the differences between educational levels are smaller among men than among women.

The weaknesses of productivity dynamics in Latin America have been widely debated in the literature (see Reference Daude and Fernández-AriasDaude and Fernández-Arias 2017; Reference Rodríguez-Apolinar and Fernández-AriasRodríguez-Apolinar and Fernández-Arias 2016; and Reference CaselliCaselli 2016; among others) and the consensus situates lagging productivity as a main problem for the region. The disappointing relative labor productivity of Latin American countries in relation to that of the United States (Figure 6) results in the fact that, in 2016, labor productivity in Latin America represented less than 30 percent of the labor productivity of the United States. Caselli (Reference Caselli2016) argues that differences in efficiency in using the production functions explain a large part of Latin America’s persistent income gap. These differences in efficiency arise mainly from resource misallocation (instead of factors such as technology adoption). Many authors agree that the fall in relative labor productivity can be explained by the loss of higher productivity jobs in Latin America, particularly in the industrial sector, and the migration of workers to other less productive activities, such as the services sector (ECLAC 2012; Reference McMillan and RodrikMcMillan and Rodrik 2011; Reference CaselliCaselli 2016).

Figure 6 Labor productivity in Latin America as percentage of labor productivity in the US, five-year moving averages, in PPP. Data from OECD/CAF/ECLAC, based on The Conference Board Total Economy Database™, May 2016.

In any case, the structural factors behind disappointing labor productivity growth in Latin America may be reinforced by the ageing process. As discussed above, the effects of workforce aging on productivity can operate in different ways and are a debated issue. The analysis of future trajectories of productivity is a complex task. In this article the wages of each group (by age, sex, and education level) are used as a proxy for productivity, and the ratios between the groups are assumed to remain unaltered over time. We are aware that this is a strong assumption, as wage gaps by educational groups are a result of the supply and demand of workers in each group and so may vary in the medium and long run as educational attainment improves. Despite the simplicity of this assumption, it is usual in exercises of static microsimulation whose main objective is to discuss potential future evolutions.

We consider the mean income earnings for active people, valued in 2011 productivity per capita dollars, by age and sex for the first two scenarios and also by educational level for the last one. The differences in the income level (a proxy of productivity) in our groups of countries are very important, with Guatemala at the lower end and Chile at the highest one (Figure 7). In all countries and for all ages, women’s earnings are lower than men’s.Footnote ⁸ Also, the wage structure through age is similar in all the countries considered, with the highest earnings between the ages of thirty and fifty-five, approximately. Note that in the first and second scenarios, no assumption is made about educational levels. Implicitly, it is assumed that the structure is the same as the observed in the current active population, to maintain the average wages within the age and sex groups.

Figure 7 Average labor income for active people by country, 2015 (in PPP dollars). Data from national household surveys, around 2015.

Results

We present the evolution of three key variables: labor force participation rate, which reflects the labor supply; the dependency rate, measured as the ratio between the inactive population (people under fifteen and those over sixty-four that do not participate in the labor market) over the active population; and productivity of the workforce, measured through labor income (by sex, age, and educational group) in 2015.

Figure 8 presents the evolution of the activity rate for the five selected countries. There are major differences between the countries and between the scenarios for each country regarding both levels and evolution. In the demographic scenario, the activity rate declines in three of the five groups of countries (Table 3). This means that the expected evolution of the age and sex composition of the population will force the activity rate down in most of the region’s countries, as there will be a rise in the proportion of the elderly, whose participation in the labor market is lower. This raises the challenge of maintaining the level of production with a significantly smaller workforce.

Figure 8 Labor force (over fifteen) participation rates by county and scenario, 2015–2050. Data from United Nations (2017) and Wittgenstein Centre (Reference Lutz, Goujon, Samir, Stonawski and StilianakisLutz et al. 2018).

Table 3 Variation of activity rates (ages 15+) between 2015 and 2050, in percentage points.

