rafmacalaba/datause-encoder-data huggingface dataset:
- 10 -

addresses the three questions posed in<sup>this paper: (a) how does the achievement</sup> of students in local secondary schools<sup>compare with that of students in either</sup> government or private secondary schools,<sup>and (b) how do these types of schools</sup> compare in terms of inputs and management,<sup>and (c) what characteristics</sup> of the schools account for any observed differences<sup>in achievement?</sup> 18. This paper contributes to<sup>the literature in three ways: (a) by exploring a</sup> larger variety of school types<sup>(national government public, local public and</sup> private schools) than previously<sup>examined in either developed or developing</sup> countries, (b) by extending the range<sup>of outc1me variables examined (achievement</sup> and attitudes), and (c) by using<sup>an appropriate multi-level model for examining</sup> school effects. It uses a hierarchical<sup>linear model (HLM) to estimate the</sup> effects of priveate, local public<sup>and national government public schools on</sup> student science achievement in the<sup>Philippines (see Raudenbush and Willms, 1990,</sup> for discussion of multi-level modelling).

# METHOD

# Background

19. Although school effects research<sup>requires the use of multi-level methods</sup> (see Aitken and Longford, 1986; Raudenbush,<sup>1988 for reviews), most previous</sup> research on private school effects has<sup>been conducted with single level models</sup> (e.g. Coleman, Hoffer and Kilgore 1982;<sup>Willms 1985; Alexander and Pallas 1985).</sup> Even efforts that have used sophisticatee<sup>statistical techniques to adjust for</sup> sel! ctivity have employed single-level<sup>models (e.g. Jimenez, Lockheed and</sup> Wattanawaha 1988; Jimenez et al. 1991).<sup>The situation, however, is changing.</sup>
# **1. Introduction**

The causes and consequences of corruption are well researched and widely known. There is ample cross-country empirical evidence that corruption slows growth, reduces public and private investment (Mauro 1995), and negatively impacts service delivery (World Bank 2004). However, knowledge about what causes individuals to engage in corrupt or illegal activities is scant. Several determinants of individual corruption have been recently proposed among which the most stunning and not least controversial is gender. Proponents of this theory claim that men’s proclivity towards corruption is inherently higher than women’s. This paper investigates this issue and presents new evidence based on data collected during a public expenditure tracking survey (PETS) in Tajikistan’s health sector. It shows that women are no less prone to extorting illegal payments from patients than men once the proper variables, such as power conferred by position for instance, are controlled for.

Men and women certainly display countless obvious physical and physiological differences. Attitudinal gender differences have been an active research matter for sociologists and psychologists for many decades. Although moderate to large differences are acknowledged in some areas (e.g. sexuality and aggression) men and women display more similarities than differences (see Hyde, 2005). Economists started to integrate gender in their analysis only recently and have uncovered interesting gender differences mostly through experiments. This paper studies two important forms of corruption in the health sector (1) informal charging and (2) moonlighting. Informal charging is a bribe-taking practice and is clearly classified as an illegal activity. Moonlighting is a more subtle form of corruption because it is not readily identified as theft or use of public office for private gain. In fact, moonlighting is a strong determinant of absenteeism and workers who collect their salary but do not show up at work can be considered as stealing public money (see Lewis 2006).

