rafmacalaba/datause-encoder-data huggingface dataset:
||||||||Root-m|ean-s|quare|error||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||n=|400|n=|1,000|n=|3,000|n= 1|0,000|n= 3|0,000|
|pT|pY|∆ATE|∆LAT E<br>∆AT E|BP|IV|BP|IV|BP|IV|BP|IV|BP|IV|
|0.1|0.1|0.07|<br>1.75|0.22|3.79|0.15|10.98|0.09|0.29|0.05|0.15|0.03|0.10|
|0.1|0.3|0.14|1.17|0.27|6.61|0.19|11.02|0.12|0.47|0.06|0.22|0.04|0.13|
|0.1|0.5|0.15|0.81|0.29|9.62|0.20|5.82|0.12|0.51|0.07|0.24|0.04|0.15|
|0.1|0.7|0.12|0.52|0.28|8.79|0.22|39.55|0.13|0.48|0.07|0.23|0.04|0.14|
|0.1|0.9|0.05|0.27|0.13|4.58|0.14|11.49|0.13|0.32|0.09|0.16|0.04|0.10|
|0.3|0.1|0.06|1.29|0.20|22.22|0.14|0.32|0.09|0.14|0.04|0.07|0.03|0.05|
|0.3|0.3|0.13|1.21|0.27|7.64|0.19|0.90|0.12|0.21|0.06|0.11|0.04|0.07|
|0.3|0.5|0.15|1.06|0.27|14.39|0.18|0.74|0.11|0.24|0.06|0.12|0.04|0.07|
|0.3|0.7|0.12|0.84|0.23|20.86|0.15|0.50|0.09|0.22|0.05|0.11|0.03|0.07|
|0.3|0.9|0.05|0.53|0.17|37.83|0.12|0.29|0.06|0.15|0.03|0.08|0.02|0.05|
|0.5|0.1|0.06|0.89|0.19|18.25|0.12|1.31|0.07|0.12|0.03|0.06|0.02|0.04|
|0.5|0.3|0.12|1.08|0.26|23.20|0.19|0.67|0.12|0.18|0.06|0.10|0.04|0.06|
|0.5|0.5|0.15|1.11|0.29|8.86|0.21|1.72|0.13|0.20|0.07|0.10|0.04|0.06|
|0.5|0.7|0.13|1.07|0.26|16.75|0.19|0.87|0.12|0.18|0.06|0.09|0.04|0.06|
|0.5|0.9|0.06|0.86|0.17|1.47|0.11|0.28|0.06|0.13|0.03|0.07|0.02|0.04|
|0.7|0.1|0.05|0.56|0.19|14.48|0.13|0.67|0.06|0.14|0.03|0.08|0.02|0.05|
|0.7|0.3|0.12|0.86|0.24|40.01|0.15|1.47|0.09|0.22|0.05|0.11|0.03|0.07|
|0.7|0.5|0.15|1.05|0.26|46.67|0.18|0.92|0.11|0.22|0.06|0.12|0.04|0.07|
|0.7|0.7|0.13|1.20|0.27|49.33|0.19|1.78|0.12|0.22|0.06|0.11|0.04|0.07|
|0.7|0.9|0.06|1.26|0.19|9.03|0.14|1.55|0.08|0.15|0.05|0.08|0.03|0.05|
|0.9|0.1|0.05|0.28|0.16|4.39|0.17|1.58|0.15|0.30|0.09|0.15|0.04|0.09|
|0.9|0.3|0.12|0.52|0.29|10.31|0.22|3.26|0.13|0.47|0.07|0.24|0.04|0.14|
|0.9|0.5|0.15|0.81|0.28|10.45|0.20|4.10|0.12|0.51|0.07|0.24|0.04|0.15|
|0.9|0.7|0.14|1.17|0.27|10.56|0.20|2.45|0.12|0.49|0.07|0.22|0.04|0.14|
|0.9|0.9|0.07|1.76|0.21|8.89|0.15|2.13|0.09|0.34|0.05|0.16|0.03|0.10|

Table 7: Root-mean-square error of ∆<sup>ˆBP</sup> ATE<sup>andˆ∆IVfortrueATEasafunctionofpTand</sup> pY , in bivariate probit model simulations with covariate X and ρ = 0.3.

