The main source of data for this paper is the Socioeconomic Database for LatinAmericaandtheCaribbean (SEDLAC), jointly developed by CEDLAS at the Universidad Nacional de La Plata (Argentina) andthe World Bank’s LAC poverty group (LCSPP), with the help of the MECOVI program. This database contains information on more than 150 official household surveys in 24 LAC countries: the 17 countries in continental LatinAmerica -Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay and Venezuela – plus Dominican Republic (a Latin American country intheCaribbean), plus 6 countries inthe non-hispanic Caribbean: Bahamas, Belice, Guyana, Haiti, Jamaica, and Suriname. The sample represents 97% of LAC total population: 100% in continental LatinAmerica, and 55% intheCaribbean. The main missing country is Cuba, which does not disclosure household survey information. Our analysis starts inthe early 1990s, when most countries in LAC consolidated their household survey programs, and ends in 2006. Table 2.1 lists the surveys used in this study. Household surveys in most countries are nationally representative, with the exception of Argentina, Suriname and Uruguay (before 2006), where surveys cover only urban population. This represents nonetheless 88%, 75% and 92% of the total population in these countries, respectively. In these three cases, we use the urban figures as proxies for the national statistics. 1
A 1987 document entitled Financing health services in developing countries: an agenda for reform, is considered a reference point for action inthe health area. It is a World Bank Policy Study prepared by three experts from the bank’s Population, Nutrition and Health Department – John Akin, Nancy Birdsall (then head of the Department), and David Ferranti – based on a set of ideas already circulating inthe institution since the mid-1980s. Although this document had not been approved by the Bank’s board of executive directors, it had already circulated inside the institution and been discussed with the World Health Organisation in an effort to attenuate possible conflicts of ideas and to legitimate it institutionally by toning down some of the political and ideological guidelines for the sector. This document set out clearly the main directions of the health sector reform agenda based on a diagnosis of health service problems that indicated insufficient spending on cost-effective programmes; internal inefficiency of government programmes; and inequity in universal, public health systems. The second part of the document makes suggestions for health sector reform in developing countries that focus basically on four measures: introducing co-payments for use of public health services, especially medical care; giving incentives to develop health insurance; strengthening private service provision; and decentralising. It also includes strategies to be pursued by the Bank to induce these reforms, which are: to include consideration of reforms to health service financing when advancing international loans and aid, to expand loans for these reforms, and to conduct research to sustain them. This discussion is backed by a wide-ranging bibliographical review and presents data onthe countries (Mattos, 2000:227; World Bank, 1987). Hernández (2002) states that this document uses the economic concept of public and private good for all health service matters, drawing a sharp line between the responsibilities of market and State in financing health services.
Nevertheless, the disinflation costs of the 90s are on average negative. The puzzling result contradicts the evidence for developed countries and stands in sharp contrast with the SRs for the LAC countries for the 70s and 80s. We show in this paper that there are three elements that are able to quantitatively explain most of this puzzle. First, the structural reforms of the 90s increased trend output. Since the techniques used to calculate SRs are likely to miss such a shift in trend growth, they mismeasure the SRs. Correcting for that underestimation leads to SRs that are positive, though still very small. Second, the exchange rate appreciation —fueled by the capital inflows that the region experienced during the early 90s—, facilitated less costly disinflations. Finally, therecent inflation history (lagged inflation was much higher during the 90s than during the earlier decades) eroded the nominal rigidities, thus enabling inflation reductions with a lower output trade-oﬀ. Had these elements remained at levels comparable to those held during the 70s and 80s, the disinflation costs would have been similar across all three decades.
Figure A3 represent the average of household’s shares (ratio of transportation spending to total household expenditure that may be 0 for many households). Panels C1-C3 in Figure A3 present the ratio of country spending on transportation to total country expenditure. The B Panels are an average of ratios defined at the household level while the C Panels are the ratio between the absolute levels (in PPP adjusted US dollars) of national transport spending to national total expenditure (it is a ratio of averages). According to Panel B1, transportation spending accounts for 11.8% of total expenditure for the average LAC household. According to Panel C1, this figure is 14.1%. At this point it is worthwhile to note the difference between the average of ratios andthe ratio of averages. While they are along the same lines, they do not report the same information. The average of ratios gives the same weight to each household, while inthe ratio of national averages the rich account for a larger part of the denominator. If the rich spend a higher share of their budget on transportation than the poor, then the ratio of average transportation spending to average total spending will be higher than the average of household ratios. That is why the average LAC value suggested for spending on private transportation is larger in Panel C (10.8%) than in Panel B (6.7%), andthe opposite happens for spending on public means of transportation (3.2 vs. 4.9%, respectively).
