rafmacalaba/datause-encoder-data huggingface dataset:
weight these day‐to‐day calculations by teachers toward greater attendance and effort.

# **3.  Confronting teacher absence:  No recipe, but some ingredients**

Armed with all this new evidence on teacher attendance, performance, and incentives, what guidance can we give to education policymakers and administrators about how to reduce teacher absence? By now, it should be obvious that there is no simple answer. The best method for combating absence and encouraging better performance in any given school system will depend on the context – including the profiles of teachers, the general quality of governance in the country or region, the amount of support and monitoring by the education ministry, and the extent of community involvement in school management.

But if there is no single recipe, there are nonetheless several ingredients that policymakers should add to the mix. First and most important is simply to _devote policy attention_ to the issue of teacher effort. Often when student results are poor, the education establishment identifies the problem as a lack of skills: if only teachers were better trained, whether in subject matter or in pedagogy, student results would improve. But better skills have no effect on learning if the teacher does not make the effort to use them. Teacher absence – at least in the many cases when it is volitional – is just one glaring indicator of a lack of teacher effort, and a reminder that policymakers need to pay attention to both skills and effort.

It is hard to tackle a problem without measuring it, and so policymakers also need to _measure teacher absence_ accurately. Regular administrative records of attendance can provide some indication of where the problem areas are, but are unlikely to be a very good gauge of actual attendance levels. For example, head teachers may cover for absent teachers, or teachers may be marked as present if they attend during any part of the school day, so that absence at any point of time is understated. If there are any indications of an attendance problem, education policymakers should commission occasional independent surveys of schools to monitor absence directly. These surveys serve as both a check on the accuracy of administrative records, and perhaps also a mechanism for increasing their accuracy, if head teachers understand that a finding of systematic inaccuracies can have consequences.

Third, policymakers should have a _willingness to experiment_ with mechanisms for improving attendance and teacher effort more generally, and to monitor and evaluate those experiments carefully. As this review shows, the rigorous evidence on absenteeism and incentives is still too scarce to draw any general lessons about
# **1 Introduction**

Though the global trend is downward, the incidence of child labor remains very high in developing countries. For example, in 2000 the International Labour Organization estimated that 246 million, or almost 16 percent, of the world’s children between the ages of 5 and 17 were child laborers. By 2008 this had fallen to 215 million - a dramatic decline but still representative of 13.6 percent of the world’s children. In response, many governments have proposed or implemented policies designed to reduce the incidence either in their own countries, through labor laws that restrict or prohibit children working, or in other countries, through policies such as restricting the importation of goods that use children in some part of their production.

Though the goal of protecting children from harmful work is a recognized aim of the rights-based approach to child labor, and while the worst-forms of child labor are prima facie intolerable, there is much to be learned about what types of activities and their intensities constitute harmful work. In order to make e↵ective and responsible policy it is important to understand the consequences working as a child on participants and the interaction of child work with other activities such as schooling. Doing so will add clarity to questions such as how young is too young to work, how much is too much work at given ages and does working interfere with human capital accumulation. This study contributes to this understudying by demonstrating that working while attending school has a large negative and significant impact on the learning progress of schoolchildren in Brazil. This finding is also consistent with most theoretical treatments of child labor that stress the trade o↵between work and human capital accumulation.

While much is known about the incidence and the determinants of child labor, surprisingly little is known about the consequences of child work on participants. Important policy 2
We also collected data on more informal interactions between communicators and other farmers. We asked all farmers in the sample whether they have ever _discussed the new technology with a communicator_ , and columns 5-7 show that these informal discussions are significantly less likely to occur in female communicator assigned villages. The treatment evidently induces a lot of informal discussion across all treatment villages relative to control, but both male and female plot owners are 11 percentage points less likely to have those discussions with female communicators. Providing a small performance-based incentives significantly augments farmers’ interactions with communicators in both female and male communicator villages.

Columns 8-10 examine whether maize farmers report interacting with women less _in general,_ not necessarily about this new technology. 77% of all households in all villages report talking to the communicators, and this is equally true for shadow communicators in the control villages. Within treatment villages, the female communicators appear no less “visible” than male communicators in their respective networks.