Sources: United Nations (2017) and Wittgenstein Centre (Reference Lutz, Goujon, Samir, Stonawski and StilianakisLutz et al. 2018).

The effects are most pronounced in the countries where the demographic transition is most advanced, represented by Brazil and Chile, which reach the end of the period with significantly lower activity rates (with reductions of 10 and 7 percentage points, respectively). At the other extreme, the countries represented by Guatemala and Peru are at no risk of their active populations diminishing because of demographic factors, as their demographic transition is still in the early stages. Results for all countries in 2015 and 2050 are presented in Figure A.2 in the appendix.

When this rate is observed at the central ages (between twenty and fifty-nine) in the demographic scenario, the declines are much more moderate, indicating that the observed reduction is mainly explained by the rise in the relative size of the older age groups (Appendix Figure A.3). In Brazil the activity rate in these ages falls by 3 percentage points, in Mexico and Chile by just 1 point, and it remains unchanged in the rest of countries.

Results from the first scenario are likely to overestimate aging effects, as no behavioral changes are included. The other two scenarios consider factors that can directly influence the population’s activity levels: large-scale incorporation of women into the labor force and rising levels of education.Footnote ⁹

When female activity rates are assumed to increase, overall activity rates improve in all countries compared with the effect of only considering demographic factors of the first scenario (Figure 8). Activity levels in Guatemala and Mexico would also be higher than the observed levels in 2015, a finding explained by their low level of female activity and younger population structure. In these countries, the female participation rate would grow by approximately 20 percentage points (Table 3). In Chile and Peru, the large-scale incorporation of women into the labor market would mitigate the negative effects of demographic developments.Footnote ¹⁰ Indeed, the activity rate would increase in the medium run before the effect is offset by the weight of demographic factors. Conversely, rising female participation in the workforce in Brazil would not be enough to maintain the current activity levels, and the overall activity rate would drop by about 5 percentage points. When considering central ages (twenty to fifty-nine), the growth in activity rates is even larger in all countries, showing increases even in Brazil (Appendix Figure A.3), implying that the decreases observed in Brazil relate to demographic factors.

Lastly, in the increased education scenario, the trends are similar to those seen in the demographic scenario, though changes are smaller. The assumptions underlying this scenario produce two effects. On the one hand, increasing the level of education would push the activity rate up, because more-educated people have higher levels of participation (both by age and by sex). On the other hand, increasing the education levels has effects on fertility and mortality that strengthen the effects of the demographic shift, bearing down on the activity rate. In Guatemala, female participation rates increase by 5 percentage points, while men’s rates remain practically stable. In the remaining countries, female activity is reduced, but to a lesser extent than that observed in the demographic scenario (Table 3). Results for all countries are presented in Appendix Table A.1.

Variations in the labor supply, in addition to demographic changes, modify the dependency ratio, understood as the number of people that each active person must sustain. In general, as population ages, the dependency ratio grows, given the increase in the elderly population (that do not participate in the labor market). However, reduced fertility rates exert decreasing pressure on the dependency ratio. Additionally, the inclusion of new workers in the labor market also decreases the dependency ratio, while any change that implies fewer workers will produce an increase in the ratio.

In the demographic scenario, trends in dependency rates are consistent with each country’s progress in the demographic transition (Figure 9). In Brazil and Chile, dependency rates would increase in the analyzed period: despite the fall in the number of children, there is a greater increase in the number of elders and an important decrease in the number of workers. In contrast, in Mexico, Peru, and (to a greater extent) Guatemala, decreased fertility rates would generate important reductions in the dependency rate. It must be noted that, by the end of the analyzed period, most countries would have approximately one inactive person for each active person. The only exceptions are Peru—which would have more than one active person for each dependent—and Chile, which would have 1.15 inactive persons per active one.

Figure 9 Dependency rate by country and scenario, 2015–2050. Data from United Nations (2017) and Wittgenstein Centre (Reference Lutz, Goujon, Samir, Stonawski and StilianakisLutz et al. 2018).