30
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||Fuz<br>|zy RDD<br>|
|---|---|---|
||Linear|Quadratic|
|Eligibility rule<br>|0.18<br>|0.14<br>|
|(s.e.)|(0.016)|(0.018)|
|_X_1|-0.12|-0.19|
|(s.e.)|(0.0044)|(0.012)|
|_TX_1<br>|0.069<br>|0.077<br>|
|(s.e.)|(0.013)|(0.027)|
|_X_2<br>|0.074<br>|0.075<br>|
|(s.e.)|(0.0027)|(0.0027)|
|_TX_2|-0.024|-0.026|
|(s.e.)<br>|(0.0068)|(0.0069)|
|_X_<sup>2</sup><br>1||0.024|
|(s.e.)||(0.0035)|
|_TX_<sup>2</sup><br>1||-0.045|
|(s.e.)||(0.0089)|
|Constant|0.3|0.32|
|(s.e.)|(0.0059)|(0.0085)|
|Socio-economic controls|No|No|
|Regional dummies|549|549|
|Standard errors in parent<br>|heses. Clu<br>|stering applied<br>|
|to all models at the level|of (550) sa|mpling strata.|

Table 9: Estimation of BFP treatment effect by fuzzy Regression Discontinuity Design, first stage 1.

The summary of results of the basic model are summarized in Table 10.<sup>82</sup><sup>_,_83</sup> We notice a significant and pronounced increase in the expenditure share on food (10 percentage points) and also to a lesser extent on education (0.9 percentage points) and school materials (0.7 percentage points). We see a significant decrease in both the medication and total health expenditure shares by about 3 percentage points each.

In light of the previously presented Engel curves, it appears that the CCT did have an impact on the behavior of the households which goes beyond the pure cash transfer. For food, we find a stark increase in the expenditure share by 10 percentage points while the slope of the Engel curve around the threshold is negative. This finding is in line with evidence from

> 82Detailed results for the linear model are reported in Tables 27, 28 and 29.

> 83In the discussion of the results, we will abstract from the compliance with the conditionalities and focus on the impacts of the program on food, health and education expenditures. Because we do not have a direct measure of the compliance of the households with the conditionalities, we are forced to trust that households widely comply. Previous impact evaluations of Bolsa Fam ́ılia found that overall compliances is very high. Soares [2012] shows that only few households were removed from the program because of not complying with the conditionality. From 2006-2008, 2,092,394 households received a warning message but only 93,231 (corresponding to 4.5% of those who had received a warning letter) had to be excluded from the program due to repeated non-compliance. The high rate of compliance seems in line with observations from the POF data: 95 percent of the children aged 6 to 17 in households which are subject to Bolsa Fam ́ılia are enrolled in school. Although actual school attendance can be expected to be lower, this indication together with the monitoring system described in section 8.1 and the previous findings of the literature do not give us reason to suspect that households disregard the conditionalities on a larger scale.

43
- 7 -

# **III. DATA**

Our empirical data are collected from over 150 rural road contracts in 19 districts of Nepal where the World Bank has been assisting the Rural Access Improvement and Decentralization Project (RAIDP).<sup>2</sup> They are located mostly in the Tarai area (Figure 2). In each district, on average 8 road contracts were reviewed―half from World Bank-financed projects and half from government-owned projects. In Nepal the rural road projects are basically implemented at the local level. With the assistance of the Department of Local Infrastructure and Agricultural Roads (DOLIDAR), the District Development Committees (DDCs) are designing, procuring and managing rural road civil works and services.

Nepal is one of the least developed countries. According to the 2011 census, about 26.6 million people live in the country. About 34 percent still live on less than $1.25 a day at 2008 international prices. Gross domestic product (GDP) per capita was only $430 in 2009. The country has some 17,000 km of road network, which is among the lowest road densities in the world. The vast majority of rural residents have to spend more than 30 minutes to access paved roads. About one-third do not have any paved road within more than 3 hours in Nepal.

The sample contracts are relatively small. In Nepal the rural road standards are not particularly high. The average unit cost of rural road upgrading works is about NRs1.6 million or $23,000 per km.<sup>3</sup> The “size of contracts” also seems fairly small at 7km on average but ranges from less than 1 km to more than 30 km (Figure 3). Depending on size, the administrative efficiency of procurers or DDCs contracting out works may differ. In some cases, it takes more than 6 months (Figure 4). The contract performance also varies across contracts. Most of the contracts did not incur any cost overruns, but some did (Figure 5). The average cost overrun rate is about 4 percent with some cost overruns offset by cost underruns.

> 2 One of the 20 districts assisted by the RAIDP has not yet had road work contracts that can be evaluated at the time of our data collection.