In summary, farmers interact with females in general, and women do not suffer from differentially less visibility or greater introversion in this context. Farmers just appear to pay relatively less attention to messages about a new agricultural technology coming from a female communicator, both in informal and formal settings. As Table 5 shows, female communicators transmit the message effectively, despite getting less face time. All of this suggests that either women are not perceived to be good at farming, or that there are differentials in male and female “identity” in agricultural occupations (Akerlof and Kranton 2000), and the role of women in agriculture and training. Providing a small inducement intensifies interactions between farmers and communicators (Table 6), and reduces the gender gap on socially-mediated outcomes (Table 5). Hence, one possibility is that these gaps in perception may be driving female-assigned communicators’ relative lack of success in convincing others, rather than a more generalized gender communication gap: by increasing exposure to women communicators, incentives improve the accuracy of farmers’ perceptions. We explore these ideas using two further sources of data and

> leads to a 0.37% increase in attendance. For women in the incentive treatment, a 1% increase in training causes attendance to go up by 0.22%. Assuming constant attendance rates across trainings (linear training-attendance relationship), incentivized women would need to provide: (0.13-0.06)/0.22 = 0.32, 32pp, more trainings to close the gap.

21
This study deepens our understanding of poverty and the spatial welfare disparities in Brazil through an investigation of the role of demographics, human capital, occupation, and structural geographical differences in returns. This is done by employing a recent survey in Brazil, the 2002-2003 Household Budget Survey ( _Pesquisa de Orcamentos Familiares_ or _POF_ ) that allows us to use consumption, rather than income, as a measure of household welfare. For a variety of reasons, consumption is considered by economists to provide a more accurate measure of household welfare. There are both conceptual and pragmatic reasons why consumption expenditures from household surveys might be preferred, for the purpose of poverty and inequality analysis, over an indicator such as household income. First, consumption tends to fluctuate less in the short term than income, which can be affected by the seasonality of employment. Consumption expenditures reflect not only what a household is able to command based on its current income, but also whether that household can access credit markets or household savings at times when current incomes are low or even negative (due perhaps to seasonal variation or a harvest failure). In this way, consumption is thought to provide a better picture of a household’s longer run standard of living than a measure of current income. Second, income measures may not accurately capture in-kind, seasonal, or informal income. While poor households are probably purchasing and consuming only a relatively narrow range of goods and services, their total income may derive from multiple different activities with strong seasonal variation and with associated costs that are not always easily assigned. Third, income surveys are susceptible to under-reporting as respondents may perceive incentives to do so.

While it is clear that regional disparities in income and welfare have existed for some time in Brazil, there is not a clear consensus on whether it is mainly due to returns to characteristics or the distribution of characteristics. Our paper explores these issues in more depth. Aside from the fact that we use consumption instead of income as a measure of welfare, our approach is different from previous studies in that we (i) disaggregate urban areas into metropolitan and non-metropolitan areas within regions to obtain a more refined picture, and (ii) analyze both differences in mean welfare and differences between distributions, using Oaxaca-Blinder and quantile regression decomposition techniques.

This paper is organized as follows. Section 2 presents the methodology for the decomposition of mean welfare differentials and the quantile regression decomposition along with a brief summary of the 2002-03 Pesquisa de Orcamentos Familiares household budget survey and the variables used in the analysis. Section 3 presents the results of these decompositions, and Section 4 concludes and discusses some of the implications for policy.

3
# **Table 2: Estimation results: Group trade viability**

||Dependent variable:<br>Income increase from group trade<br>||
|---|---|---|
||[iii]|[iv]|
|Constant|-2.136<br>(0.411)|-1.831<br>(0.364)|
|COVI|0.058<br>(0.353)||
|COST|0.402<br>(0.294)||
|FREQ|0.677 **|0.687 **|
||(0.339)|(0.311)|
|VOLU|-0.055<br>(0.333)||
|NEWC|0.593 *|0.621 *|
||(0.360)|(0.366)|
|PRIC|0.577 **<br>(0.291)|0.633 **<br>(0.285)|
|A|0.752 **|0.765 **|
|QUL|(0.337)|(0.313)|
|FREE|0.748 **<br>(0.330)|0.860 ***<br>(0.320)|
|Observations|142|142|

_Note_ : Estimation results for location dummies omitted. The superscripts *, **, and *** denote significance, respectively, at the 90%, 95% and 99% level.

