Abstract
Standard tax and benefit incidence analysis is used to estimate the effects of fiscal policy on poverty and inequality in Peru. Results suggest that the extent of inequality and poverty reduction induced by Peru’s fiscal policy is small. This result is associated with low social spending rather than with inefficient spending. Most social spending components are progressive and overall social spending is also progressive. We find that direct cash transfers are well targeted and are especially effective in reducing extreme poverty in rural areas. We also find that in-kind transfers are effective in reducing inequality. Finally, direct taxes slightly reduce inequality, while, countering intuition, indirect taxes are neutral once informality is incorporated in the estimates.
In the last few decades, Peru has gone from a financially broke state in the late 1980s to an example of fiscally responsible management, in a world where such an attribute has become quite scarce. In effect, tax collections declined through the second half of the eighties, reaching a nadir of 4.9 percent of gross domestic product (GDP) in the first half of 1990, whereas only a decade earlier tax collections amounted to about 15 percent of GDP. In the late 1980s, money printing became the main source of state financing with hyperinflationary consequences. In this context, social services collap-sed. After the reconstruction of the tax system in the early nineties, Peru started expanding social expenditures, mostly through targeted infrastructure investments, but also through not so well-targeted food programs, and a number of rather small-scale programs, such as preschool care centers (wawa-wasis) and immunization campaigns. During the last decade, as the fiscal situation of the country improved, larger scale social protection programs, such as the Comprehensive Health Insurance System (Seguro Integral de Salud—SIS), were implemented. Spending by the social sectors also increased, more than doubling the total social spending in the course of the decade. Only in 2005 was a cash transfer program introduced.
High inequality in Peru is a long-standing and well-known condition. Although there have been considerable advances in the last decades in the reduction of both poverty and inequality, poverty still affects about a third of the population, while inequality levels are quite high by international standards (Jaramillo and Saavedra 2010; López-Calva and Lustig 2010). Improving the redistribution and poverty mitigation effects of fiscal policy is important for Peru’s development, as recent estimates suggest that public transfers and donations are responsible for only one-tenth of the poverty reduction achieved during the last decade (Inchauste et al. 2012).
In this article, standard tax and benefit incidence analysis is used to estimate the effects of fiscal policy on poverty and inequality in Peru. Data to assess the incidence and progressivity of social spending and taxes come from the National Household Survey (Encuesta Nacional de Hogares [ENAHO] 2009) and from government budgetary accounts. Different income definitions are used in order to observe the effects of different taxes and social expenditure items across the income distribution. 1 In the benchmark scenario, contributory pensions are included in the households’ market income, and in a sensitivity analysis, they are treated as a government transfer. The analysis does not include behavioral or general equilibrium effects.
Results indicate that the extent of inequality reduction induced by fiscal policy in Peru is small. This result is associated with low social spending rather than with inefficient spending. Most of the social spending components are progressive and overall social spending is progressive as well. However, social benefits tied to the formal labor market (health and pensions) are either relatively progressive or regressive. In-kind transfers have the largest impact while direct transfers are the most effective per dollar spent in the short run. These two types of transfers are not easily comparable, however, as the former have larger long-run effects through human capital formation. Taxes, on the other hand, have positive though small effects on inequality. Countering intuition, indirect taxes are neutral once we incorporate informality in our estimates. A policy implication deriving from these results is that targeted transfers are the most effective way to reduce poverty in the short run. In contrast, linking benefits to formal employment relationships tends to exclude the poor. Also, results call attention to the role of informality in the neutrality of indirect taxes.
The article is organized as follows: the second section describes the structure of social spending and taxes in Peru. The third section presents the data and the specific assumptions made in the analysis. The fourth section presents the main results. The fifth section presents our conclusions.