The main result of this paper is that with similar power and opportunities, women are equally likely as men to extract bribes by informally charging patients. However, among doctors and doctors only, women are less aggressive than men, i.e., they charge smaller amounts. Women are, on the other hand, significantly less likely to moonlight than men and this holds true even within positions. Finally the paper shows that there is some evidence for the fair wage2
discontinuities in costs could interact with the distribution of ability, consider the simplest of examples-one in which there are three populations, a "poor" population, which can only afford primary schooling, a "medium" population, which can afford primary and secondary schooling (but not university), and a "rich" population, which can afford primary, secondary, and tertiary schooling. Figure _5_ shows a hypothetical example in which ability is assumed to rise with years of schooling, and to reach the same maximum for those students who are attending the highest year of schooling they can afford. In this example, discontinuities in costs, which result in a cut-off in the amount of schooling which different populations can afford, lead to discontinuities in the distribution of aggregate ability (the heavy line in the graph)-where aggregate ability is simply the weighted average of the ability of "poor", "medium", and "rich" students for a given year of schooling. If unobserved ability is rewarded in the labor market, as seems likely, then the discontinuities in ability could result in discontinuities in the earnings function. Note, however, that the hypothetical distribution of ability in Figure _5_ would lead to inordinately "low" earnings for those with only the first year(s) of secondary number of children in household h who are attending primary school, secondary school, and university in 1998, and the constant in the regression has been suppressed. The parameters 0, y, and 8 will provide a reasonable approximation to the private unit costs, by level, if attendance rates in 1998 are close to those in 1997. This procedure assumes that household composition, the fraction of children who are attending school at every level, and actual (rather than measured) unit costs remained constant between 1997 and 1998. There is no reason to suppose that household composition or unit costs changed in the Philippines between 1997 and 1998. Attendance rates may have dropped, however, because of the effect of the East Asian crisis on incomes in 1998, even though the Philippines was much less affected by the crisis than other countries in the region. Administrative data show no significant impact of the crisis on _enrollment_ rates in primary or secondary school; but attendance rates may still have fallen if households were more likely to keep their children at home or doing odd jobs. A weaker assumption for the calculation of unit costs is that attendance rates in primary school, secondary school, and university in 1998 are a constant _fraction_ of attendance rates in 1997, such that Ep<sup>98</sup> = MIEP<sup>97</sup> , Es<sup>98</sup> **=** VE.97, and EC9' = WE,<sup>97</sup> , where the subscripts stand for education levels, superscripts for years, and the parameter N' is less than one. Under this assumption, the proposed estimation procedure will over-estimate unit costs by a factor<sup>1</sup> /, but will produce accurate estimates of the _ratio_ of unit costs across levels.

21
10 infrastructure, multigrade teaching, inspection frequency). Absence rates decline as national per-capita incomes rise.

As Table 2-2 summarizes, absenteeism in primary schools ranges from 11% in Peru to 27% in Uganda. Losses can be large. For example, a study of schools in one region of Kenya found that teachers were absent from school 30% of the time.<sup>39</sup> An unpublished study conducted in a sample of secondary schools in Senegal reported that teachers were absent approximately 30% of the time.<sup>40</sup> An observational study of 120 schools in rural Ghana, reported that, on average, teachers attend schools four days a week, implying that 5.5 instructional hours are lost each week. On the day that researchers visited schools, almost one-fifth (19.4%) of the teachers were absent.<sup>41</sup> A recent survey of absenteeism in Indonesia found that out of those reported to be full-time teachers 21% were absent from school; 27% out of those who were not absent at the time of the interview were out of their class and only 53% of them were engaged in teaching activity.<sup>42</sup> Teacher absenteeism studies in South Asia show similar results. A nationally representative survey of 3750 schools in India showed that 25% of teachers in government primary schools are absent at a typical point during a school day.<sup>43</sup> Absence rates ranged from 15% in Maharashtra to 38% in Bihar and 42% in Jharkhand. Another survey in Udaipur focused on 60 non-formal education centers run by a non-governmental organization and found teachers’ absence rates of 36%.<sup>44</sup> The PROBE study in India, which was carried out in 234 randomly selected villages of Bihar, Madhya Pradesh, Rajasthan, Uttar Pradesh, and Himachal Pradesh in late 1996, found that one-third of the teachers were absent during research staff school visits.<sup>45</sup> Other surveys of primary schools in the Indian states of Uttar Pradesh and Madhya Pradesh found that 17% of teachers were absent from school and additional 20% were in school but absent from class.<sup>46</sup> Absenteeism among primary school teachers in West Bengal was 20%,<sup>47</sup> in Bangladesh it was 15.5%,<sup>48</sup> while in Pakistan, about 18% of public and private school teachers were, on average, absent from school.<sup>49</sup> Cross-sectional averages may mask the extent of this problem because in Pakistan the researchers found that 23.5% of primary school teachers were absent during at least one of the two visits.

