rafmacalaba/datause-encoder-data huggingface dataset:
Table 1:

# Curricular Emphases on Mathematics and Science Education at the Primary and Lower Secondary Levels, 1960s **°** 1980s, by World Region (constant cases) I

# A. Proportion of the Official Pfimary Curticulun Devoted to Mathematics and Science Education

|Lat<br>|inAmerica/<br>Caribbean<br>|S<br> <br>Asia|ub-Saharan<br>Africa|Middle East/<br>NorthAfrica|OECD<br>Countries2|<br>Totals|
|---|---|---|---|---|---|---|
|Mathemati<br>|cs<br>||||||
|1960<br>|18.6 (13)<br>|15.2 (14)<br>|15.1_(15)_|15.9 (17)|17.9 (20)|16.6 (79)|
|1980|19.1 (13)|17.9 (14)|17.5 (15)|17.0 (17)|19.3 (20)|18.2 (79)|
|Sciences<br>|||||||
|1960<br>|10.5 (13)<br>|8.6 (14)<br>|5.2 (15)|6.9 (17)|6.1 (20)|7.3 (79)|
|1980|11.6 (13)|7.6 (14)|7.1 (15)|7.6 (17)|6.8 (20)|7.9 (79)|

# B. Average Annual Hours of Instruction in Mathematics and Science at the Primary Level

|L<br>|adn America/<br>Caribbean<br>|<br> <br>Asia|Sub-Saharan<br>Aflica|Middle East/<br>North Africa|OECD<br>Countries2|Totals|
|---|---|---|---|---|---|---|
|Mathematic<br>|s<br>||||||
|1960<br>|168.2 (7)|130.9 (11)|128.4 (7)|130.4 (13)|167.5 (17)|146.5 (55)|
|1980|171.8 (7)|156.7 (11)|151.8 (7)|149.6 (13)|170.7 (17)|160.6 (55)|
|Sciences<br>|||||||
|1960<br>|88.3 (7)<br>|71.9 (11)|54.8 (7)|53.2 (13)|56.4 (17)|62.6 (55)|
|1980|85.3 (7)|62.6(11)|66.0 (7)|68.1 (13)|58.8(17)|65.8 (55)|

# C. Emphasis on Mathematics and Science Education at the Lower Secondary Level (1980s only)

|L<br>|atin America/<br>Caribbean|Asia|Sub-Saharan<br>Africa|Middle East/<br>North Africa|OECD<br>Countries2|Totals|
|---|---|---|---|---|---|---|
|Mathematics<br>|||||||
|Percent<sup>3</sup><br>|14.5 (17)|14.9 (10)|16.5 (18)|13.5 (17)|14.0 (16)|14.7 (78)|
|Weekly Hrs<sup>4</sup>|12.5 (19)|12.1 (10)|13.3 (19)|11.5 (17)|11.8 (17)|12.3 (82)|
|Sciences<br>|||||||
|Percent<sup>3</sup><br>|17.0 (17)|17.8 (11)|14.6 (18)|13.3 (16)|16.7 (16)|15.7 (78)|
|Weekly Hrs<sup>4</sup>|13.6 (18)|14.2 (11)|11.7 (19)|11.1 (17)|14.0 (17)|12.8 (82)|

1. Unweighted averages, number of cases in parentheses

2. This category includes Westem Europe, North America, Australia and New Zealand.

3. This row refers to the percentage of the official lower secondary curriculum devoted to each subject 4. This row refers to the average wekly hours of instruction at the lower secondary level in each subject
**Figure 1: Manufacturing production in the United States and Europe** a) United States and Europe             b) Selected European countries

<!-- Start of picture text --> 115 125<br>110 120<br>115<br>105<br>110<br>100 105<br>95 100<br>95<br>90<br>Start of Great Recession 90 Start of Great<br>85 Recession<br>85<br>80 80<br>Euro area (17 countries) United States France Italy Germany United Kingdom<br>Production in Total Manufacturing, 2005 = 100 Production in Total Manufacturing, 2005 = 100<br>Source:OECD Stats. Source: OECD Stats.<br><!-- End of picture text --> 13. At the same time, private investment has been falling. In countries where unemployment rates increased steeply, housing prices have been falling, weakening household balance sheets even further. This effect was particularly pronounced in the U.S. where the burst of the housing bubble in 2008 triggered a vicious cycle with foreclosures, falling housing prices, and reduced household spending. Private investment fell steeply, largely driven by a decline in residential investments, but given the persistent excess capacity, non-residential investment also remains weak (see Figure 2). Similarly, private investment in Europe has fallen. Good private investment opportunities are hard to find when factories continue to carry spare capacity and office buildings remain vacant.

