rafmacalaba/datause-encoder-data huggingface dataset:
China is much more decentralized than OECD countries and middle-income countries, particularly on the spending side. More than half of all expenditure takes place at the _sub-provincial_ level. In part, the sheer size of the country explains this degree of decentralization, but the structure of government and some unusual expenditure assignments also give rise to this pattern of spending. Functions such as social security, justice, and even the production of national statistics are largely decentralized in China, whereas they are central functions in most other countries.

Fiscal disparities among subnational governments are larger in China than in most OECD countries. These disparities have emerged alongside a growing disparity in economic strength among the provinces. From 1990 to 2003, the ratio of per capita GDP of the richest to poorest province grew from 7.3 to 13. In China, the richest province has more than 8 times the per capita public spending than the poorest province. In the US, the poorest state has about 65 percent of the revenues of the average state, and in Germany, any state falling below 95 percent of the average level gets subsidized through the “ _Finanzausgleich_ ” (and any receiving more than 110 percent gets taxed). In Brazil, the richest state has 2.3 times the revenues per capita of the poorest state (Dollar and Hofman, forthcoming).

Inequalities in spending are even larger at the sub-provincial level. The richest county, the level that is most important for service delivery, has about 48 times the level of per capita spending of the poorest county (Dollar and Hofman, forthcoming). These disparities in aggregate spending levels also show up in functional categories such as health and education where variation among counties and among provinces is large (Figure 4).

These differences in public spending translate into differences in social outcomes. Up through 1990, there were only modest differences across provinces in infant survival rate, but by 2000 there had emerged a very sharp difference, closely related to the province’s per capita GDP. So too with the high-school enrollment rate: there used to be small differences across provinces. By 2003, high-school enrollment was nearing 100% in the wealthier provinces while still less than 40% in poor provinces (Figure 5).

There is some redistribution within China’s fiscal system, but not enough. Poor areas have very little tax collection and hence cannot fund decent basic education and 11
**Financial (Dis-)Information: Evidence from an Audit Study in Mexico**<sup>∗</sup>

Xavier Giné Development Economics Research Group, World Bank Cristina Martínez Cuellar The University of Chicago Rafael Keenan Mazer CGAP, World Bank Keywords: financial information, audit study. JEL codes: D14, D91, G02, G21, O16.

> ∗ Giné: xgine@worldbank.org. Martínez: cristinam@uchicago.edu. Mazer: rmazer@worldbank.org. We thank CONDUSEF and in particular Jesus Chavez Ugalde and Maria Fernanda Saldivar Cortes for their constant encouragement and support. We also thank the team at IPSOS for data collection. We are grateful to Sumit Agarwal, Santosh Anagol, Matthew Bird, Eva Gutiérrez, María Soledad Martínez-Peria, David Medine, José Luís Negrín Claudia Ruiz, Enrique Seira and Bilal Zia for helpful comments and to the Russian Trust Fund and the Knowledge for Change Program (KCP) for funding.
WPS 3241

# **Environmental Determinants of Child Mortality in Rural China:**

# **_A Competing Risks Approach_**

Hanan Jacoby* & Limin Wang**<sup>1</sup>

# **Abstract**

We use a competing risk model to analyze environmental determinants of child mortality using the 1992 China National Health Survey, which collects information on cause of death. Our primary question is whether taking into account of cause of death using a competing risk model, compared with a simple model of _all-cause_ mortality, affects conclusions about the effectiveness of policy interventions. There are two potential analytical advantages in using cause of death information: (1) obtaining more accurate estimates and (2) validating causal relationships. Although, we do not find significant differences between estimates obtained from the competing risk model and those from simpler hazard models, we do find evidence supporting the causal interpretations of the effect of access to safe water on child mortality. Our analysis also suggests that a respondent-based health survey can be used to collect relatively reliable information on cause of death. Modifying future demographic and health survey (DHS) instruments to collect cause of death information inexpensively may be worthwhile for enhancing the analytical strength of the DHS.

# World Bank Policy Research Working Paper 3241, March 2004

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> * DECRG, World Bank, ** ENV, World Bank.

> We gratefully acknowledge the financial support from Swedish International Development Agency (SIDA), TF024884. We thank Kirk Hamilton, the task manager of this study, for suggestions and comments, Jostein Nygard and Adam Wagstaff for useful discussions. Alexandra Sears provided excellent assistance.
**| olyPlanning, nd Research |**

**Educaionand Employment**

From the mid-1950s to the 1960s, the govemment of Peru undertook a major expansion of public education, increasing the number of schools, requiring primary schools that offered an incomplete cycle to add grades, and increasing school inputs (principally teachers and textbooks).