39. Vermeersch and Kremer 2004 40. Diouf 2005 41. EARC 2003 (Educational Assessment and Research Centre) 42. Suryadarma et al 2004 43. Chaudhury et al. 2004b 44. Duflo and Hanna 2005 45. Public Report On Basic Education; De and Dreze 1999 46. Rao 1999; World Bank 2001 47. Sen 2002 48. Chaudhury et al. 2004a 49. Ali and Reed 1994. Subsequent research in rural Northwest Frontier and Punjab showed 14% absenteeism in public schools; in boys’ schools teacher absenteeism was only 11% and in private schools it was 9% (Sathar et al. 2005).
# **Table A11: Second stage: Log Refugees at 100km instrumented by distance-based IV with decay function represented (i.e. IV generated using 1)mean border distance,** **<u>2)square of mean border distance and 3) square root of mean border distance)</u>**

|||||L|og of welfare|(consumption|aggregate per a|dult equivalen|t)||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Log of refugees (100km) predicted by Log of<br>Distance IV (mean border distance)|0.05464<br>(0.07485)|0.05431<br>(0.07471)|0.12505*<br>(0.07173)|0.11830*<br>(0.06503)|||||||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (square of mean border distance)|||||0.06371|0.06398|0.14884*|0.14469**|||||
||||||(0.07608)|(0.07598)|(0.07755)|(0.07078)|||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (square-root of mean border<br>distance)|||||||||0.06550|0.06475|0.11356|0.10918*|
||||||||||(0.07593)|(0.07577)|(0.07155)|(0.06513)|
|HH head age||0.00244|0.01173**|-0.00202||0.00242|0.01168**|-0.00251||0.00242|0.01175**|-0.00185|
|||(0.00511)|(0.00563)|(0.01666)||(0.00511)|(0.00563)|(0.01671)||(0.00512)|(0.00562)|(0.01664)|
|Age squared||-0.00003|-0.00008|0.00001||-0.00003|-0.00008|0.00001||-0.00003|-0.00008|0.00000|
|||(0.00005)|(0.00005)|(0.00014)||(0.00005)|(0.00005)|(0.00014)||(0.00005)|(0.00005)|(0.00014)|
|HH head sex (male)||0.00407|0.02267|-0.08058||0.00401|0.02239|-0.08093||0.00401|0.02281|-0.08047|
|||(0.03472)|(0.03776)|(0.06331)||(0.03471)|(0.03777)|(0.06346)||(0.03471)|(0.03775)|(0.06327)|
|Household size|||-0.02625***||||-0.02626***||||-0.02625***||
||||(0.00539)||||(0.00539)||||(0.00539)||
|Married monogamously|||-0.42458***||||-0.42366**||||-0.42502***||
||||(0.16461)||||(0.16471)||||(0.16454)||
|Married polygamously|||-0.37064**||||-0.36977**||||-0.37107**||
||||(0.17213)||||(0.17222)||||(0.17208)||
|Separated (divorced/widowed)|||-0.41299**||||-0.41264**||||-0.41317**||
||||(0.17258)||||(0.17272)||||(0.17251)||
|Completed primary sch|||0.22308***||||0.22317***||||0.22304***||
||||(0.03704)||||(0.03702)||||(0.03705)||
|Commercial farming|||0.10497||||0.10456||||0.10516||
||||(0.08958)||||(0.08966)||||(0.08952)||
|Wage employment|||-0.00101||||-0.00106||||-0.00098||
||||(0.04835)||||(0.04834)||||(0.04835)||

13
**Table 4: Production Function Estimation**

|||<br>|**(at const**<br>|**ant price**<br>|**, 1996=10**<br>|**0)**<br>||||
|---|---|---|---|---|---|---|---|---|---|
|Year|Sector|logA|Output<br>elasticity<br>To labor|T stat.<br> <br>|Output<br>elasticity<br>to capital|t stat.|R Z|MPL"|MPK'|
|1996|State|0.19|0.30|1.75|0.78|4.07|0.95|5917.8|0.22|
||Non-<br>state|1.85|0.41|3.78|0.68|7.12|0.97|13508.4|0.41|
|1997|State|-0.95|0.21|1.40|0.87|5.00|0.94|4759.8|0.24|
||Non-<br>state|1.72|0.40|3.21|0.69|6.21|0.96|15438.3|0.4|
|Common Effect<br>1994-97 (exl.<br>1995)|State|0.433|0.247|2.65|0.8345|8.02|0.94|5175.9|0.27|
||Non-<br>state|1.631|0.352|5.36|0.722|12.56|0.97|11470.8|0.47|

Notes: A indicates estimated figures.

27
migrating, 85.1 percent of them are married to a spouse born in the same country of origin, and only 8.5 percent are married to a U.S. native,<sup>16</sup> with no major differences by gender or age at migration. For those married after migration, the share of within-country marriage declines to 65.9 percent, while the share of marriages with a native increases to 20.2 percent (and the remaining 14 percent marry someone from a third country other than the origin or destination). The propensity to marry a native is larger for females (23.3 percent) than for males (17.6 percent).