Inthe third panel of table 4.4 unemployment rates between the poor andthe non-poor elderly are compared. In several countries non-poor elderly unemployment is significantly lower than that of the poor. It is important to be cautious about the interpretation of these results. Even though they point to the scarce labor opportunities for the old poor, the differences could be attributable to the interaction among other factors. For instance, many professionals and entrepreneurs that work into later life would receive higher income than the poverty line even if they stopped working (possibly they have saved enough money during adulthood to not need working to survive), but the nature of their work allows them to continue working. In other words, we should not conclude that these people are not poor because of working during old age. Continuing with the example mentioned above, professionals and entrepreneurs usually have more flexible jobs that are not physically demanding, and this could be the reason why they choose to keep on working. There is some evidence pointing out that elderly labor supply is more sensitive to this kind of non-pecuniary benefits (see for example Haider et al., 2001).
On average, poverty inthe Gallup Poll is 16 points higher than in national household surveys when using the US$2 line. This gap is naturally linked to the differences in incomes between the two sources discussed in section 3. More than being concerned about the specific poverty levels that arise from the Gallup Poll, we care about the rankings and comparisons across countries, and across population groups within countries. Figure 4.2 shows a positive significant correlation between poverty estimates using the Gallup survey and those computed at CEDLAS with national household survey microdata. The linear correlation coefficient is 0.62 for LAC, 0.71 for LatinAmerica, and 0.92 without the main income deviants identified inthe previous section. The poverty ranking that arises from the two alternative data sources turns out to be similar (see table 4.3). The Spearman rank correlation coefficient is 0.93. Chile, Argentina, Costa Rica and Uruguay are the countries where income poverty is less serious, while Bolivia, Nicaragua and El Salvador are located inthe other extreme. 15 Haiti ranks as the country with the highest income deprivation level inthe region. In summary, despite a much rougher approximation to per capita income, the picture of poverty inLatinAmericaandtheCaribbean viewed through the Gallup lens is not very different from the one obtained with household survey microdata. Poverty levels are highly correlated across both information sources andthe poverty rankings are roughly consistent. However, there seems to be problems either with the national representativity of the survey or with the income variable in a few countries that should be revised and corrected inthe next rounds of the Poll to increase the reliability and usefulness of the data.
Over and above the theoretical arguments for and against microfinance, much of the buzz about the industry is rooted in successful case studies. Although such small samples suffer from evident selection bias, it is nonetheless a useful starting point to identify some of their crucial singularities and draw lessons for the future. Ten case studies inthe field from different countries in LAC are examined: BancoEstado y Bandesarrollo (Chile), Compartamos (Mexico), BancoSol, Banco Los Andes y FIE (Bolivia), Crediamigo (Brazil), Banco Caja Social (Colombia), Credife (Ecuador) y Mibanco (Peru). The choice was not guided by any particular criterion except for the fact that they are all matured projects and list among the 100 largest MFIs inthe region –with a share of 26% of total clients and 34% of portfolio within this group as of 2006-, making them highly representative examples. Their very success has attracted the attention of a number of scholars –the list of studies the following analysis is based on is at the bottom of Table 15, where some major characteristics of each program are summarized.
The goal of this paper is to study the behavior of LAC economies during disinflations from low and moderate inflation peaks. It focusses on a set of four important macroeconomic variables: inflation, output growth, trade balance and exchange rates. The main objective is to set the stylized facts straight andpoint out cases where we might need new theories or further empirical work to explain particular outcomes. In this paper we do not attempt to go beyond this description of the main macroeconomic stylized facts and some preliminary speculations explaining some outcomes we find. A few of the interesting questions that arise from the stylized facts identified along the paper, are more deeply explored in a couple of companion papers (Hofstetter, 2007a and 2007b). Other relevant questions are left for future research.
Argentina. Sources: ILO (International Labor Organization) and INDEC (National Institute of Statistics and Census). Surveys cover Gran Buenos Aires and include people ages 10+. Definition: No job and searched actively during the reference week. Prior to 2003, unemployment rates are averages based on surveys in May and October. In 2003, several methodological changes were introduced: the frequency of surveys was increased to one per quarter; some types of female labor that had previously been ignored were included in employment; andthe definition of job search of the unemployed was broadened. At one pointin time, the INDEC reports results for both versions of the surveys: it reports the new series for the second quarter of 2003 andthe old series for May 2003. The ratio of the unemployment rates inthe new and old data is 1.14. Therefore, to correct for the break inthe series in 03, for 03-07, we average outcomes for the second and fourth quarter and divide the figure by 1.14.