The authors examine the effects of Peru's educational policies, and the effects of family background and community characteristics on the schooling levels of a sample of adults. Data on males and females were analyzed separately by birth cohort, using a sample of 5,644 females and 5,241 males aged 20 to 59.

# The authors found that:

inedicator that the link between socioeconomic ba,-kground and access to schools had weakencd.

* The relative effect of parents' education differed for sons' and daughters' schooling. Fathers' education had tv -ce as great an effect on sons' schooling as mothers' education; for daughters, both parents' education had equivalent, strong positive effects.

* Primary schools expanded rapidly even in rural areas, so rural residence did not adversely affect the primary schooling of males. Urban or rural residence did matter greatly for females, however, suggesting less desire by rural parents to invest in the schooling of their daughters.

* The govemment's policy to expand the schools played a major role in raising education levels and narrowing the gap between rural and urban residents, and males and females.

* The impact of parents' years of schooling and occupations on the educational levels of their children lessened over time as the supply of schools expanded throughout the country - an

- Urban residence at the age of 13 remained an important deterninant of educational attainment because of the great disparity between urban and rural areas in availability of secondary schools.

* The availability of material school inputs, such as textbooks and desks at the primary level, had a large positive effect on final school levels.

This paper is a vroduct of the Education and Employment Division, Population and Human Resources Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Cynthia Cristobal, room S6035, extension 33640.

The PPR WoTking PapeT Series dissenPdnates the rindings of work under way in the Bantes Policy, Planning, and Research Complex. An objective of die series is to get these rindings out quickly. even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent ofrficial policy of the Bank.

Produced at the PPR Dissemination Center
# **1 Introduction**

Improving the quality of education is one of today’s main challenges to policy makers in Indonesia and elsewhere in the developing world. Despite the fact that Indonesia doubled real spending on education in the last decade<sup>1</sup> , the quality of instruction, as measured through international student comparisons like PISA and TIMSS, has not (or not much) improved. PISA researchers conclude for example that half of Indonesia’s 15-year-olds do not have “a basic level of proficiency, at which students begin to demonstrate the reading literacy competencies that will enable them to participate effectively and productively in life [p50. in OECD (2010)].”<sup>2</sup> This suggests that many teachers and schools fail to prepare the next generation for an ever-more demanding global labor market.

This paper presents two empirical results for Indonesia that potentially also apply to the broader developing world. First, section (2) shows that subject-matter knowledge of primary school teachers in Indonesia is arguably low on average, and most likely a binding constraint to learning in many Indonesian primary schools. Second, section (4) presents evidence that increases in teachers’ subject-matter knowledge can help increase student-learning outcomes. The inference is based on three types of value-added regressions: a standard model in levels, a school fixed-effects model, and a flexible student-teacher fixed-effects model. The latter estimates the parameters of a value-added model, only using variation within each student-teacher pair across the three different subjects – mathematics, science and Indonesian language.

The student-teacher fixed-effects model attempts to address important threats to conventional value-added models in levels. The fixed-effects results, for example, would be insensitive to situations in which intrinsically more able students students are matched to better schools or teachers. Moreover, models in levels are incapable of separating out a teacher’s subjectmatter knowledge from other (unobserved) teacher attributes, such as motivation, general intelligence, etc. The idea of looking at within-student variations across different subjects is not new and has been successfully applied before by Metzler and Woessman (2012).<sup>3</sup> Metzler

> 1See World Bank (2013)

> 2Almost 80% of Indonesian 15-year-olds do not have a baseline level of proficiency in Mathematics, that is below level 2 as coded by OECD (2010).

> 3The idea of using within-student variation (across subject scores, or otherwise) has been used before, see for example Dee (2005), Dee (2007), Ammermüeller and Dolton (2006), Clotfelter, Ladd, and Vigdor (2010),

2
but primarily in the lower end of the distribution, which is consistent with the existence of a previous lower bound.

In addition, the simultaneous reduction in both unofficial payments and tax payments for low-risk firms suggests that the unofficial payments were more likely not for collusion to pay lower taxes but for extortion. Less-frequent interaction with tax officials due to e-filing therefore reduces the occurrence of bribe-giving.