Social Spending and Taxes in Peru: A Bird’s-eye View
According to Comisión Económica para América Latina y el Caribe (CEPAL, 2011), in 2009 Peru’s social spending was below the Latin American average of 15.2 percent of GDP. 2 Peru’s total social spending, including central, regional, and local government levels, was 8.4 percent of GDP, which in per capita terms represented only 30 percent of the average for the region. Tax revenue was also below the region’s average: while the average tax revenue in the region in 2009 was 19 percent of GDP, in Peru it was 16 percent. 3 Contrastingly, value-added tax (VAT) revenue in the region averaged 6.4 percent of GDP, while in Peru it reached 7.7 percent (United States Agency for International Development [USAID] 2011). This section provides a description of the structure of benefits and taxes in Peru. The benefits description is limited to the categories incorporated in a comparable social spending definition called Commitment to Equity (CEQ) social spending. 4 It includes spending on social assistance, education, and health. Although not included in the CEQ social spending definition, pensions are also described, as they are used in a sensitivity analysis. The tax description includes government tax revenue as well as the main social contributions collected by the government. table 1 sets out these data.
Peru: Government Spending and Revenue by Category (as percentage of GDP), 2009.
Sources: Social spending from Sistema Integrado de Información Financiera and Unidad de Estadísticas del Ministerio de Educación. Taxes from Superintendencia Nacional de Aduanas y Administración Tributaria. Government spending from Banco Central de Reserva del Peru and International Monetary Fund (2011).
Note: CEQ = Commitment to Equity; GDP = gross domestic product; PPP = purchasing power parity; VAT = value-added tax.
aTotal government spending includes central, regional, and local government levels. bThe social spending definition here presented departs from Peru’s official public accounts in that it includes contributory health spending and it does not include pensions-related expenditures.
Social Benefits
Social Assistance Spending
In 2009, social assistance spending was 1.6 percent of GDP, 59 percent of which concentrated on social infrastructure programs. The remainder 0.64 percent of GDP was distributed among social programs that target poor households. Of these the largest portion went to food transfer programs, which represented 0.25 percent of GDP in 2009. On that year, the most important food programs, representing 98 percent of this line of social expenditure, were three: Programa Integral de Nutrición with 3,792,261 beneficiaries and an average annual transfer of 120 soles (purchasing power parity [PPP]US$71 or 19.5 cents per day) per beneficiary, Programa de Complementación Alimentaria with 306,762 beneficiaries and an average annual transfer of 375 soles (PPPUS$221 or 60 cents per day), and Vaso de Leche program with 3,215,100 beneficiaries and an average annual transfer of 100 soles (PPPUS$60 or 16.4 cents per day).
In 2005, a means-tested conditional cash transfer (CCT) program, Juntos, was introduced in Peru’s rural areas. The program targets rural poor families with children under fourteen or pregnant women. Qualifying families receive a transfer of 100 soles per month (PPPUS$60 or 50 cents per day for a family of four members) conditional on their children attending school and having regular checkups in a public health facility. If the beneficiary is a pregnant woman, then the transfer is conditioned on her attending prenatal checkups in the local public health facility. In 2009, Juntos represented 0.15 percent of GDP and had 409,610 beneficiaries. Since then, it has expanded significantly: between 2009 and 2012, the program’s budget increased 45 percent.
Education Spending
Education spending, including both basic and tertiary education, was 2.8 percent of GDP in 2009. Basic education includes three levels: preschool education (ages three to five), primary education (ages six to eleven and grades 1 through 6), and secondary education (ages twelve to sixteen and grades 7 through 11). In Peru, basic education is mandatory and free in public schools. Basic education represented in 2009 close to three-quarters (71.3 percent) of education spending. Primary education was almost half (48.6 percent) of basic education spending. Tertiary education includes both university and vocational education and training. Spending on the latter was barely 1.6 percent of total education spending while university spending represented 19.3 percent.
Health Spending
In 2009, public health spending in Peru was 3.1 percent of GDP. Public health services, which cover 96 percent of the population, are divided into a subsidized regime (1.9 percent of GDP) and a contributory regime (1.2 percent of GDP). In the subsidized regime, the government provides health services to the uninsured population in return for either out-of-pocket payments that cover subsidized tariffs established by the health facility or no payment at all if they qualify as beneficiaries for a means-tested free health insurance called SIS. This subsidized regime covers 75 percent of Peru’s population, about one half of them (12 million) through SIS. health service provision in this regime comes from the health ministry’s hospitals and other facilities. Spending on the subsidized regime includes public spending in hospitals and other health facilities (individual health spending), as well as in the SIS. An additional category within this regime is collective health spending, which includes spending on health-related activities that have communities or specific population groups as beneficiaries.