**Figure 2: Private investment and unemployment** a) United States b) European Union

<!-- Start of picture text --> 12% 10% 20%<br>18%<br>19%<br>10% 9%<br>19%<br>16%<br>18%<br>8% 8%<br>14% 18%<br>6% 7% 17%<br>12%<br>17%<br>4% 6%<br>10% 16%<br>2% Unemployment rate (left)Private Investment - Nonresidential (in% of GDP, right) 8% 5% Unemployment rate (left) 16%15%<br>0% Private Investment - Total (in % of GDP, right) 6% 4% Private Investment - Total (in % of GDP, right) 15%<br>20002001200220032004200520062007200820092010<br>Source: Authors' calculations based on data from BEA and BLS. Source: Authors' calculations based on data from Eurostat.<br>00q1 00q4 01q3 02q2 03q1 03q4 04q3 05q2 06q1 06q4 07q3 08q2 09q1 09q4 10q3 11q2<br><!-- End of picture text --> 5
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles

|**Table 20: Public**<br>**middle income c**<br>|**debt, pensio**<br>**ountries for**<br>|**n spending and implicit pe**<br>**various years during the en**<br> <br>|**nsion debt**<br>**d-1990s a**<br>|**for 35 low and**<br>**nd 2000**<br>|
|---|---|---|---|---|
|Country|Public debt<br>|IPD b<br>Pension<br>|y discount r|ate|
||1999/2000|Spending<br>2%|4%|5%|
|||as share of GDP<br> <br>|||
|Brazil|33|9<br>500|330|275|
|Macedonia|41|9<br>441|291|244|
|Slovenia|25|11<br>429|298|255|
|Romania|18|6<br>386|256|214|
|Poland|43|12<br>379|261|220|
|Ukraine|59|9<br>365|257|220|
|Portugal|55|5<br>358|233|193|
|Malta|56|5<br>356|234|194|
|Slovakia|31|8<br>304|210|179|
|Hungary|59|9<br>300|203|171|
|Uruguay|45|14<br>295|214|187|
|Kyrgyz Rep.|135|7<br>282|185|154|
|Croatia|33|11<br>274|201|175|
|Estonia|7|9<br>268|189|163|
|Moldova|78|8<br>229|159|136|
|Lithuania|28|7<br>221|155|134|
|Nicaragua|109|2<br>220|131|104|
|Turkey|65|5<br>217|146|123|
|Costa Rica|34|2<br>203|121|97|
|Philippines|71|1<br>185|107|81|
|Iran|10|2<br>146|89|72|
|Bolivia|56|4<br>111|73|62|
|Argentina|53|5<br>106|85|78|
|<br>Ecuador|209|1<br>103|63|51|
|Mexico|19|1<br>101|65|54|
|Colombia|24|2<br>88|56|46|
|Dominican Rep.|23|1<br>80|49|40|
|Cape Verde|52|1<br>78|47|38|
|Chile|9|7<br>77|60|53|
|Senegal|78|2<br>73|51|44|
|<br>Mauritius|35|3<br>63|47|42|
|El Salvador|22|2<br>60|43|37|
|Peru|43|2<br>57|40|34|
|Korea|33|1<br>57|33|26|
|Morocco|79|1<br>50|32|26|

**Sources** : Own calculations and public debt data based on SAVEM tables (World Bank), At-a-glance tables prepared for the Annual Meetings (World Bank), and various IMF statistics on Article IV consultations.