Whereas the impact on tax payments for low-risk firms occurs primarily on the extensive margin (number of months paying positive taxes), Table 7 (column 2) indicates that the increase in tax payments for high-risk firms occurs on the intensive margin (the amount of taxes paid, conditional on paying in a given month). Similarly, the cumulative distribution function shows that, among high-risk firms, the higher tax payment among Group A firms relative to the combined control group occurs mostly on the upper end of the distribution (Figure 3, panel C). This pattern would be consistent with high-risk firms—both treatment and control—not being constrained by tax officials on the minimum amount of tax they could pay (unlike low-risk firms). However, with e-filing, treatment firms at the upper-end of the distribution lose support to reduce high levels of tax liabilities.

For high-risk firms, the increase in tax payments is consistent with their losing the opportunity to collude with tax officials to reduce their tax liabilities. From anecdotal evidence, one of the deterrents to evasion under e-filing is higher transaction costs for collusion, because after a firm submits a declaration online it is difficult to change it (unlike declarations filed on paper, which are easy to change). From the survey data, we find some additional evidence consistent with high-risk firms becoming less able to rely on assistance from colluding with tax officials. We find that the amount of time firms spent daily collating records _increases_ by 1.2 hours among more risky firms, while among low-risk firms there is no significant impact (p=0.042 for the difference between the two 31
new EU member countries. Tenure status does not vary as much across age brackets in the new EU countries as it does in Western and Southern Europe.

Many household and individual characteristics are correlated with tenure status, but are also correlated with each other. To formally explore the relationship between household characteristics and the decision to rent, own outright or own with a mortgage, we first run multinomial regressions of the following form: where y can take on three values: zero if the household rents the dwelling, one if the household owns the dwelling it lives in and holds a mortgage and two if the household owns the dwelling without holding a mortgage, while X is a set of household characteristics. Given the unordered nature of the three choices, we use multinomial regressions. For two countries – Denmark and Netherlands-, we use simple logit regressions, as the survey sample in these countries does not include any owners without mortgages. We run separate regressions for each survey, yielding a total of 69 regressions. Although we focus mostly on the 2007 results, we make comparisons across survey-years, where there may be some notable trends. All the results make use of household weights provided in each survey.<sup>13</sup> The probability that a household will choose one of the three options is: Given that the probabilities add up to one, we normalize β 1=0, which allows us to derive two log-odd ratios: where pj denotes the response probability in equation (2).<sup>14</sup>

> 13 As mentioned above, we do not have sufficient information on the stratification in order to correct the standard errors for cluster effects. This might result in a downward bias in our standard errors. 14 See Woolridge (2001) for a discussion.

14
negative drought impacts.

In all estimations, the height for age z score has significantly positive effects on schooling, though only highly frequent droughts decrease the positive effect.

# **5.2.3. Malawi**

Tables 9 and 10 summarize estimation results for Malawi, using the same specification as Ethiopia. In Malawi, we focus on the 2001 drought. Although this drought was followed by a flood in 2001-2002, our preliminary analysis showed that the flood had insignificant impacts so we decided to focus on the 2001 drought.

# Tables 9 and10 to be inserted

In Table 9, we examined how the height for age z score affects drought impacts on child schooling. The results in Columns 1 to 4 show that the height effect is very robust to the drought, but the drought (and its likelihood of occurrence) does not have significant effects on schooling. This result remains the same even if we use the drought frequency (Table 10).

In Columns 5 and 6, we split the sample based on the ranges of the height z score. Interestingly, we observe a clear contrast between the two groups. Children who embody less human capital experienced significantly negative impacts of droughts. The increased frequency of droughts significantly reduces schooling completed. Moreover, a higher frequency of droughts decreases the positive effect of the height for age z score. In contrast, we observe significantly positive effects of droughts among healthy children (with greater height for age z scores), suggesting that healthier children are better able to invest in human capital after a disaster. This result also supports our proposition that the accumulation of human capital prior to disasters prevent adverse impacts on human capital formation.

# **6. Conclusion**

This paper examined the impacts of disasters on dynamic human capital production using panel data from Bangladesh, Ethiopia and Malawi. Empirical results showed that the accumulation of (biological) human capital prior to disasters helps children maintain investments in intellectual human capital in the post-disaster period. Human capital formed in early childhood (nutritional status, proxied by height for age z scores) plays a role of insurance with resilience to disasters by increasing schooling investment and outcomes, despite negative disaster impacts on the investment itself.