The contributory regime, on the other hand, is part of the traditional social security system and focuses on formal sector workers and their families, which add up to 21 percent of the population. The contributory health insurance is called EsSalud and provides health services through its own facilities.
Pensions
In 2009, all pension systems were contributory in Peru. Since 1993, two systems have coexisted in the country: the national pension system (Office National des Pensions [ONP]) and the private system of pension fund management firms (Administración de Fondos de Pensiones [AFP]). Enrollment in one of the two schemes is mandatory for dependent workers in firms with more than ten employees and optional for independent workers and workers in firms with fewer than ten employees. In 2009, 47.5 percent of wage earners were affiliated to one of the systems; 12 percent was affiliated to the national pension system; and 33.5 percent to the private system. 5 Although the number affiliates in the private system is higher than in the public system, the number of pensioners under the public system is considerably higher than in the private sector. There are 331,780 retired pensioners under the public system and only 41,803 retired pensioners under the private system, representing 21.2 percent of the population of sixty-five and older. The national pension system, managed by the government, operates under a common-pool, pay-as-you-go financial scheme while the private system works under individual retirement accounts.
Ever since the hyperinflation process in the eighties liquidated the ONP’s assets, contributions have been significantly lower than the cost of providing pensions. The ONP has a structural deficit and consequently public transfers have been necessary over the last two decades in order to fund its liabilities. As shown in table 1, the national pension system, measured by the value of pensions paid, represented 0.9 percent of GDP in 2009. The subsidy included in the cost represented 0.4 percent of GDP. In contrast, the value of pensions paid in the AFP system was 0.1 percent of GDP. Average pension benefit in ONP was PPPUS$12 per day, while in AFP it was PPPUS$18.
Late in 2011, a noncontributory pensions program targeting individuals sixty-five and older in extreme poverty was implemented. Pension 65 is the name of the program and by December 2012, it had 247,673 beneficiaries, 75 percent of the extreme poor in that age group in 2009. Beneficiaries receive 125 soles per month (PPPUS$75), which is above the 99 percentile of the extreme poverty gap indicator, so it should lift virtually all of its beneficiaries out of extreme poverty. Perfect coverage by this program would result in a reduction in extreme poverty of about 7.3 percent.
Taxes and Social Contributions
The main taxable items in Peru are income, consumption, and imports. Property taxes are collected at the local level and the tax authority reports them within the “other taxes” category. As table 1 shows, most of the tax revenue comes from VAT collection and income taxes. Only a third of income tax revenue comes from personal income. The third tax in importance is the excise tax (impuesto selectivo al consumo [ISC]), with a tax on fuels as its main component. The “other taxes” category includes mainly import tariffs and property taxes.
Income Tax
Income tax in Peru applies a progressive rate on personal income and a flat rate on corporate profits. Corporations residing in Peru are subject to a 30 percent tax rate on reported profits. In the case of dividend distributions, an additional rate of 4.1 percent is levied. Personal income tax brackets are calculated on the basis of a tax unit (unit investment trust [UIT], worth approximately US$1,241 in 2009). The personal income tax has four brackets: an exempted bracket for taxable income up to seven UITs, 15 percent for taxable income between seven UITs and twenty-seven UITs, 21 percent from twenty-seven to fifty-seven UITs, and 30 percent for income above this amount.
VAT
In Peru, the VAT is called the general sales tax (impuesto general a las ventas [IGV]). It is levied on each transaction at the different stages in the production of a taxed final good or service, generating a tax credit toward the following stage, so that ultimately it is the consumer who pays the tax. Following international trade practice, the IGV is not applicable to exported goods. IGV taxes paid to produce export goods are refunded. In 2009, the applicable IGV tax rate was 19 percent. The IGV tax is generally applicable to every transaction, but a few exemptions are in place for either specific goods or goods exchanged in the Amazonian region. The largest and most important exemptions are those associated with unprocessed foodstuffs.