The reduction of fertility rates presses downward on the dependency rate in every country. When we do not consider children in the dependency rate and instead measure it as the ratio between the inactive population over fourteen and the active population, the dependency rate increases in every country. In Mexico and Peru, this last measure increases while the global dependency rate (total inactive population per active population) decreases. This means that the fall in the number of workers and the increase in the number of inactive elders is compensated by the substantial fall in the number of children.

In scenario 2, the inclusion of new workers results in a reduction of the dependency rate compared to scenario 1. Under this context, every country has more than one active person per dependent. By the end of the period, Brazil has the largest value, switching places with Chile. This is explained by the low female participation rates in Chile compared to Brazil. These results reveal the potential of female labor force participation in light of the demographic processes of the region.

In the third scenario, we observe improvements in the dependency ratio compared to the first scenario, though these differences are small. This is because higher levels of educational attainment have ambiguous effects on the dependency ratio, as was mentioned before. Only in Brazil and Chile are changes in the dependency ratio positive. To sum up, with increasing female participation or—to a lesser extent—a more educated workforce, the inclusion of new workers results in the stability of the dependency rate compared to the demographic scenario, except for Guatemala, where an important reduction prevails (see Table 4). Results for all countries in 2015 and 2050 are presented in Appendix Figure A.4.

Table 4 Variation in dependency rates between 2015 and 2050, in percentage points.

Sources: United Nations (2017) and Wittgenstein Centre (Reference Lutz, Goujon, Samir, Stonawski and StilianakisLutz et al. 2018).

Because changes in the composition of workers affect the value of their contribution to the production process, it is worth considering the productivity changes implicit in each of the scenarios, using wages as a proxy. As discussed above, the effects of aging on productivity are controversial and difficult to predict. In our exercise, we project changes using the available information to illustrate potential trends. The evolution of per capita productivity can be explained by three components: (1) the number of workers (the more workers there are, the greater the total productivity), (2) the contribution of each worker, which can be summed up in productivity per worker that depends on the age, sex and education level of the workers, and (3) the change in the country’s population, that changes the denominator of the per capita productivity. Figures 10 and 11 present the evolution of productivity per capita and per worker, respectively.

Figure 10 Productivity per capita (PPP dollars) by countries and scenarios, 2015–2050. Data from United Nations (2017) and Wittgenstein Centre (Reference Lutz, Goujon, Samir, Stonawski and StilianakisLutz et al. 2018).

Figure 11 Productivity per worker (PPP dollars) by countries and scenarios, 2015–2050. Data from United Nations (2017) and Wittgenstein Centre (Reference Lutz, Goujon, Samir, Stonawski and StilianakisLutz et al. 2018).

In the purely demographic scenario, the differences observed in the trends of per capita productivity are explained by the stage in the demographic transition that countries have reached.Footnote ¹¹ Countries at the early stages of the transition (Guatemala, Mexico, and Peru) would experience substantial increases in aggregate productivity over the thirty-five years considered because of the evolution of the demographic composition of their population.Footnote ¹² However, the productivity levels will remain low in comparison with the other countries of the region. Countries where the transition is most advanced (Brazil and Chile) would present reductions in productivity, albeit not very large ones (less than 5 percent). The main channel through which these changes are processed is the change in the number of people in the working-age group: it increases as a share of the total in the first group and declines in the second. In all countries, though, the share of workers in the most productive age groups (between thirty and fifty) is ex.

However, given that the weight of older workers would increase in the period (and older workers show higher productivity), every country shows improvements in productivity per worker. In Guatemala, Mexico, and Peru, the increases in productivity per capita are greater than the increase in productivity per worker.Footnote ¹³ In contrast, in Brazil and Chile the increase in productivity per worker is slightly diluted because of a reduced number of workers.