One surprising finding from the econometric analysis is that increased quality requirements of group trade (QUAL) are significantly related to higher trader incomes, while it would be expected that meeting higher quality standards is costly and would, hence, reduce income. However, this outcome might be the result of multicollinearity between the QUAL and the PRIC variables or between the QUAL and the NEWC variables. The respective correlation 22
Table **1:** ActivitY Gross Doestic Product<sup>and Income (total and per capita), by Type of Acanomic</sup> (millions of constant<sup>1970Kwacha)</sup>

||1970|1974|1975|1980|1983|1984|1985|
|---|---|---|---|---|---|---|---|
|Agriculture|136.1|150.5|157.0<br>|166.4<br>|172.3<br>|181.9<br>|198.7<br>|
|Mining|460.3|474.3|427.9<br>|398.7<br>|446.5<br>|402.8<br>|381.4<br>|
|Industry|235.0|339.4|34r.0|326.8<br>|346.7<br>|349.7<br>|366.4<br>|
|<br>Other|437.1|509.7|508.2|525.9|506.9|500.8|513.0|
|GDP (market prices)|1268.5|1473.9|1438.1<br>|1417.8<br>|1472.4<br>|1435.2<br>|1459.5<br>|
|Plus T/T adjustment|(a)<sup>-</sup>|-104.7<br>|-387.2<br>|-367.3<br>|-427.1<br>|-350.0<br>|-360.0<br>|
|GDY(b)|1268.5|1369.2|1049.9|1050.5|1045.3|1085.2|1099.5|
|Population (000's)|4159|4700<br>|4846<br>|5647<br>|6285<br>|6500<br>|6725<br>|
|GDP per capita (K)|305|314<br>|297<br>|251<br>|200<br>|191<br>|190<br>|
|GDY per capita (K)|305|291|217|186|166|167|163|

**Notes:** aRough estimates for 1984<sup>and1985</sup> bGross domestic income:<sup>this is equal to GDP plus adjustment for variations in</sup> the terms of trade.

Tables 2.02 and 2.04<sup>and</sup> Sources: Calculated<sup>on the basis of data in World Bank1984,</sup> World Bank, 1986, Tables 2.02 and 2.04.
repeated to show how much this distortion varies by firms’ sales levels. For firms with median sales, the total effect of bribery on sales growth is -23.9 percent<sup>7</sup> , which is similar to the effect from column (1). However, the distortion is larger for firms that generate low sales. We compared the effects of bribery on firms that are in the 25<sup>th</sup> and 75<sup>th</sup> percentiles of the 2007 sales distribution. Among firms who were bribed, firms with sales in the 25<sup>th</sup> percentile saw growth rates that were 43 percentage points lower than the rate for firms that are in the 75<sup>th</sup> percentile<sup>8</sup> .

Bribery is estimated to have a larger negative effect on the growth of small firms; a higher opportunity cost of paying for bribes is one reason for this larger effect. Small firms can find it more difficult to afford bribe requests and thus their operations are slowed down and productive investment or hiring decisions are retarded. Clarke (2011) finds that most bribes are petty. Therefore small firms may feel that bribes are more costly than large firms. On the other hand, for large firm who want to expand, paying bribes may serve to speed the attainment of permits which result in greater sales that compensate the bribe payments. The results imply visibility for large firms does not create distortions severe as for small firms.

Age is another explanation for why small firms are more severely affected by bribery. Small firms are likely to be younger. Young firms have less experience dealing with public officials, making them more vulnerable to bribery. In column (3), we interact bribery with a dummy for firms greater than 10 years old. In this interaction, firms who are older have an average 21 percent higher sales growth than young firms. In column (4), bribery is interacted with sales and age. In this specification, the coefficient on the age and bribery interaction term is positive but not significant.

Selection bias due to sample composition is a concern since bribery statistics is restricted to firms that conducted at least one business transaction. However, the direction of the bias that could emerge from the selection is not clear. Including only firms doing business transactions does not necessarily negatively bias the role of bribery since these firms may be better suited to deal with bribe seekers. Table 8 shows results including firms that did not conduct any business transactions. We assume these firms did not experience bribery. The sample increases by over 2,000 firms and there are 13 additional bribery clusters. IV estimates are shown with as

> 7 Estimated using Eq (4).

> 8 This effect is calculated using Equation (4). The total effect is calculated at the 25th and 75th percentiles of sales and then a difference is taken.

18
statistically significant correlation between growth and infrastructure. Second, education is highly correlated with all the infrastructure subsectors except roads<sup>14</sup> . The final observation is that the various infrastructure subsectors are highly correlated among themselves.

Overall, these results suggest that there is a strong likelihood of multicollinearity, which if confirmed, needs to be addressed as part of our econometric work. In particular, multicollinearity has the effect of significantly increasing standard errors resulting in low t-statistics. In other words, including all independent variables in the model at the same time could result in many of them not being significant, even though their respective relationships with the dependent variable may be significant when considered individually. To avoid this problem, we regress the infrastructure variables one by one in model (5)<sup>15</sup> . We may then compare model (5)’s goodness of fit with that of the benchmark model (4).