Excise Tax (ISC)
This tax is applied to alleged luxury goods, including cars, liquor, jewelry, soft drinks, among others, and to fuels. The largest portion of total revenue from this tax is obtained from the ISC on fuels. ISC rates vary with the product. In the case of certain goods, such as beer and fuels, the ISC is calculated on a specific basis depending on the amount sold or imported.
Contributions to Social Security
The two main contributions are those made toward health insurance (EsSalud) and to the national pension system (ONP). The contribution rate for EsSalud is 9 percent, and the contribution rate for the ONP, and for the private pension system as well, is 13 percent. Employers are liable for the EsSalud contributions while the ONP/SPP contributions are deducted from the employee’s paycheck.
Data and Assumptions
The main data source used throughout this analysis is the National Household Survey (ENAHO), produced annually by the National Institute of Statistics (Instituto Nacional de Estadística e Informática), in its version for 2009. The survey has national coverage and collects data on all household members. Household members fourteen years or older report in the survey whether they pay direct taxes, receive cash or food transfers, attend school, are affiliated with public health insurance programs, and whether they attended public health facilities when they had health-related issues. Households also report detailed consumption and income data. Income estimates include reported self-consumption in both urban and rural areas. The data available allow us to estimate the incidence of personal income tax, cash transfers, food transfers, indirect taxes, education services, health insurance programs, and public health services utilization. It also allows us to estimate the value of pensions funded through the public system as well as contributions to this system.
We have been able to produce estimates for most of social spending and tax items identified earlier. Most of the estimated taxes and benefits were directly identified from the survey. However, we use data from other public sources, such as the Finance Ministry’s National Financial Information System (Sistema Integrado de Información Financiera) and the Education Ministry’s Statistics Unit (Unidad de Estadísticas del Ministerio de Educación), to assign the amounts to in-kind health and education benefits. To estimate indirect taxes, we use the detailed consumption data from the household survey as well as data from the National Superintendence of Tax Administration (Superintendencia Nacional de Aduanas y Administración Tributaria [SUNAT] 2012) for scaling up.
Because there is no reliable way of linking them to household income, the main spending categories left out of the analysis are infrastructure social assistance spending and collective health programs. The smaller social assistance programs are also left out due to data limitations; that is, the household survey does not identify participation in these programs. Not included taxes are corporate income tax, excise taxes applicable to goods other than fuels, and other taxes such as import duties and property taxes. In the case of contributions to EsSalud (contributory health insurance), the assumption is that the employer bears its cost.
The indirect taxes identified are the VAT and the excise tax on fuels. The amount each household pays on taxes was simulated by applying the active tax rule to the amount of expenses on each taxed item that the household reported in the survey. An important factor to consider is that tax evasion is high in Peru, especially when it comes to VAT. Official estimates indicate that VAT evasion is about a third of current VAT revenue. As survey data do not allow identification of which of the items purchased by each household paid taxes, assumptions must be made.
Given the nature of our data, VAT payment is identified only at its final stage. 6 The analysis considers that VAT affects the value of a transaction only if conducted under formal conditions. In order to identify which transactions are made under formal/informal conditions, two assumptions are made: (1) all purchases by households made from street vendors, “farmers markets,” or other informal conditions do not pay indirect taxes, and (2) purchases by households in rural villages with 100 households or less do not pay indirect taxes. While justification of the first assumption is straightforward, the second rests on the fact that the government’s fiscal presence on rural areas is quite limited. For instance, only 8 percent of total social security contributions come from rural area. In order to operationalize the first assumption, we exploit the fact that the survey collects data on the type of establishment where a purchase took place for an extensive list of goods and services (465 products just in the food category) in order to identify purchases from formal establishments.