When women are incorporated into the labor market, productivity per capita improves in all the countries. This is because the number of people in the labor market increases, significantly raising overall output. However, productivity per worker improves less than productivity per capita. The reason is the assumption that incorporating women into the labor market would not alter the gender gaps currently affecting earnings, which stands in for productivity differences in this exercise. The implication is that the new female workers entering the labor market would have below-average productivity, forcing down productivity per worker. In Guatemala, for example, the productivity per capita increases by almost 50 percent in scenario 2, while productivity per worker only increases by 2 percent. In Peru and Mexico, productivity per capita grows by 15 percent and 26 percent respectively, but productivity per worker remains virtually unchanged.

As expected, the greatest increase in productivity is observed in scenario 3 (Table 5). In Guatemala, the 86 percent increase in productivity per capita means it would surpass Mexico’s productivity levels, even when considering the 41 percent growth in this country. The impact of education is also very strong in Peru, where productivity per capita would increase by 35 percent. Only under this scenario, we observe a significant increase in productivity in Brazil and Chile. The increases in productivity per worker are even higher than those for productivity per capita. The productivity changes in this scenario are very high and respond to an important increase in the educational level and to the assumption that wage differentials between educational groups would not change in response to the modification in the educational composition of the population. While the first factor is plausible,Footnote ¹⁴ a more realistic analysis of the variation of the wage differential would require the application of general equilibrium models, which is beyond the scope of this article.

Table 5 Variation in productivity between 2015 and 2050, percentage variation.

Sources: United Nations (2017) and Wittgenstein Centre (Reference Lutz, Goujon, Samir, Stonawski and StilianakisLutz et al. 2018).

It is relevant to notice that these results about the potential evolution of productivity under the different scenarios consider only the effect of demographic and educational factors. The effect of public and private investment, as well as foreign direct investment, whose relevant role has been underlined for Latin America (Reference GutierrezGutierrez 2006; Reference MonteroMontero 2008), is not considered in our simplified framework. The exercise shows that one way to reverse the decline in overall productivity in the countries more advanced in the demographic transition is to increase education, as the higher productivity associated with this would offset the decline in the number of workers caused by demographic change. The potential adverse effects of population aging on productivity growth shown in the demographic scenario can be reversed if economies rely on a smaller but more productive workforce. Our exercise illustrates the prediction of Lee and Mason (Reference Lee and Mason2010), who state that if small cohorts of workers have high levels of human capital, standards of living may increase even in a context of apparent unfavorable age structure. Also, our exercise does not contemplate how other factors, such as soft skills, may boost productivity (Reference Adhvaryu, Kala and NyshadhamAdhvaryu, Kala, and Nyshadham 2018; Reference Prada, Rucci and UrzuaPrada, Rucci, and Urzua 2019).

Final Remarks

Population aging generates many challenges and concerns in Latin American countries, but these are not homogeneous across the region. In most of the region’s countries (Venezuela, the Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Paraguay, Peru, and Bolivia) the demographic factor is still favorable and there is scope to exploit the demographic dividend and increase the population’s total labor force participation. In these countries, the main problem is low productivity. In another set of countries (Brazil, Chile, Colombia, Costa Rica, and Uruguay), major productivity increases can be achieved by substantial changes in the incorporation of women into the labor market and, especially, in the level of education. Two countries (Argentina and Panama) are in an intermediate situation, with very small productivity gains in the demographic scenario.

A main message of our analysis is the importance of policies that can mitigate the potentially negative impact of an aging workforce, especially in contexts of low productivity. In a region always compelled by short-run tensions and instabilities, the design of policy responses to medium- and long-run challenges constitutes a complex task. In all the countries, to secure the conditions for sustained growth there is a need to create institutional and political conditions for higher productivity by incorporating new workers or increasing their level of education. In the specific case of new workers, given the prevailing gender gaps in labor market participation and in contexts of high informality, policies promoting the creation of formal female jobs, childcare facilities, and the promotion of a system of values centered on gender equality seem crucial steps. The achievement of higher productivity can also benefit from complementary actions in terms of improvement of health outcomes and active labor market policies that help to mitigate the depreciation of skills over the work life and to develop soft skills to enhance productivity.