In Bangladesh, children with more biological human capital are less affected by the adverse effect of the flood, and the rate of investment in intellectual human capital increases with the initial human capital stock after the disaster, achieving a faster recovery. In Ethiopia and Malawi where droughts are rather frequent, the exposure to highly frequent droughts reduced growth and inequality of human capital. Human capital stock helps maintain investments in the long run, though highly frequent shocks disrupt subsequent investments. In all countries, our evidence shows that children embodying more human capital prior to disasters are resilient to the disasters and experience a faster recovery.

15
2 costs of an intervention), section IV covers cost-effectiveness, section V financing, and section VI summarizes. The bulk of the results are presented in tables, with a brief text discussion. There is much that can (and has been) said about cost and effectiveness of interventions without recourse to - actual cost data. Rather than repeat the conclusions of other studies, this paper tri; to amass as large a database as available, in order to draw some new conclusions.

# I. Methodology.

This section discusses some of the methodological problems, both with respect to defining cost-effectiveness, and with obtaining data on costs and impact of nutrition interventions. Costeffectiveness methodology is widely used in the health area (as a more readily quantified alternative to cost-benefit), and it therefore seems natural to extend this to nutrition interventions. Costeffectiveness studies have been very widely undertaken for child survival initiatives in developing countries, for instance. Stewart (1988) for example surveys the cost-effectiveness of four types of child survival interventions, and Brenzel (1989) surveys 28 immunization projects in 16 developing countries. However there is a paucity of such studies in the nutrition area.

There are very few attempts to undertake cost-benefit analysis of nutrition interventions (Scandizzo and Swamy, 1982, for the food distribution system in India, and for micronutrients Levin, 1985, Correa, 1980 and Popkin et al, 1980). Cost-benefit studies will not be discussed here.

It is interesting to speculate as to why so few cost-effectivenes **s** tudies exist for nutrition interventions. One reason is perhaps that less is spent on nutrition interventions than on some health interventions (nutrition expenditures form about 10% of health expenditures in developing countries). More importantly, it is murch more difficult to assess the impact of nutrition interventions which occur over a reasonably long period of time, with equally long-lasting results, as compared to the impact of an immunization program with a very well defined, short duration "input", and an easily measured
expression is a function of Kakwani’s index of progressivity. The last term on the right reflects the extent of reranking in the transition from the pre-tax to the post-tax income distribution<sup>15</sup> .

Aronson, Johnson and Lambert (1994), here after AJL, show that when tax liabilities are determined on the basis of income and non-income factors, differences in tax treatment at given levels of (equivalent) income are bound to arise. On top of this possible unequal treatment of equals, the tax system may also imply reranking among unequals. These authors demonstrate<sup>16</sup> that the redistributive effect of a tax, _RE_ = _G X_ − _GY_ , can be written as a function of three factors: (1) the vertical or progressivity effect ( **V** ), (2) a measure of classical horizontal inequity ( **H** ), and the Atkinson-Plotnick index of reranking ( **R** ). Formally, we have: _RE_ = _V_ − _H_ − _R_ (2.3) Assuming that the population has been divided in groups of pretax equals, the various components of (2.3) are defined as follows. _V_ = ( _G X_ − _Gb_ ) , where Gb the between group Gini coefficient of the distribution of post-tax income. Classical horizontal inequity is measured by: _H_ = ∑α _k_<sup>_G_</sup> _k_ , where α **k** is the product of the population share and the post- _k_ tax income share in group **k** , and **Gk** is the Gini index of inequality in the distribution of after-tax income in group **k** . Reranking is given by the last term in (2.2): _R_ = ( _GY_ − _CY_ ) . The fact that these components are positive implies that both horizontal inequity and reranking reduce the vertical or progressivity effect.

Hyun and Lim (2005) use the AJL methodology to examine the redistributive effect of the Korean income tax system over three years: 1991, 1996 and 2000. Their empirical results based on microdata sets collected by the Korean National Statiatical office are presented in table 2.4. The last three rows of this table show the normalized version of the three components of the redistributive effect as a percentage of RE. The

> 15 It is important to keep in mind that in the benchmark model of income tax where both tax and post-tax income increase with pre-tax income, there is no reranking as we move from the pre-tax to the post-tax distribution of income.  In other words, the benchmark case implements a rank-presenving transformation of pre-tax incomes into post-tax income.

> 16 Their demonstration hinges on the following decomposition of the Gini coefficient of post-tax income distribution: _GY_ = _Gb_ + ∑α _k Gk_ + _R_ . _k_

15