Social Spending, Taxes, and Income Redistribution in Peru: Main Results
Impact on Inequality and Poverty
Table 2 presents the Gini coefficient and the poverty headcount ratio (using international poverty lines and national poverty lines) for both the benchmark case and the sensitivity analysis. Estimation results show that direct taxes, direct transfers, indirect taxes, and in-kind transfers all have equalizing effects. Health and education in-kind transfers have the largest equalizing effects among taxes and transfers. The effects of direct taxes, direct transfers, and indirect taxes are quite small.
Taxes, Transfers, Inequality, and Poverty in Peru: Benchmark Case and Sensitivity Analysis.
Note: Final income* is an alternative definition of income. It is used across the different articles in this special issue and is explained in the Overview article.
Source: Author’s calculations based on Encuesta Nacional de Hogares (ENAHO) 2009 and national accounts.
Note: PPP = purchasing power parity.
Although in-kind transfers have a larger effect on inequality than do direct transfers, these are more effective in reducing inequality in the short run. Effectiveness can be measured as the redistributive effect of the transfer divided by its relative size as a portion of GDP. Using this metrics, the indicator for direct transfers is 2.42 while it is 1.21 for direct and in-kind transfers in the benchmark case. Do note, however, that these effects are not easily comparable. Direct transfers, such as Juntos, are meant to increase current incomes and encourage the use of health and educational services among the poor. In-kind transfers, on the other hand, are essential for human capital formation, investment that is recovered only in the long run. Thus, the long-run effectiveness of each transfer in reducing inequality may be quite different from their immediate results.
Direct transfers also have a positive effect on poverty reduction. This effect is most important among the extreme poor. Note that poverty reduction is larger in the sensitivity analysis. This is because of two effects: initial incomes are lower and direct transfers are higher. In table 2, we can also observe that the market income Gini coefficient for the sensitivity analysis is marginally lower than that for the benchmark case. As market income in the benchmark case includes contributory pensions while it does not in the sensitivity analysis, one can conclude that contributory public pensions have a small unequalizing effect. Indirect taxes slightly increase extreme poverty and increase total poverty more significantly. Headcount poverty turns out larger after indirect taxes than even before government action.
Historically, poverty has concentrated in the rural areas of Peru. In spite of significant migration in the last half a century, differences in access to key assets, such as education and basic infrastructure (water, electricity, and roads), go a long way toward explaining differences in poverty rates between rural and urban areas (Escobal and Torero 2000). table 3 shows the poverty and inequality effects of taxes and transfers for urban and rural areas. Three important results come out. First, direct transfers achieve much greater reductions in inequality (0.014 vs. 0.002 points of Gini) and poverty (2.9 and 1.9 vs. 0.2 percentage points in extreme poverty and total poverty, respectively) in rural areas than in urban areas. This result reflects both the fact that direct transfers are concentrated in rural areas and that they are means tested, resulting in better targeting. In effect, according to our estimates, 71 percent of the benefits provided as direct transfers (95 percent in the case of Juntos) go to rural areas. Second, as expected, direct taxes have no effect on poverty in either area. However, they have a much larger equalizing effect in urban areas than in rural areas. Third, indirect taxes increase poverty and inequality in urban areas, but, partly because of our informality assumptions, have no effect on inequality and increase poverty only slightly in rural areas.
Taxes, Transfers, Inequality, and Poverty in Urban and Rural Areas, Benchmark Case.
Source: Author’s calculations based on Encuesta Nacional de Hogares (ENAHO) 2009 and national accounts.
Note: PPP = purchasing power parity.
Coverage and Effectiveness of Direct Transfers
Table 4 presents indicators that measure the extent to which direct transfers are effective and efficient in reducing poverty (using both international and national poverty lines). 7 The first column presents estimates of the headcount poverty effectiveness indicator, which is the same indicator used in the previous section only now applied to the effects of direct transfers on poverty. From these indicators, one can conclude that direct transfers are more effective in reducing extreme poverty than in reducing total poverty.
Direct Transfers Poverty Reduction Efficiency and Effectiveness Indicators, Benchmark Case.