**Table 2: Sample pair-wise correlations between dependent and explanatory variables (p-values in parenthesis)**

||**GDPpc **|**Education**|**Telecom**|**Electricity**|**Roads**|**Water**|**Sanitation**|
|---|---|---|---|---|---|---|---|
|**GDPpc**|1|0.63|0.93|0.98|0.58|0.51|0.60|
|||(.0000)|(.0000)|(.0000)|(.0000)|(.0056)|(.0015)|
|**Education**||1|0.68|0.80|0.23|0.61|0.67|
||||(.0000)|(.0000)|(.1331)|(.0006)|(.0002)|
|**Telecoms**|||1|0.93|0.46|0.56|0.67|
|||||(.0000)|(.0013)|(.0019)|(.0002)|
|**Electricity**||||1|0.59|0.64|0.73|
||||||(.0002)|(.0002)|(.0000)|
|**Roads**|||||1|0.36|0.45|
|||||||(.0599)|(.0240)|
|**Water**||||||1|0.81<br>(.0000)|
|**Sanitation**|||||||1|

We formally assess the presence of multicollinearity by examining tolerance and its inverse, variance inflation factors (VIFs), which give a sense of the magnitude by which the standard error of an independent variable is “inflated” when it is predicted by other independent variables in the

> 14 One would expect a superior endowment of roads to be associated with ease of access to education by facilitating an individual’s travel to school. The statistically insignificant pairwise correlation coefficient between education and roads (correlation coefficient of 0.23, with a _p_ -value of 0.1331) is therefore somewhat counter-intuitive. A possible reason for this could lie in the definition of our roads infrastructure variable as kilometers per capita of paved roads. In other words, roads that are not paved are not accounted for in the analysis, which in turn could lower the correlation between education and roads infrastructure. 15 An alternative is to rely on a synthetic index of the various infrastructure services as done by Calderon and Serven (2004). This is a useful solution when the focus is on the aggregate level of infrastructure. Here the emphasis in on the relative relevance of the various infrastructure sub-sectors and hence we discarded the synthetic index approach.

14
# I. Introduction

Many studies present the status of youth in Brazil. They tell us about secondary education attainment rates (World Bank, 2000; Soares, Carvalho, Kipnis, 2003; _Waiselfisz et al., 2004;_ Rodriguez and Herran, 2000), youth violence (Waiselfisz, 2004; Abramovay et. al., 2003; Abramovay and Rua, 2002; Human Rights Watch, 2003), youth unemployment (Bonelli, Reis, and Veiga 2004), youth participation (Weiss, 2004; Instituto Cidadania, 2004), and a myriad of other factors that we use to determine how well youth are surviving their transition from childhood to adulthood.<sup>1</sup> While these various indicators are useful to understand the status of youth for a single indicator or, at best, in a single sector, they cannot be summed to give us a measure of the overall wellbeing of Brazilian youth.<sup>2</sup> Instead, we are limited to discussing how well youth fare relative to adults or to other youth by comparing a list of single indicators. Some show youth are doing well while others show that they are not doing so well, making it difficult to assess the overall status across the many dimensions of youth well-being. Thus, it would be useful to have a single indicator that summarizes the multi-dimensionality of youth well-being in order to allow for comparisons across regions of Brazil as well as track progress over time as the Government, the non-governmental sector, communities, families, and youth work to improve the situation of Brazilian youth.<sup>3</sup> Such an index was created for the United States by researchers at Duke University in the United States. The Child Well-Being Index (CWI) uses 28 key indicators in the areas of health, relationships, material goods, behavior, labor market, community, and emotional/spiritual well-being indicators to create a single index for children and youth in

> 1 The period “youth” is difficult to define due to the many different criteria that can be used to specify the period.  Some disciplines base it on biological change, such as the period when the body changes from being a child to being an adult.  Others base it on economic transitions – being a household dependent to earning one’s own keep – or social transitions – being a household dependent to being a household head. This leads to very different age periods to capture “youth”, as well demonstrated by the wide range of ages used for youth policies or in research.  The common thread among all these definitions is that the youth period is one of transition (World Bank, 2003; Lloyd 2004).

> 2 Well-being has been defined as quality of life, and refers to objective and subjective aspects of the human existence (Cummins 1996).  Objective aspects concerns facts or behavior, which can be operationally observed and measured.  Subjective aspects are associated with perceptions of facts and behaviors of daily life.

> 3 The international community is increasingly recognizing the need indices to measure welfare.  The United Nations has created a Human Development Index, using four simple indicators.  More recently, the Commonwealth Youth Programme-Caribbean Office has been developing a youth index for its member countries.

2