IPDs are high relative to conventionally defined public debt for most countries. Only a few countries (Cape Verde, Ecuador, Korea, Morocco, Nicaragua, Peru and Senegal) have higher explicit public debt than IPD even when using the 5 percent discount rate case. This result highlights the importance of this indicator for a proper assessment of long-term fiscal sustainability. It is also a measure of just how difficult a rapid shift from an unfunded system to a funded pension scheme would be in some countries. Last but not least, these figures suggest that pension debt or wealth could be a significant factor in determining savings behavior in many developing countries.

- 94 -
**Table 2. Econometric analysis of ERRs:** Single **policy variable Tobit specifications**

|Independent<br>variables|Parallel rat|e premia|Trade op|enness|Fiscal d|eficit|Distortions<br>pri|in tradable<br>ces|Real interes|t rate dummy|
|---|---|---|---|---|---|---|---|---|---|---|
|Specification|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Intercept|19.7|33.0|10.8|24.9|16.9|31.8|-207.0|-158.4|15.3|26.6|
|Policy variable|-0.055<br>(6.1)***|-0.049<br>(5.1)***|2.53<br>(2.8)***|2.18<br>(2.4)**|-0.33<br>(2.7)***|-0.34<br>(2.9)***|-2.26<br> <br>(4.0)***|-1.93<br>(3.3)***|2.48<br>(2.1)**|1.30<br>(1.1)|
|Capital/labor<br>ratio (log)|-|-2.04<br>(3.0)***|-|-2.07<br>(2.7)***|-|-2.37<br>(3.4)***|-<br>|-2.29<br>(3.4)***|-|-1.88<br>(2.6)***|
|Years<br>of|-0.30|-0.18|-0.03|0.43|0.28|0.80|-0.66|-0.09|0.00|0.48|
|education|(1.0)|(0.6)|(0.1)|(1.3)|(0.9)|(2.4)**|(2.1)**|(0.3)|(0. )|(1.4)|
|Project<br>complexity|-2.74<br>(2.0)**|-3.16<br>(2 3)**|-3.50<br>(2.6)***|-3.80<br>(2.8)***|-3.43<br>(2.5)***|-3.68<br>(2.7)***|-3.54<br> <br>(2.6)***|-3.93<br>(2.9)***|-3.82<br>(2.8)***|-3.90<br> <br>(2.8)***|
|Terms of trade|0.02|0.00|0.00|-0.01|0.01|-0.02|0.1|0.08|0.04|0.01|
|improvement|(0.2)|(0.1)|(0.0)|(0.2)|(0.2)|(0.2)|(1.3)|(1.0)|(0.5)|(0. 1)|
|GDP growth|-|0.34<br>(1.6)|-|0.49<br>(2.3)**|-|0.64<br>(3.1)***|-<br>|0.29<br>(1.4)|-|0.69<br>(34)***|
|Log likelihood|-2526|-2519|-2534|-2526|-2540|-2528|-2522|-2515|-2541|-2532|
|No.<br>of<br>observations|656|656|656|656|656|656|656|656<br>_|656|656<br>L|

Notes: Dependent variable is reestimated economic rate of return (ERR) for public and private projects Numbers in parentheses are t-statistics Significance levels: *** = 99 percent; ** = 95 percent; * =90 percent. a/ Each pair of columns includes a different policy variable, as indicated.

Sources: Authors'calculations.
It is important to note that the uniqueness of the truthful vote is not guaranteed in general under the conditions of Theorem 5. For instance, if we have 4 voters with preferences _R_<sup>1</sup> = _R_<sup>2</sup> = _x_ 1 _x_ 2 and _R_<sup>3</sup> = _R_<sup>4</sup> = _x_ 2 _x_ 1. Then, the strategy profile _s_ = ( _s_ 1 _,s_ 2 _,s_ 3 _,s_ 4) = (1 _,_ 1 _,_ 0 _,_ 0) is another Nash equilibrium different from the truthful vote _s_<sup>∗</sup> ( _R_ ) = (1 _,_ 1 _,_ −1 _,_ −1).

