rafmacalaba/datause-encoder-data huggingface dataset:
11 middle-income and high-income countries. At the tertiary level, the average rate of return in low-income countries is double that of a high-income country (Psacharopoulos and Patrinos 2004).

Compared to Korea and Singapore, the rates of return to each year of additional schooling in Thailand are more than double and closer to quadruple, reflecting the scarcity of skilled workers. Returns to education are not as high in Malaysia—closer to those in the United States and the United Kingdom, but still exceeding other high-income countries in East Asia, suggesting a similar story (Figure 6).

But the returns to schooling, despite tremendous growth in the numbers of students and graduates over time, have not declined in Thailand. They have increased. In 1970, the rate of return to secondary schooling was 11 percent (Blaug 1976). Between 1985 and 1995, the average private rate of return to a year of education was 10-11 percent (Hawley 2004). In 2002 it was as high as 15 percent. By 2009 it had declined, but only to 13.5 percent (Garcia and Patrinos 2012). In Malaysia, though the information is sparse and methodologies differ, Hoerr (1977) estimated for 1968 a rate of return to primary schooling of 13 percent, 18 percent for upper secondary, and higher education of 16 percent. And even though Gallup (1997) estimated an overall rate of return of just 7.6 percent for 1979, Chapman and Harding (1985) found an average return to education of 9.4 percent in 1977. But in 2002 and 2004, the average rate of return was between 10.0 and 10.5 percent (Ismail 2007). In Korea, by contrast, the return to schooling was above 15 percent in the early to mid-1980s; it has since steadily declined to about 7 percent (Choi and Jeong 2003). These trends imply that these countries may be lagging in the classic Tinbergen-type “race” between skill-intensive technological change and the ability of learning systems to produce those skills (Acemoglu and Autor 2012).

<!-- Start of picture text --> Figure 6: Private Returns to Schooling<br>16 15.2<br>12.6<br>12<br>10.4<br>10<br>9<br>7.3<br>8<br>4.1<br>4<br>0<br>Singapore Korea 2002 UK 2002 US 1995 Malaysia Philippines Thailand<br>1998 2004 2000 2002<br>Source: Psacharopoulos and Patrinos 2004<br><!-- End of picture text -->
the level of redistribution achieved by fiscal policy, suggesting there is significant room for the country to do more. Second, most of the inequality reduction achieved by China is through in-kind education and health benefits and social security contributions which are highly progressive. Yet, the heavy burden of health user fees -relative to households’ disposable income- may discourage families accessing health care services when needed and school related user fees suggest the high cost of raising children. Cash transfers are equalizing but their contribution is rather small. The third main finding is that progressive and effective inequality reduction instruments such as personal income taxes remain relatively under-utilized.

The rest of the paper is organized as follows. The next section provides an overview of the fiscal system. This is followed by a description of the data and the methodology, including the introduction of the various income concepts that are critical for the analysis. The subsequent section describes the main results while the final section summarizes and concludes with a discussion of the implications of these findings for policy.

# Overview of China’s Fiscal System in 2018

In this section, we provide an overview of China’s fiscal system. As discussed in Lustig (2018), the redistributive power of a fiscal system depends on two crucial factors: size and progressivity. We present the size of China’s total revenues by source, as well as China’s total spending by category.

## Total Revenue

Total fiscal revenue in China was 26,197 billion RMB in 2018 (3,950 billion in 2018 USD), representing 29.1 percent of total GDP. Table 1 shows its constituent parts: tax revenues, contributions to social insurance and nontax revenue. Tax revenues account for almost 60 percent of total revenue. Within taxes, the four largest items are value-added tax (VAT), corporate income tax, personal income tax and consumption tax. Contributions to social insurance amount to 30 percent of total revenue, the majority of which are collected through the employee’s social insurance system and only 4 percent is collected through the resident’s system. The employee’s system is legally required for any type of formal employment while the resident’s system covers those not covered by the employee’s system.<sup>4</sup>

> 4 Non-tax revenue accounts for the remaining 10% of total revenue, and includes special program receipts, charge of administrative and institutional units, penalty receipts, operation income from government capital, income from use of state-owned assets/resources and other non-tax revenue. Source: Statistical Yearbook of China 2019, National Bureau of Statistics, 2019.