Table 2 examines how the frequency of changes in marital status differs across four major origin countries (Mexico, India, China, the Philippines) and the Middle East and North Africa (MENA) region, which in total account for two third of all immigrants from developing countries. Differences in migration patterns are stark: while Mexican migrants tend to migrate young (median age standing at 23 for men) and are usually low-educated (less than 10 percent have a bachelor degree), the opposite is true for the other origin countries. For example, Indian migrate later in life and are particularly highly skilled (with 50 percent men holding at least a master degree). These demographic discrepancies translate into differences in the relative timing of migration and marriage decisions. While only 31 percent of Mexican men are married before migrating, this is the case for about 40 percent of Indian and Chinese men. Likewise, while 45 percent of Mexican women get married after migration, this is only the case for 22 percent for Indian women and 28.7 percent of women from Muslim countries in the Middle-East and North-Africa. Tables A.1 to A.5 in the Appendix reproduce the content of Table 1 separately for Mexico, India, China, Philippines, and the MENA region. Interestingly, we see that the likelihood of marrying a U.S. native after migration is substantially lower for Chinese (4.2 percent) and Indian (8.1 percent) compared to Mexican (22.5 percent) and Middle-Eastern and North-African (23.8 percent); the likelihood of intermarrying with a U.S. native is also particularly high for women from the Philippines (39.3 percent) compared to women from other countries (less than 20 percent).

The ACS data can be further used to provide descriptive evidence on the evolution of the probability of a change in marital status with the time since migration as a function of the

> 16Notice, as this might appear surprising, that the ACS provides information on the year in which an immigrant moved to the U.S. for the last time; multiple migration spells, which remain unobserved in the data, together with the possibility that the spouse is a second-generation immigrant from the same origin who also migrated in the past, might explain why some individuals appeared to have married a native before migration.

18
Table 6: Government subsidy per child, by total family expenditure quintile and level of <u>education (public schools only), in $</u>

|Quintile|Primary|Secondary|Tertiary|All levels|
|---|---|---|---|---|
|1st|69|114|70|75|
|2nd<br>|78|130|189|97|
|3<sup>rd</sup><br>|142|172|327|155|
|4<sup>th</sup><br>|138|224|454|206|
|5<sup>th</sup>|305|316|398|325|
|All (mean)|129|208|355|173|
|N|545|223|68|836|

Source: Own calculations using the 1998 Enquête Niveau de Vie des Ménages The subsidy amounts in Tables 6 and 7 were calculated as follows: (a) the relevant population was ranked by household expenditure; (b) the population was divided into expenditure quintiles; (c) the expenditure amount going to each quintile for each education level, as well as the number of children in each cell was calculated; (d) the expenditure amounts were divided by the number of children in each cell. The same methodology was used in calculating the family education expenditure amounts in Tables 4 and 5.

For those attending public schools (Table 6), the subsidy amount per student increases steadily as we go to higher expenditure quintiles. Students from families in the highest (5<sup>th</sup> ) quintile receive four times more than families in the lowest quintile. This finding is more pronounced in students in tertiary (professional) education. On average, each student receives about $173 per year. For those attending private schools (Table 7), the subsidy per student increases but the increase is less pronounced. Students from families in the highest expenditure quintile receive just over twice the subsidy received by students from families in the lowest quintile. However, the subsidy amounts going to students from families in the 2<sup>nd</sup> , 3<sup>rd</sup> , 4<sup>th</sup> and 5<sup>th</sup> quintiles are of similar magnitude. On average, each student going to public schools and are subsidized receive approximately $270 per year. Overall, the allocation of subsidies seems to be 15
Although urban concentration in China is well below that of comparative countries and the world average – the Gini coefficient is 0.43 against 0.56 for the world in 2000 (see Table 3) (Henderson 2004) - just 10 major municipalities account for almost a quarter of China’s GDP and possibly a third of its growth rate (see Table 4). It is in these urban crucibles that China’s new industrial economy is being nurtured.