**Example 9 :** _Let_ ( _N,X,_ ( _Ri_ ) _i_ ∈ _N ,W,F_ ) _be a ranked-choice election as described in Theorem 5, and where N_ = {1 _,_ 2 _,_ 3} _, R_<sup>1</sup> = _R_<sup>2</sup> = _x_ 1 _x_ 2 _, R_<sup>3</sup> = _x_ 2 _x_ 1 _, and c_ 1 = _c_ 2 = _c_ 3 =<sup><u>1</u></sup> 3<sup>_.Therefore,_</sup> _the truthful vote is s_<sup>∗</sup> ( _R_ ) = ( _s_<sup>∗</sup> 1<sup>(</sup><sup>_R_)</sup><sup>_,s_∗</sup> 2<sup>(</sup><sup>_R_))=(1</sup><sup>_,_1</sup><sup>_,_−1)</sup><sup>_.Table3displaysthepossible_</sup> _strategy voting profiles along with the corresponding payoffs._

voter 2

||1|0|−1|
|---|---|---|---|
|1|( <sup>2</sup><br>3<sup>_,_ 2</sup><br>3<sup>_,_−1</sup><br>3<sup>)</sup>|( <sup>1</sup><br>6<sup>_,_ 1</sup><br>2<sup>_,_ 1</sup><br>6<sup>)</sup>|(−<sup>1</sup><br>3<sup>_,_−1</sup><br>3<sup>_,_ 2</sup><br>3<sup>)</sup>|
|voter 1<br>0|( <sup>1</sup><br>2<sup>_,_ 1</sup><br>6<sup>_,_ 1</sup><br>6<sup>)</sup>|(0_,_0_,_ <sup>2</sup><br>3<sup>)</sup>|(0_,_−<sup>1</sup><br>3<sup>_,_ 2</sup><br>3<sup>)</sup>|
|−1|(−<sup>1</sup><br>3<sup>_,_−1</sup><br>3<sup>_,_ 2</sup><br>3<sup>)</sup>|(−<sup>1</sup><br>3<sup>_,_0</sup><sup>_,_ 2</sup><br>3<sup>)</sup>|(−<sup>1</sup><br>3<sup>_,_−1</sup><br>3<sup>_,_ 2</sup><br>3<sup>)</sup>|

voter 3: _s_ 3 = −1

**Table 3:** The voters’ payoffs ( _u_ 1( _s_ ) _,u_ 2( _s_ )) for all _s_ ∈ _S_ of Example 9.

_As expected, the truthful vote is not a Nash equilibrium since voter 3 can profitably deviate to s_ 3 = 0 _. In addition, we remark that this game does not admit any Nash equilibrium._ This example suggests that a pure strategy Nash equilibrium may not exist if the election is not tight. However, there are instances where a Nash equilibrium may exist 27
these 5 procedures from all 68 hospitals in Croatia.<sup>6</sup> The final dataset consists of 352,618 records (number of cases). Each record contained data concerning patient age, facility, treatment dates, length of stay, and amount billed to HZZO. The main outcomes of interest are: i) volume of activity, ii) average length of stay (ALOS).

The interrupted time series approach requires data to be in equally spaced intervals over time, so the data was aggregated to each month for each hospital. A fixed-effects, firstorder autoregressive model was estimated as specified in Equation (1) to analyze the effect of: i) the full introduction of PPTP in 2005, and ii) the introduction of DTS in 2009: In Equation (1), _Yht_ are various outcomes of interest (admissions, length of stay) for each hospital _h_ and month _t_ , _Montht_ is a time trend, _γmonth_ is a vector of dummy month variables to correct for seasonality, and _μh_ is a fixed-effect controlling for time-invariant unobservables for each hospital. _DTSt_ and _Month_DTSt_ are variables associated with the introduction of DTS— _DTSt is_ a binary dummy variable that captures the level effect at time 0 when DTS is introduced, and _Month_DTSt_ is a time variable starting when DTS is introduced to capture the change over time due to DTS. A linear combination of _β2_ + _β3*12_ is used to estimate the impact of introducing DTS by December 2009. Similarly, PPTPt and Month_PPTPt are dummy and time variables associated with introduction of PPTP, and a linear combination of _β4_ + _β5*48_ is used to estimate the impact of PPTP. Here we focus on the major expansion of PPTP and the introduction of new PPTP codes beginning Jan 1, 2005. _Xht_ are observable covariates within hospitals over time such as patient age, condition at hospitalization entry, and type of health insurance. _εht_ are first-order autoregressive standard errors to account for serial correlation.