Source: Author’s calculations based on Encuesta Nacional de Hogares (ENAHO) 2009 and national accounts.
Note: PPP = purchasing power parity.
The vertical expenditure efficiency (VEE) indicator measures the amount of direct transfers that go to the poor. This indicator shows that 47 percent of direct transfers reach the extreme poor, while 71 percent reach the total poor population (using international poverty lines). The spillover index (S) indicates how much of the spending that reached the poor was in excess of the strictly necessary amount required for the beneficiaries to reach the poverty line. As can be observed, the spillovers are rather small, which suggests that the level of the transfer is well designed. The poverty reduction efficiency indicator is the product of VEE times 1-S. This indicator fares quite well when compared to those obtained for Brazil’s targeted programs (Immervoll et al. 2006). Finally, the poverty gap efficiency (PGE) measures the transfers’ effectiveness in reducing the poverty gap. PGE estimates are similar to those obtained for Brazil, and indicate that in 2009 Peru’s direct transfers were far from sufficient to close the poverty gap. This reflects two problems. The first is low coverage: Juntos and food programs reached only 27 and 36 percent of the poor, respectively. The second is a low value of per capita transfers. While the Juntos transfer covers 20 percent of the daily extreme poverty line (US$0.50 of the US$2.5), the largest food programs at best cover a meager 7.5 percent of the daily extreme poverty line.
Figures 1 and 2 show, respectively, leakage and coverage levels of the direct transfer programs, both separately and jointly. Figure 1 shows quite clearly that Juntos (the CCT program) is a much better targeted program. Only 16 percent of Juntos beneficiaries are nonpoor compared to almost half in the case of food programs. Juntos’s better targeting partly reflects not only its focus in rural areas, where, given the high poverty rates, targeting errors are less likely, but also a more rigorous enforcement of its targeting rule. Results for both programs jointly reflect the fact that food programs have a larger pool of beneficiaries. Figure 2 shows that coverage of food programs is greater than Juntos coverage among both the moderate and the extreme poor. This difference does not seem so large when one considers that Juntos budget is half the budget of food programs and that, as shown earlier, the Juntos per capita transfer is considerably larger than average food programs transfers. Direct transfers jointly cover 58 percent of the extreme poor and 50 percent of the moderate poor.
Direct transfers’ beneficiaries by income group. Source: Author’s calculations based on Encuesta Nacional de Hogares (ENAHO) 2009 and national accounts.
Direct transfers’ coverage by income group. Source: Author’s calculations based on Encuesta Nacional de Hogares (ENAHO) 2009 and national accounts.
Incidence Analysis
Table 5 presents the results of the incidence analysis corresponding to the benchmark scenario. As expected, direct taxes impact only the income of the richest deciles, reflecting the progressive tax rate structure. The effects of direct transfers are consistent with our previous results: both food programs’ transfers and Juntos’s transfers in particular are highly concentrated among the poor. Direct transfers change the first decile income by 11.4 percent, while their effects on the second and third decile are considerably lower. Juntos’s effects on deciles above the fourth are almost nonexistent, while food programs impact households as high as the eighth decile.
Incidence of Taxes and Transfers by Decile, Benchmark Case.
Source: Author’s calculations based on Encuesta Nacional de Hogares (ENAHO) 2009 and national accounts.
Note: CCT = conditional cash transfer; SIS = Seguro Integral de Salud.
Indirect taxes have a significant effect on incomes across the distribution. Counterintuitively, their effects are higher among nonpoor households, an effect that may be a result of high informality levels, as richer households are more likely to buy from formal establishments, while poorer households are more likely to buy products in informal conditions, such as from street vendors or in informal markets. Under the informality assumptions we use, indirect taxes are neutral (Kakwani index: 0.015). Further, dropping the assumption on rural areas does not change the substantive neutrality result (Kakwani index: −0.036).
