rafmacalaba/datause-encoder-data huggingface dataset:
14 the Gini coefficient. Figure 4.2 shows the Lorenz curves for Rural, Urban excluding Lagos, Lagos Urban and Nigeria for 1985/86 and 1992/93. The figure shows that Lagos urban distribution has actually improved. The worsening in national income distribution is thus caused mainly by the worsening rural distribution. The Gini coefficients for rural, other urban and Lagos

**Table 4.2: Distribution of Per Capita Food Expenditure by Population Deciles (Constant 1985/86 Naira Per Annum**

|Decile|1985/86**PCE**|**1992/93 PCE**|**Percentage Change**|
|---|---|---|---|
|First five percent|99.20|53.85|-45.7|
|Second five percent|145.89|110.41|-24.3|
|Second Decile|186.50|163.89|-12.1|
|Third Decile|224.17|236.95|5.7|
|Fourth Decile|260.98|309.41|18.5|
|Fifth Decile|295.78|374.01|26.45|
|Sixth Decile|353.27|461.17|30.54|
|Seventh Decile|408.63|567.06|38.77|
|Eighth Decile|482.14|698.77|44.93|
|Ninth Decile|595.65|947.83|59.13|
|Tenth Decile|1047.96|1472.48|40.51|
|Nigeria|391.63|554.92|41.7|

Source: NCS, 1985/86 and 1992/93 urban are shown in Table 4.3. In Lagos urban, the Gini coefficient increased from 0.340 to 0.399. For other urban excluding Lagos, the value increased from 0.366 to 0.441 and in the rural areas, the Gini increased from 0.391 to 0.456 showing a worsening of income distribution. This worsening is reflected in the national Gini coefficient which increased from 0.387 to 0.450.

**Fig 4.1: Evolution of Household Expenditures by Expenditure deciles**

<!-- Start of picture text --> 35 __.. .w<br>Z 30<br>25<br>-s20<br>20<br>15<br>00<br>0 5-<br>0.<br>1 2 3 4 5 6 7 8 9 10<br>Population deciles<br>*1985 01992<br><!-- End of picture text -->
Tuberculosis and Malaria. Nevertheless, political commitment remains inadequate and the epidemic is still a major challenge.

Contraceptive use is low in Haiti. Considering the proportion of the population infected by HIV/AIDS, unprotected sexual activity is a major risk factor. Nevertheless, according to the HCVH survey contraceptive use is not as alarmingly low as suggested by some papers. The results indicate that more than 80 percent of youth who are aware of AIDS have taken precautions against it (Table 5.4). Of course this result does not show the proportion of those who have ever had one unprotected sexual encounter, which is likely to be very high. Teen pregnancy is also connected to unprotected sex.

|Table 5.4: Perc<br>taken Precautio<br>|entage of those a<br>nsit,2001(perce<br>|ware of A<br>nt)<br>|IDS who ha<br>|ve ever<br>|
|---|---|---|---|---|
|Agegroup|Metropolitan|Urban|Rural|Haiti|
|15–19 years|88.1|78.9|71.1|76.6|
|20–24years|93.4|89.4|78.3|85.4|
|15–24years|90.9|83.0|74.0|80.5|
|25–34 years|92.7|84.6|75.8|82.8|
|35–44years|94.3|89.1|71.3|80.6|

_Source:_ Own calculations based on ECVH 2001.

# **Teenage pregnancy**

Contributing to Haiti’s high fertility rate are teenage mothers who deliver around 8 percent of all births (World Bank 2002). A reason for this high number is that the onset of sexual initiation is earliest in the Caribbean region after that of Africa inside marriage. The LAC average for births to young females was 52 per 1000 females aged 15–19 in 2002. For Haitian teenagers (aged 15–19), the number of births per 1000 females in 2000 was 68.3, 31 percent above the regional average (World Bank 2005a, 2003). Teen pregnancy, as result of early unprotected sexual activity, is generally associated with negative outcomes such as lower education, maternal mortality, limited job opportunities, etc. The regression results in Table 4.5 support this hypothesis because marriage increases the probability of dropping out of school and teenage pregnancy is highly correlated with marriage in Haiti. Causality is likely to not only go from teen pregnancy to low education, but low-educated teens are also predisposed to have children early (Table 5.5). In this regard, Table 5.6 reveals that sexual initiation is closely connected to education. Thus, keeping youth in school would likely reduce HIV/AIDS and teen pregnancies dramatically. Pregnancies and HIV/AIDS disproportionately affect youth from low-income families because they lack information and family counseling.