# **System-level Descriptive Statistics**

Table 3 presents annual summary statistics for each of the procedures from 20002009. Over the ten year period, cataract procedures accounted for the greatest proportion of these five procedures when counting by absolute numbers of cases (39%), but in terms of total billed costs, hip replacements accounted for the highest percentage (28%). Considerable variation can be seen in both the number of cases and the length of stay for each procedure;

> 6 Total number of hospitals across the observed period.

6
**_Table 5: Distribution of Education and Health Expenditures in Cali, by income quintile, September 1999 (percent)_**

||**1**|**2**|**3**|**4**|**5**|**Total**|
|---|---|---|---|---|---|---|
|**Primary education**|32.9|30.5|21.8|9.3|5.6|100.0|
|**Secondary education**|23.4|25.7|21.7|18.8|10.4|100.0|
|**University education**|4.2|15.3|23.2|24.5|32.8|100.0|
|**Public health centers**|31.0|24.9|25.1|11.9|7.1|100.0|
|**Public hospitals**|27.7|22.1|24.4|12.3|13.5|100.0|
|**SISBEN**|44.2|23.7|21.2|9.3|1.6|100.0|

Source: Own calculations based on EPSOC (1999).

# _4.3. Service Satisfaction_

Access and distribution of city expenditures is one angle to look at municipal programs – another angle is to see how their quality is rated. Part of the EPSOC survey was geared to obtain feedback from the population on satisfaction levels with municipal services in education, health and infrastructure provision. We were particularly interested to see whether the ratings of city services differed between the poor and non-poor populations.

As Table 6 reports, satisfaction levels varied significantly across services. Contrary to expectations, students attending school showed little dissatisfaction levels although a further breakdown revealed that dissatisfaction with public education (12 percent) was twice as high as dissatisfaction levels with private education (5 percent). Similarly, electricity, water and garbage collection were viewed as satisfactory by a large majority of the population. Health dissatisfaction – again especially of public services – was considerably higher with about onequarter of the population not content with the service being offered. Probed for why the public health service was not viewed as positive, most respondents listed long waiting times as the single most important inconvenient aspect. The city’s sewerage service obtained similarly low ratings. And two-thirds of the population viewed as especially negative environmental cleanliness (‘ _saneamiento_ ’). This term included four components – regularity of (a) fumigation campaigns (against bacteria); (b) cleaning of public parks; (c) cleaning of public canals and tubes; and (d) cleaning of neighborhoods (streets and side-ways). Although all four aspects of such environmental cleanliness obtained negative ratings, dissatisfaction was clearly highest with the fumigation campaigns, across all regions of the city.

20
Policy Research Working Paper 8207

# **Abstract**

This paper uses new survey data to measure the government’s capacity to deliver goods and services in a manner that includes: high coverage of the population; equal access; and high quality of service delivery. The paper finds variation in these indicators across and within Indian states. Overall: (i) access to government provided goods and services is low—about 60 percent of the surveyed population are unable to apply for goods and services they self-report needing; (ii) inequality in access is high—women and poor adults are more likely to report an inability to apply for goods and services they need; and (iii) less than a third of the respondents who did manage to apply for a government delivered good or service found the application process to be easy. Access can be improved by reducing application costs and processing times, simplifying the application process, and providing alternative channels to receive applications.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at lklapper@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
6 growth in deposits took place via term deposits with maturities between 181 days and 3 years. However, changes in the composition of deposits from sight to term deposits, for example, is less significant in Russia than in other countries because retail deposits can be withdrawn on demand regardless of their contractual maturity. The latter might created unexpected liquidity problems for banks, and it brings a generalized uncertainty of duration gaps at a time when the demand for longer-term funding from consumers and enterprises is on the rise.