> 3 The road upgrading unit cost is estimated at $360,000 in Africa (Alexeeva _et al_ ., 2008). Foster and BricenoGarmendia (2010) also estimates the road rehabilitation cost at $200,000 to $500,000 per km, depending on country and the scale of roads.
# CONTENTS

|Preface .........................................<br>v|
|---|
|Acknovwledgements<br>................................<br>vii|
|Introduction .....................................<br>ix|
|Projection Results ............<br>....................<br>ix|
|Data and Methods ............<br>....................<br>x<br>|
|Total population and age-sex composition .....<br>.........<br>x|
|Mortality level and trend ........<br>.................<br>x|
|<br>Fertility level and trend .........................<br>xiii|
|Migration level and trend .......................<br>xviii|
|Summary of Results ..........<br>....................<br>mxx|
|World and continents ............................<br>xx|
|Geographic regions and. income groups ..............|
|World Bank regions ........<br>...................<br>xS|
|Some country results .......<br>..................<br>xovi|
|References .<br>..................................<br>XXax|
|Appendix: Sources of Population Data .....<br>............<br>Xl|
|Tables<br>Iii|
|.. .<br>.....................................<br>|
|Detailed Population Projections ......................<br>1|
|World, Geographic Regions, and Income Groups ....<br>1....|
|World........................................2|
|Less developed countries .4|
|<br>More developed countries.<br>6|
|Geographic Regions|
|Africa<br>.8|
|East Africa .10|
|West Africa .12|
|North Africa .14|
|Ame.ca .16<br>|
|Latin America and the Caribbean .18|
|Northern America .20|
|Asia .22|
|East and Southeast Asia .24|
|South Asia .26|
|Southwest Asia.<br>28|
|Europe and U.S.S.R .30|
|<br>Oceania..............<br>32|
|Income Groups|
|<br>Low-income economies ...<br>34|
|Lowe--middle-income economies .36|
|Upper-middle-income economies ...................<br>38|
|High-income economies .........................<br>40|
|<br>Nonreporting nonmember economies .42|
45 Taylor, S. D., 2012. _Globalization and the Cultures of Business in Africa: From Patrimonialism to Profit, Bloomington_ , IN, Indiana University Press.

**_—_** UNIDO, 2009. _Industrial Development Report 2009 Breaking In and Moving Up: New Industrial Challenges for the Bottom Billion and the Middle-Income Countries_ , New York.

World Bank, 2012. _World Development Report 2013: Jobs_ , Washington, D.C.

World Bank, 2008. _World Development Report 2009: Reshaping Economic Geography_ , Washington, D.C.

World Economic Forum, 2012. _The Future of Manufacturing: Opportunities to Drive Economic Growth_ , Cologny/Geneva.
The comparison of the nominal mean household consumption values between HBS and NPS shows that consumption levels are higher in the NPS than in the HBS, particularly for the most recent rounds HBS 2011/12 and NPS 2012/13. However, when we look at the distribution by quintiles, we can see that NPS consumption levels are higher than HBS ones only for the better‐off groups, especially for the richest 20 percent where the difference exceeds 30 percent, while the consumption levels of the poorest 20 percent are equivalent between the two surveys. The same pattern is observed when comparing real household consumption values (adjusted by the spatial and seasonal deflator). The higher consumption values in the NPS should not be expected given that HBS probes for a larger number of consumption items and includes more commodities (such as clothing, restaurants, etc.). As most of the difference is coming from the significantly higher consumption level of the richest quintiles, we would expect either higher underreporting in the HBS or significant differences in the sampling between the two surveys. This point will be discussed below.

When adjusting for inter‐year price variations using the survey price deflators, mean household consumption levels appear to be higher in the HBS 2011/12 than in NPS 2012/13. The difference seems to be due to much lower consumption levels of the poorest quintiles in the NPS than in the HBS. While there is almost no difference between HBS and NPS consumption levels of the richest population groups, the difference seems to be very important for the poorest groups attaining around 40 percent. Also, HBS shows an increase in mean household consumption levels over time, mainly driven by an increase of the consumption of the poorest groups, while NPS shows a decline in mean household consumption levels over time mainly driven by a reduction of the consumption of the poorest quintiles.

As these figures are difficult to compare due to the differences in the inter‐year price deflators and base year, we also use the CPI to adjust for inter‐year price variations and take HBS 2011/12 as base year for all HBS and NPS rounds. This reduces the discrepancy between HBS and NPS mean household consumption levels and shows a similar upward trend in mean household consumption over time for both surveys. While we continue to observe a larger increase in mean household consumption in the HBS than in the NPS and a larger increase in consumption levels of the better off in the NPS compared to HBS, both surveys now display improvements over time of mean household welfare, particularly for the better‐off groups.