Most countries either consolidated or introduced household surveys inthe 70s. The picture of income inequality from that decade on is hence clearer. Some international organizations (ECLAC, IADB andThe World Bank) shed additional light onthe issue by starting to generate periodical reports depicting the level, structure and trends of income inequalityinthe region. Table 4.1 shows the signs of theinequality changes in most LAC countries inthe last three decades. During the 70s inequality only significantly increased inthe Southern Cone (Argentina, Chile and Uruguay). In contrast several countries (Mexico, Bahamas, Panama, Colombia, Peru and Venezuela) experienced equalizing changes while the rest shows stable distributions. The 80s were a “lost decade” also in distributional terms. Most countries suffered a significant increase inthe level of income inequality. In around half of the countries inequality continued to increase inthe 90s, although in most of them at lower rates. As a result of the patterns described above most LAC countries have now more unequal income distributions than around 1970, and very likely also more unequal than at the end of the World War II. There are some exceptions, but for the majority of LAC countries the economic changes of the last half-century have been mainly unequalizing.
Inequality has dramatically increased in Argentina during the last three decades. 21 The Gini coefficient for the household per capita income distribution inthe Greater Buenos Aires area has increased from 34.5 in 1974 to 53.8 in 2002 (CEDLAS, 2003). Even if the observations for therecent crisis years are ignored, the increasing trend is noticeable. None of the other LAC countries has experienced such deep distributional changes as Argentina has. 22 Inequality also increased inthe neighbor Uruguay during the 90s, although the increase was smaller. Moreover, there were no sizeable distributional changes in Uruguay inthe 70s and 80s. As a consequence of these divergent patterns, the distributions of Argentina and Uruguay, once almost identical, now are significantly different. The other country inthe Southern Cone, Chile, has always had higher inequality indicators. The Chilean income distribution became more unequal during the 70s and 80s. That “storm” finished inthe 90s (Ferreira and Litchfield, 1999), although there are no signs of distributional recovery: inequality measures slightly increased during the last decade (see Contreras et al., 2001).
Inequalities continued inthe emergent democracies and electoral competition did not prevent elites from continuing to take advantage of uneven states. The exclusionary patterns inaugurated decades earlier also remained in place, states continued to be severely repressive toward the poor, and social policies were effective in co-opting poor constituencies without incremental taxation for the rich. Recentdevelopments portray a new era of durable yet unequal democracies. This is so because Latin American elites have learned that there are ways and devices to overcome political crises other than military coups, and that by avoiding ruptures they can safeguard their own rights to private property. However, this apparent equilibrium is not perfect, certainly not inthe eyes of the poor, but also not from the viewpoint of elites, because inequality gives rise to significant threats inthe form of criminal violence and political uncertainty. Yet sociological, more than economic, forces seem to prevent elites from cooperating to seek a more virtuous equilibrium.
Chronologically, the first studies to develop a set of measure expressions for accounting quality through the observance of accounting data were proposed by Healy (1985) and DeAngelo (1986), who estimate and interpret the adjustments for total accruals, andthe variation between the adjustments for accrual from one year to another, respectively, as a measure of the magnitude of discretion carried out by the management (discretionary accruals). Subsequently, Friedlan (1994) proposes a modification to the model that was initially suggested by DeAngelo (1986), estimating the variation of the accrual adjustments from one year to another, taking into consideration the operation activity in each period. At the beginning of the 1990s, Jones (1991) proposes a new model to estimate the discretion of those accrual adjustments that are considered normal (non-discretionary). This model is, without a doubt, the starting point for the measure and comparison of the level of earnings management between countries, with it being, until now, the model with the most use at an international level to analyze the changes in accounting quality, andon which subsequent authors have suggested modifications and new variables to potentialize the same (Dechow, Richardson and Tuna, 2003; Dechow, Sloan and Sweeney, 1995; Kothari, Leone and Wasley, 2005; Larcker and Richardson, 2004).
Vessuri, Hebe; Cruces, José Miguel; Ribeiro, Renato Janine, & Ramírez, José Luis. (2008). Overtaken by the future: Foreseeable changes in science and technology. In Ana Lúcia Gazzola & Axel Didriksson (Eds.). Trends in higher education inLatinAmericaandtheCaribbean, (pp. 51-81). Caracas, Venezuela: UNESCO, International Institute for Higher Education inLatinAmericaandtheCaribbean.