**Table 3: Urban Gini Coefficients of Selected Countries**

|Brazil|Japan|Indonesia|UK|Mexico|Nigeria|France|India|Germany|USA|Spain|
|---|---|---|---|---|---|---|---|---|---|---|
|0.6|5<br>0.65|0.61|0.60|0.60|0.60|0.59|0.58|0.56|0.54|0.52|

_Source_ : Henderson (2004)

**Table 4: Large cities’ share of GDP**

|Rankby GDP|City|Share ofcity GDPtonational(%)|
|---|---|---|
|1|Shanghai|5.2%|
|2|Beijing|3.1%|
|3|Guangzhou|2.9%|
|4|Shenzhen|2.2%|
|5|Tianjin|2.0%|
|6|Chongqing|1.9%|
|7|Hangzhou|1.7%|
|8|Chengdu|1.6%|
|9|Qingdao|1.4%|
|10|Ningbo|1.4%|

_Source_ : China Statistical Yearbook (2003) However, the perspectives we gain from analyzing organizational changes in the urban context must be tempered by four considerations.

First, the MLSOEs in the major East Coast cities are losing ground to other firms and in certain subsectors are not the dynamic leading edge of the industrial economy. Many of the privately owned, foreign owned or joint venture firms are growing more rapidly and are aggressively experimenting with organizational as well as other innovations. Indeed, it can be 9
**Plc,Planning, and Research**

# **Health and NutrtitonI**

Hospitals dominate health care in most parts of the world and for a variety of reasons are likely to continue being a key factor in the overall performance of the health care system. Any efforts to improve this performance must therefore give greater hospital efficiency the highest priority.

Aftcr discussing key issues of managerial, clinical, and production efficiency, Frenk, Ruelas, and Donabedian suggest an agenda for research, which would include two types of research: Observational studies that document levels of hospital performnance and correlate them with organizational design and environrmental variables. It is especially imponant to devise and test sensitive, specific indicators of managerial, clinical, and service production efficiency.

- Comparative intervention studies that would introduce planned change in hospitals and assess - the consequences using control groups as well as cost-benefit and cost-effectiveness analyses.

The mostly highly recommended subjects for research! in order of priority, are:

* Good descriptive studies of the hospital system and the main aspects of organization - design to chart, for example, the formal and informal relations among managers and clinicians, the frequency of different arrangements for intemal communication, types of departmentalization, and management systems.

* The systematic design, testing, and study of explicit quality monitoring and assurance systems. Such studies should include the analysis of interactions between managers and clinicians, especially as they constrain clinical autonomy and decision making.

* Studies to determine which social, personal, organizational, and educational factors account for managerial skill and success in managing a - hospital to get the information needed for the recruitment and training of successful hospital managers.

* Studies of the structure and dynamics of medical labor markets, to improve understanding of why there is an oversupply of doctors in so mary different countries.

This paper is a product of the Health and Nutrition D.vision, Population and Human Resources Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Sonia Ainsworth, room S6-065, extension 31091 (37 pages with chans).

The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Plaruning, and Research Complex. An objectivc of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interprctations, and conclusions in these papers do not necessarily represent official policy of the<sup>Bank.</sup> Produced at the PPR Dissemination Center
_j_ from country _i_ as _Mi,j_ , we work with the following estimating equation.

The _Di_ terms are dummy variables assigned to each exporter, to reflect the set of exporterspecific variables that remain fixed across importers. The variables _NAFTAi,j_ and _EEAi,j_ are also dummies, capturing joint membership in either the North American or European free trade bloc. The terms _Disti,j_ and _Ti,j_ measure bilateral distance and import barriers (a combination of trade-weighted import tariffs and trade tax equivalents of export restraints) as a share of total import value. We expect the coefficients applied to these variables, _α_ 2 and _α_ 3 to both be negative. Recall that the _Index_ term is meant to capture the effects related to _σ_ in the discussion above. From the expressions in (8), we expect _α_ 6 to be negative as well. We expect the interaction term to be positive, based on equation (13) and Proposition 5. We have also included the interaction term _α_ 8 to allow for possible variations in the impact of tariff and competition-related barriers depending on the level of development of the trading partner. We explore this issue further below with split-sample regressions.

Table 2 presents robust regression results for equation (25), based on both versions of our competition index. We have reported robust regression results because the BreuschPagan (1979) Chi-squared test statistic (as implemented in STATA) leads us to reject the hypothesis of homoscedasticity at any conceivably reasonable level of significance. Further examination with Szroeter’s (1978) test statistic points to a pervasive problem, involving 15