26
# **Introduction**

By its nature corruption is hard to measure. Yet, over the last two decades a series of data sets and indices have emerged that attempt to capture the phenomenon of corruption. Work with indicators on corruption typically confronts two basic issues. First, it is not always clear what types of corruption are measured. Second, it is not clear whether and to what degree perception measures of corruption reflect biases of respondents who either try to hide the truth or make biased guesses. The practical result is that perception measures of corruption can raise awareness but are not actually helping design policy. As argued by Kenny (2006) it could be that new more fine-grained firm-level indicators of corruption might generate more useful findings.

In this paper we explore whether the juxtaposition of several data sets can help disentangle some of the issues. We start by considering potential bias in aggregate perception indicators of corruption by confronting them with indicators generated from firm surveys. We then confront the firm surveys of corruption with data on regulatory obstacles and find plausible stories both about the link between regulation and corruption and about the effectiveness of removing obstacles through corruption, including obstacles to obtaining infrastructure service.

Turning to infrastructure, we find just a few data and case studies. The firm-level indicators suggest that infrastructure sectors are plagued by corruption, but not more than other sectors of the economy. If there is a sector that is particularly plagued by corruption it is construction, including for infrastructure projects. But there are important stylized facts about price levels and access to infrastructure services that shed light on mechanisms of corruption and are relevant for policy-making. We argue that the first line of defense for policymakers against the major illeffects of corruption in infrastructure is the restriction of government transfers to firms and reliance on consumer prices that effectively cover full cost. Corrupt elites may have an interest in going along with such policy change, because it might make them better off too.

We conclude with some basic thoughts about a sensible attitude towards measurement of corruption and policymaking.

# **Noise and Bias in Aggregate Indicators?**

Aggregate indicators of corruption tend to be based on perceptions of “experts” in firms, government and other institutions who provide views about the degree of corruption in different countries. The best know such indicators are Transparency International‟s (TI) Corruption Perception Index (CPI) and the World Governance Indicators (WGI) of the World Bank.

There are two potential issues with such indicators: noise (that the indicators only weakly reflect underlying levels of corruption) and bias (that the reflection is distorted by the influence of factors other than corruption). Given the illicit nature of the activity, it is unsurprising that even 2
certification process would receive a "professional allowance" (or "certification allowance") equal to 100% of their base pay (World Bank 2010, Chang et al. 2014).<sup>7</sup> The certification process was initially meant to include a high-standards external assessment of teacher subject knowledge and pedagogical practice, with an extensive skill-upgrading component for teachers who did not meet these standards (featuring up to a year of additional training and tests). However, teachers' associations opposed the high-standards certification exams. Thus, by the time the final law and regulations were negotiated through the political and policymaking process, the quality-improvement stipulations had been highly diluted. They were replaced with a much weaker certification requirement that simply required teachers to submit a portfolio of their teaching materials and achievements. Even for those who did not pass the portfolio evaluation, just two weeks of additional training were required to attain certification. Thus, in practice, the certification process yielded a doubling of base pay with only a modest hurdle to be surmounted.<sup>8</sup> Pre-reform teacher salaries in Indonesia were lower than teacher salary benchmarks in other Southeast Asian countries (which was part of the justification for the policy), but teachers were reasonably well paid even before the reform. Using representative household survey data from the 2012 Indonesian labor force survey (Sakernas, August 2012), we estimate that pre-reform teacher pay was at the 50<sup>th</sup> percentile of the college-graduate salary distribution. Civil-service teachers also enjoyed more generous benefits than equivalent workers in the private sector, and had high job security. Overall, teacher jobs were attractive even before the reforms, and quit rates were very low.

The reform led to a substantial increase in teacher salaries, moving teacher compensation from around the 50<sup>th</sup> percentile of the college-graduate salary distribution to the 90<sup>th</sup> percentile. This large salary increase was not conditional on teachers' subsequent effort or effectiveness, but instead depended only on a one-time determination that the teacher met some certification criteria. Hence, for all practical purposes, the policy can be considered as having resulted in an unconditional salary increase for eligible incumbent teachers. To the extent that undergoing the certification process

> 7 Note that the professional allowance was 100% of _base_ pay, rather than of total pre-certification pay. Teachers often receive other allowances based on the location of their job posting and taking on additional tasks, and so the allowance increased total pay by 75% on average and by 65% for teachers who were eligible for certification (see Table IV). 8 Very few teachers entering the certification process failed it. Further, even those who failed the first attempt were all certified after a two-week training program, which mainly focused on helping teachers prepare the portfolios that would need to be submitted with the certification process (World Bank 2010).