Finally, after direct taxes, direct transfers and indirect taxes, households in the first decile are net transfer receivers, while households from the second decile on are net tax payers. The analysis changes significantly when health and education transfers are included, as these, as well as public health insurance beneficiaries, are concentrated among the poorest deciles. The public health contributory system is the only transfer with a higher impact on the income of richer deciles. Thus, if we look at the final income, the bottom half of the distribution are net transfer receivers.
Progressivity Analysis
Figure 3 shows the concentration coefficients for the social spending categories identified in this study. The CCT program Juntos is the most progressive program in Peru, followed by food programs. The public health insurance system (SIS) is also progressive, as are all basic education transfers. Tertiary education is only relatively progressive, while the EsSalud transfer (contributory health insurance) is almost regressive. Overall, identified CEQ spending is also mildly progressive. Public pensions, not included in CEQ social spending, are the only identified regressive transfer, their concentration coefficient being 0.58.
Concentration coefficients for total CEQ social spending and by categories. Source: Author’s calculations based on Encuesta Nacional de Hogares (ENAHO) 2009 and national accounts. Note: Social spending includes all cash transfers (except for contributory pensions) and other direct transfers plus public spending on education and health.
Conclusions and Policy Implications
Our findings indicate that the extent of inequality and poverty reduction induced by Peru’s fiscal policy is small. The Gini coefficient falls from 0.504 to 0.463 after all benefits and taxes are considered, while direct transfers and taxes barely reduce the Gini coefficient to 0.489. In-kind education and health transfers have the largest equalizing effect. Direct transfers reduce extreme and total poverty by 1.2 and 0.8 percentage points, respectively. Overall social spending is progressive, although some of its components are only relatively progressive. The less progressive programs are contributory pensions and contributory health insurance, both corresponding to entitlements linked to formal employment relationships and, thus, strictly speaking, they are not transfers except for the fact that the pensions system receives a substantial subsidy that is financed out of taxes. In contrast, the most progressive programs are means tested. The CCT program Juntos is especially well targeted and effective in reducing extreme and moderate poverty. However, because spending on the program is quite small, poverty reduction is limited. As for taxes, we find that direct taxes are progressive, but have little effect on inequality. We also find that once informality is introduced in the analysis indirect taxes are neutral. This result is associated with the high levels of informality in Peru’s economy.
One policy implication deriving from these results is that targeted transfers are the most effective way to reduce poverty in the short run. In contrast, linking benefits to formal employment relationships tends to exclude the poor. Policy makers would do good in keeping this in mind when designing interventions to help the poor. The fact that in 2009, direct transfers achieved a greater reduction of poverty (fourteen and ten times as large in extreme and total poverty, respectively) and inequality (seven times as large) in rural than in urban areas is associated with the fact that 95 percent of Juntos’s benefits concentrated in the rural areas. These results validate the expansion of the program that has occurred in the years following. Preliminary estimates suggest that this expansion may have reduced rural extreme poverty in as much as 5.4 percentage points.
How can this effort be supplemented around a goal of eliminating extreme poverty? One important challenge for Peru’s social policy is how to reform its poorly targeted and corruption-prone food programs. One possibility that should be evaluated is to use its resources for either supplementing Juntos benefits or expanding it faster, including urban areas, thus enhancing its PGE. Also, in 2009, Peru did not have a noncontributory pensions program. Pension 65, a means-tested program targeting individuals sixty-five or older in extreme poverty that started operating in 2012, has come to fill this void. Full coverage of target population, three-quarters of which has been achieved in the first year of operation, would reduce poverty in 7.3 percent. These programs can make a significant contribution to extreme poverty reduction in the short run, particularly in the rural area, which clearly should be priority. However, a sustainable reduction in poverty and inequality requires an effort to close the infrastructure gap between urban and rural areas as well as significant improvements in the quality of basic public services, such as education, which tends to reproduce rather than ameliorate social inequities.
Footnotes
Acknowledgment
Barbara Sparrow provided outstanding research assistance throughout the research that lead to this article. I am also grateful with Nora Lustig, Carola Pessino and John Scott, and two anonymous reviewers for their valuable comments to earlier versions of this article.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.