Sberbank’s market share of retail deposits continued to slide down. Interestingly, so did the combined market share of the 30 largest Russian banks (see Annex). This seems to indicate increasing competition for retail deposits in the banking sector. Anecdotal evidence suggests that foreign banks in particular have stepped up their acquisition efforts. Overall, the banking sector increased its reliance on deposits as a source of funding. The share of deposits among bank liabilities increased from 44.4 percent to 58.5 percent between 2004 and 2005. Yet, real interest rates on deposits still remain negative for all maturities (see annex). Interest rates spreads have also not changed significantly since 2003 (see fig. 4).<sup>9</sup>

**_<u>Fig. 3: Evolution of private deposits by type of bank (all, RUR, FX)</u>_**

<!-- Start of picture text --> 120<br>100<br>80<br>60<br>40<br>20<br>0<br>Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05<br>30 largest 100 largest Foreign all 10 largest foreign Sberbank Vneshtorg Regional All private**<br><!-- End of picture text --> _Source: DIA_ 9 The interest rates shown below are weighted averages based on the volume of deposits by type of instruments.
# **Accounting for Regional Differences in Mother and Child Health: Bangladesh, West Bengal, Bihar, and Jharkhand**

## **Susmita Dasgupta* David Wheeler**

_Key Words:_ Child wasting, maternal anemia, Bangladesh, Eastern India, spatial differences _JEL Classification_ : I120; I150.

### **This research was conducted under the South Asia Water Initiative - Sundarbans Landscape.**

*Authors’ names are in alphabetical order.

The authors are respectively Lead Environmental Economist, Development Research Group, World Bank; United States; and Senior Fellow, World Resources Institute.

We are grateful to Indrani Gupta, Subhendu Roy, Dinesh Nair and Mainul Huq for their review comments on this paper. We would like to extend our special thanks to Stuti Khemani, Tamer Samah Rabie and Michael Toman for their expert opinion. We are also thankful to Siobhan Murray for her valuable help with the maps.
on gas consumption in period 1 crucially depends on the specific information set held by consumers.

Since households are reclassified every period on the basis of their annual accumulated consumption, fully informed consumers in the treatment and control groups face the same expected price moving forward. Hence, if perfectly informed, both groups would have essentially the same incentive to restrain consumption so as not to surpass the 1500 cubic meters threshold in period 1, despite the fact that the bill received in period 0 contained sharp differences in unit prices. However, in the light of the well-documented prominence of information imperfections in residential energy markets with ex-post billing (Shin [1985]), and considering the complexity and novelty of the price determination mechanism in the residential market for natural gas of Greater Buenos Aires, we would expect consumers to be imperfectly informed. To provide further evidence on the information set held by consumers, we have surveyed a subset of households in the estimation sample.

# III(ii). _Survey evidence on consumers located near the discontinuity_

We have administered a telephone survey to 353 households from the estimation sample. The sub-sample surveyed was stratified by district to ensure an adequate geographical representation. The survey was conducted in September 2010 and targeted the member of the household that was responsible for paying the gas bill.<sup>7</sup> The survey questionnaire consists of two sections. The first section collected information on basic socioeconomic characteristics of the household head (age, education, occupation) and housing conditions. The second section explored knowledge about the amount billed and payment method, and assessed the extent to which consumers read their utility bills. In addition, this section collected extensive information on perceived and objective knowledge on how the amount billed is computed; specifically, on: (1) how frequently tariffs are determined; (2) the past consumption periods used for their determination; and (3) the consumption threshold that leads to a higher unit price.

# "Place Table II approximately here."

Table II reports summary statistics on surveyed households. For comparison, it also provides summary statistics from the national Household Survey (Encuesta Permanente de Hogares) on households living in Greater Buenos Aires. Relative to the average resident, surveyed household members from our estimation sample are more likely to be female and married. They are also more likely to have tertiary education, and tend to be considerably older than the average resident. These differences are likely to reflect the fact that the survey targeted the household member that was responsible for paying the gas bill. The fact that surveyed households are more likely to have tertiary education might also suggest that they tend to have higher-than-average income. Table II further suggests that surveyed households are more likely to be homeowners, and have a slightly larger number of rooms and different families. This evidence is consistent 6