8
schooling outcomes.For grades 5 and 7, the data reports whether a student passed the exam and if they passed with more than 60 percent marks.

In Table 5, we show that the passing rate in government schools falls by 1.8 percent for grade 7 students (Columns (ii) and (iv)) with the coefficient significant at the 10 percent significance level, whereas there is no effect on grade 5 (Columns (i) and (iii)). The program effects passing with more than 60 percent marks much more significantly. Both private and government school 7th graders do worse on this measure. This effect is almost twice as large for government schools than the private schools and the difference is statistically significant (Columns (vi) and (viii)).<sup>24</sup>

# **5.4 Mechanisms**

In order to shed light on the mechanism, we examine the employment outcomes of children in our DID framework. The data on child labor come from the National Sample Survey Organization (NSSO) employment and unemployment surveys (rounds 2004-05 to 2008-09) and we employ earlier rounds to carry out a falsification check. The data asks individuals to identify their principal occupation in the last month. We examine two outcome variables: Child reports working (employed, self employed or unpaid family labor) and child reports doing chores (housework or free collection of goods). We restrict our sample to 206,321 non-disabled children aged between 5 and 15 from the two rounds and look at their reported principal activities. The indicator for ‘working’ equals to 1 if a child’s reported principal activity is working in the household enterprises (paid or unpaid), as wage employee, or in other types of work, and 0 otherwise. The indicator for doing chores is 1 if a child’s reported principal activity is attending to domestic duty or doing any other housework, and 0 otherwise.<sup>25</sup>

> 24The program does not affect the students’ appearing at exams conditional on enrollment, as reported in Appendix Table S6.

> 25Other alternatives for principal activity include attending educational institution, seeking jobs, rentiers, pensioners, remittance recipients, and others.

17
39 Table 3. Bank Profitability

|||GEM|OC|NEM|PBT|Number of|
|---|---|---|---|---|---|---|
|||A|s % of T|otal Ass|ets|Banks|
|Turkey|1983|_5.0_|3.6|1.3|1.0|39|
||1984|5.7|3.3|2.4|1.9|41|
||1985|3.8|2.9|0.9|0.6|44|
||1986|5.4|2.8|2.6|1.9|49|
||1987|7.3|2.8|4.5|3.0|50|
||1988|8.5|3.3|5.2|3.5|52|
||1989|7.4|3.8|3.6|2.4|53|
||1990|12.3|_5.1_|7.2|3.6|56|
|Spain|1990|_5.1_|3.0|2.1|_1.5_|160|
|Australia|1990|2.5|1.6|0.9|0.4|1165|
|Italy|1990|4.1|2.7|1.4|0.8|188|
|Holland|1990|1.2|0.4|0.8|0.2|173|
|Belgium|1990|1.7|1.2|0.5|0.3|91|
|Denmark|1990|2.9|2.0|0.9|-0.2|119|
|Germany|1990|3.1|2.0|1.1|0.6|281|
|OECD|1990|3.2|2.0|1.2|0.7|--|

Sources: **Bank** Profitability (OECD, 1992), the Banks Association of Turkey. Variables: GEM: Gross Economic Margin = Interest Received - Interest Paid + Other Income (net). OC: Operating Costs.

NEM: Net Economnic Margin = GEM - OC.

**PBT:** Profit Before Taxes = NEM - Other Expenses(net) equal to ROA. Total Assets: Arithmetic averages of the end of year values.
glow (Andreoni, 1990) as the willingness-to-pay for another farmer whom the participant does not know and who produces no spillovers from the participant’s standpoint. I assume that spillovers related to either contagion or knowledge externalities decay to zero beyond a certain plot or home distance threshold respectively. At the 95th percentile in plot and home distance (for a home distance larger than 1.7 miles and an average plot distance larger than 35 walking minutes), the median willingness-to-pay for a farmer whom the participant does not know is zero, pointing to a lack of warm-glow giving behavior; the same holds true at the 90th percentile.

# **6.3 The Social Benefit of the Training Is Large and Heterogeneous**

The willingness-to-pay data allow me to evaluate three policy-relevant objects: the value of the per capita externality generated by training one farmer; the social benefit of the training; and how different targeting criteria for the training generate different levels of social benefit. In what follows I will show how to calculate the per capita externality and social benefit of training one farmer, and then compare two targeting criteria. The first allocates the training according to a _price criterion_ : the farmer with the highest willingness-to-pay gets the training. The second allocates the training according to an _externality criterion_ : the farmer producing the highest per capita externality gets the training. While it is trivial to foresee that the externality criterion will produce a higher social value than the price criterion, farmers’ valuations allow us to fine tune this intuition by quantifying the change in social benefit when externalities are accounted for in targeting decisions.