The main differences that stand out can be summarized as follows:

- i) There is a decline in aggregate consumption as well as in food consumption between NPS 2008/09 and NPS 2010/11, no matter the inter‐year price deflator used. This can be

25
Leslie, H. M., X. Basurto, M. Nenadovic, L. Sievanen, K. C. Cavanaugh, J. J. Cota-Nieto, B. E. Erisman, et al. 2015. “Operationalizing the Social-Ecological Systems Framework to Assess Sustainability.” _PNAS_ 112 (19), 5979–84.

- Lester, S. E., B. S. Halpern, K. Grorud-Colvert, J. Lubchenco, B. I. Ruttenberg, S. D. Gaines, S. Airamé, and R. R. Warner. 2009. “Biological Effects within No-Take Marine Reserves: A Global Synthesis.” _Marine Ecology Progress Series_ 384, 33–46.

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- Mann, K., and J. Lazier. 2005. _Dynamics of Marine Ecosystems: Biological-Physical Interactions in the Ocean_ s, Wiley-Blackwell.

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- McGuire, T. R. 1983. “The Political Economy of Shrimping in the Gulf of California.” _Human Organization_ 42 (2), 132–45.

- OECD 2006. _Agricultural and Fisheries Policies in Mexico: Recent Achievements, Continuing the Reform Agenda_ , Organisation for Economic Cooperation and Development Publishing.

- Ostrom, E. 1990. _Governing the Commons: The Evolution of Institutions for Collective Action_ , Cambridge University Press: Cambridge (UK).

- Ovando, D. A., R. T. Deacon, S. E. Lester, C. Costello, T. Van Leuvan, K. McIlwain, C. Kent Strauss, et al. 2013. “Conservation Incentives and Collective Choices in Cooperative Fisheries.” _Marine Policy_ 37, 132–40.

- Parma, A. M., and R. B. Deriso. 1990. “Experimental Harvesting of Cyclic Stocks in the Face of Alternative Recruitment Hypotheses.” _Canadian Journal of Fisheries and Aquatic Sciences_ 47 (3), 595–610.

- Pauly, D. 1997. Small-Scale Fisheries in the Tropics: Marginality, Marginalization, and Some Implications for Fisheries Management, _in_ E. Pikitch, D. Hupert and M. Sissenwine, eds, “Global trends: Fisheries Management.” American Fisheries Society Symposium 20: Bethesda, Maryland, 40–49.

- Petterson, J. S. 1980. “Fishing Cooperatives and Political Power: A Mexican Example.” _Anthropological Quarterly_ 64–74.

- Polis, G., M. Rose, F. Sanchez Pinero, P. Stapp, and W. Anderson,. 2002. “Island Food Webs,” _in A New Island Biogeography of the Sea of Cortes_ . New York: Oxford UP, 280–362.

- Reddy, S. M., A. Wentz, O. Aburto-Oropeza, M. Maxey, S. Nagavarapu, and H. M. Leslie. 2013. “Evidence of Market-Driven Size-Selective Fishing and the Mediating Effects of Biological and Institutional Factors.” _Ecological Applications_ 23 (4), 726–41.

- Reed, W. J. 1975. “A Stochastic Model for the Economic Management of a Renewable Animal Resource.” _Mathematical Biosciences_ 22, 313–37.

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3
Table of Contents

|1. INTRODUCTION .<br>...........................................|1|
|---|---|
|2. HISTORY OF THE ZIMBABWE FAMILY PLANNING PROGRAMME ....<br>2.1. LAWS ON FAMILY PLANNING .........................|......<br>1<br>3|
|2.2. EVOLUTION OF A POPULATION POLICY ..................|4|
|3. ACCESS TO FAMILY PLANNING SERVICES .........................|_5_|
|3.1. PUBLIC SECTOR SOURCES OF FAMILY PLANNING ....|.......<br>6|
|3.1.1. Zimbabwe National Family Planning Council ............|6|
|3.1.2. Ministry of Health and Municipal Clinics ...............|9|
|3.2. PRIVATE SECTOR ..................................|10|
|3.3. METHODS PROMOTED ..............................|10|
|3.4. ELIGIBIlITY FOR SERVICES ...........................|11|
|4. FUTURE DIRECTIONS ........................................|11|
|4.1. RESOURCE CONSTRAINTS ............................|12|
|5. CONCLUSION ..............................................|12|
|6. BIBLIOGRAPHY .<br>...........................................|13|
where _p0_ – proportionof non-smokers _p1_ – share of smokers at the moment RR – the relative risk of diseases in smokers in relation to non-smokers.