I use farmers’ willingness-to-pay for others to calculate the externality generated by a trained farmer _t_ in village _v_ : Where _Nj_ is the number of sampled farmers _j_ = _t_ in village _v_ .

Column (2) in Table 2 shows the mean, standard deviation, median, maximum and minimum value of the per capita externality, calculated using equation (11); it ranges from 500 to 24,250 UGX and has a median value of 6,750 UGX.

The social value of _t_ ’s training is then the _Per Capita Externality_ in (11) scaled up by the relevant population size (excluding _t_ ) plus _t_ ’s willingness-to-pay for self. Indicating the relevant population in _t_ ’s village as _Nv_ yields 21
participation by recipient countries, pairing each of the 34 donors in our sample with each of the 76 recipients would imply a total of 2584 potential aid relationships. This figure represents only 50.3% (row 7 of the table) of the 5134 total potential aid relationships implied by pairing each of the 34 donors with each of the 151 eligible DAC recipients. Of the 2584 potential pairings in the survey, a positive aid flow is recorded in only 998 cases, or 38.6% (row 8). These 998 active aid relationships account for only 19.4% (row 9) of the 5134 total pairings of the 34 donors with each of the 151 DAC recipients. For the earlier survey years of 2005 and 2007 these percentages were even lower. Pooling the three survey years (last column of Table A3), nearly 64% of potential observations are censored by recipient nonparticipation in the PDMS (although the majority of these would be censored anyway due to zero aid flows), and more than 60% of the remaining pairs are censored by donors‟ provision of zero aid.

There is some evidence suggesting that the censored observations may differ systematically from the uncensored observations, potentially creating a selection bias in our use of country systems tests. Use of country systems averages 38.3% for 2010 among all observations corresponding to the initial (2005) set of PDMS recipient participants. The average for 2010 is only 28.5% for recipients that joined the survey in 2007, and is only 26.5% for recipients that did not participate until 2010. Very few recipients dropped out, but many were added over the three rounds, and use of country systems is markedly higher in the earlier volunteers. Uncensored observations differ significantly from censored observations on all of the recipient-level regressors in the analysis: they are larger, poorer, more aid-dependent, grow more slowly, and score less well on Control of Corruption and Civil Liberties.

Correcting for potential selection bias using the Heckman approach requires identifying exogenous variables that affect selection by recipients into the PDMS, and by donors and 19
when combined with the second and first approach. As we will later describe in more detail, we opted for combining the three approaches in the current study. Yet, before we present our empirical results, the next section tries to summarize what is known about child mortality determinants, in particular with regard to those related to other MDGs in the Sub-Saharan context.

# _Evidence on Child Mortality Determinants in SSA_

The literature on the determinants of child mortality, whether infant or under five, is relatively abundant. A number of single- and multiple country regression-based studies have been carried out in the last twenty years, most of them taking advantage of DHS data. It is indeed difficult to collect data suitable for mortality/survival analysis and DHS surveys are among the few surveys that permit this type of analysis since they collect information on the reproductive history of women. More specifically, they gather data on dates and survival status of all births, information that is obviously crucial for child and infant mortality analysis. Furthermore, being in large part conceptualized centrally by Macro International, the DHS surveys use a framework that allows the estimation of country-level models relying on the same set of variables. Given the characteristic of the dependent variable, hazard models are typically used to estimate mortality/survival models. The selection of variables is very often based on Mosley and Chen’s conceptual framework presented in the previous section. It usually includes child level (like gender and birth order), mother level (like birth spacing, age at birth, education, ethnicity) and household level information (like wealth and access to water). In the following, we review a number of empirical infant and child mortality studies that have been conducted in the SSA context. In addition, our literature review includes Charmarbagwala et al.’s (2004) world-wide meta-analysis of mortality, health and nutrition studies that only contains a couple of studies carried out on SSA datasets. This compilation of the results from 38 papers reveals, not surprisingly, a certain degree of consistency in the determinants of child mortality. However, as we will see, there is also quite some heterogeneity in the results – even within the SSA – suggesting that policies need to be adapted to the country-specific context.

In the reviewed micro-econometric studies, child characteristics typically show the expected influence on mortality. Boys are often found to be significantly more likely to die than girls and the same holds for first born children (see for example Lavy et al., 1996; Ssewanyana and Younger, 2007). In terms of maternal proximate determinants, the studies in general confirm the important influence in particular of mother’s age and birth intervals (for 8
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