The following algorithm was used to calculate the **cost of inpatient treatment in 24-hours** and **day care hospitals** for diseases caused by smoking:

1. define average base rate per inpatient treatment case in 24-hours and day care hospitals (averaging rates specified in regional tariff agreements);

2. extract information from statistical survey forms on the total number of cases of treatment for ICD10 codes in question; 3. defining the values of RR based on available open sources 4. relate the disease classes with PAR indicators- the shares of diseases associated with smoking by disease (attributable risk of smoking); 5. determine the number of treatment cases triggered by smoking (the number of treatment cases under ICD-10 codes, multiplied by the relative attributable risk of smoking); 6. on diseases of the circulatory system, respiratory system, and malignant neoplasms: determination of the clinical profile group on the basis of decoding the DRGs provided in hospital and day hospitals, and the determination of the DRG weight on the profile of diseases; 7. for diabetes – determination of DRGs, which include the treatment of diabetes and the calculation of the average DRG weight for the disease; 8. calculate price per treatment case based on profile group (average base rate multiplied by average disease-profile weight); 9. determine the cost of treatment for diseases associated with tobacco smoking as the number of treated cases caused by tobacco smoking, multiplied by price per case under respective DRG profile group and multiplied by PAR.

Overall, the cost is determined by formula: where _Cost_ – the cost of inpatient treatment for diseases associated with smoking; NзаболМКБ – number of registered diseases according to the ICD-10 code; Кз<sup>КПГ</sup> – average weight of DRG profile groups;

- _БС_ – average DRG base rate for inpatient care;

- PAR – population attributable risk for ICD-10 code (the proportion of diseases associated with smoking).

For the calculation of health care losses from tuberculosis caused by tobacco, the calculation was made according to the following formula: where Cost TB – the costs for the treatment of diseases associated with tobacco; NзаболМКБ – number of registered diseases according to the ICD-10 code; NFZ – the standard of financial expenses of treatment of patients with tuberculosis in the conditions of 24-hours and day hospitals; 22
East Asian NIEs such as Korea and Singapore have generated patents per capita much higher than predicted by their income levels alone, in much the same way as their R&D. Interestingly, in recent years patenting in Hong Kong has also exceeded predicted levels, even though its R&D is much lower than predicted. Exhibit 10 shows that Malaysia and China have generally innovated at around the levels predicted by income, although China’s patenting has accelerated to levels higher than predicted by income in recent years. Indonesia, Philippines and Thailand, on the other hand, have performed below the predicted level, in line with their R&D underperformance.

<!-- Start of picture text --> Exhibit 9 Exhibit 10<br>Patents per 100,000 Population and Per Capita Income Patents per 100,000 Population and Per Capita Income<br>40 0.2<br>35 United States 0.18 Regression relation between Korea<br>patents and per capita income<br>0.16<br>30<br>Japan<br>0.14<br>25<br>0.12<br>China<br>20 0.1 Malaysia<br>Hong Kong Regression<br>15 relation between 0.08<br>Korea Singapore patents and per<br>0.06<br>10 capita income Philippines<br>0.04 Indonesia<br>5<br>0.02<br>0<br>0<br>8.9 9.4 9.9 10.4 10.9 6.4 6.9 7.4 7.9 8.4 8.9<br>-5 -0.02 Thailand<br>Log(GDP per capita) Log(GDP per capita)<br>Source: USPTO and World Bank Development Indicators.<br>P a te n ts /1 0 0 ,0 0 0 p o p u la tio n P a te n ts /1 0 0 ,0 0 0 p o p u la tio n<br><!-- End of picture text --> What factors determine the flow of innovation outputs in an economy? The large empirical literature estimating “knowledge production functions” in developed economies typically finds a strongly significant relationship between innovation inputs like R&D expenditure and innovation outputs such as patent counts.<sup>28</sup> Bottazi and Peri (2005) study the short and long run dynamics of knowledge production in OECD countries by relating the flow of patents to domestic R&D flows and to the existing stocks of domestic and international knowledge (measured respectively by the stocks of patents accumulated in the country and in the rest of the OECD). The idea is that innovation depends not only on the current resources devoted to R&D in the country but also on knowledge spillovers from the entire stock of earlier knowledge accumulated in the world as a whole. Bottazi and Peri find long run elasticities of patenting on R&D and the stock of foreign knowledge of around 0.8 and 0.6 respectively. Thus,

> 28 For example see Hausman, Hall and Griliches (1984, 1986), Griliches (1990), Blundell, Griffith and Van Reenen (1995),  Jaffe and Trajtenberg (2002).

24