4
the adoption of the treaty in 1989, become closely associated with UNICEF’s efforts to reduce infant and child mortality. Throughout the 1980s, UNICEF had been supporting the efforts of countries to encourage breastfeeding, widen access to ante- and post-natal care, conduct growth monitoring, use oral rehydration to reduce morbidity and mortality associated with childhood diarrhea, encourage child spacing, and make the basic six vaccines for childhood diseases (diphtheria, pertussis, tetanus, measles, polio, and tuberculosis) more widely available, in conjunction with WHO’s Expanded Program on Immunization (EPI). UNICEF’s Universal Childhood Immunization campaign (UCI) ran from 1985-1990, and was a crucial element of the prevailing approach to child survival. In ratifying the treaty, state parties indicated their commitment to, among other elements, the prevailing and widely accepted approach to child health.

Ratification of the treaty was rapid: by September 1990, some twenty states had ratified the convention; by December 1992, 127 countries had ratified it, and 27 others had signed it, making it one of the most quickly adopted instruments in the “international bill of rights.” (LeBlanc 1995 ) Figure 1 below illustrates this phenomenon: by 1995 ratification of the treaty was nearly universal.

# **Data and models**

Data on immunization coverage from 1988-2007 were obtained from WHO and UNICEF. These measure the proportion of children who have received the DTP3 and measles vaccine at one year of age and are obtained from either service delivery records or, where available, from cluster surveys carried out under the auspices of the WHO’s Expanded Programme on Immunization. Although several studies have voiced concern over the disparity between coverage rates reported from service delivery records and “actual” coverage rates measured by sample surveys (Boerma et al. 1990 , UNICEF 1996 , WHO 1999 ), one study compared these data with household-level DHS data from 82 countries and found that, in general, using reported rates as a dependent variable is reasonable, and did not exhibit any significant evidence for systematic bias. Correlations between measles and DTP3 rates at the national level are quite high (Gauri and Khaleghian 2002).

8
operates he gets the long-term benefits of the carbon stocks delivered net of the payments he makes. If he deviates he gets the benefits of the carbon storage minus what he paid upfront. Then in all future periods, he guarantees himself the benefits of the alternative options for carbon credits. Since both parties can deviate from the contract, the contingent payment must be sufficient to ensure a self-enforcing contract. It follows that the compensation scheme is bounded by the future gains of the relationship.

The buyer’s optimization program is given by (7) The seller’s IRC can be rearranged as equality (8) and because a profit maximizing buyer pays only as much as is needed to induce the seller to participate, then the _IRCs_<sup>_m_</sup> binds: and expression (5) can be restated as, which gives the lower bound on the fixed payment, _p_ , for inducing long-term seller cooperation. The presence of the performance payment allows the buyer to offer a lower fixed payment. By substituting (8) in (9), the optimal distribution of the total compensation among the fixed payment and the performance bonus is established. The optimal stationary REDD contract is defined in Proposition (1).

**Proposition 1.** _If parties are purely profit maximizing agents that trade repeatedly and contract enforcement is imperfect, assuming δ high enough, an optimal stationary REDD contract ⟨p_<sup>_∗_</sup> _, b_<sup>_∗_</sup> ( _q_<sup>_∗_</sup> ) _⟩ implements forest conservation,_ _<u>q,</u> by satisfying IRCs_<sup>_m,IRC_</sup> _b_<sup>_m,DICC_</sup> _s_<sup>_m_</sup>

11
22

# _Improving Prescription_

Major savings can be made by improving prescription practices, but progress in this area is slow. Prcribers have strong opinions about drugs which are influenced by their medial training and colleagues, patient expetations, and not lat, the efforts of the pharmcutial industry to encourage them to prescribe newer, more expensive drugs. There are, however, indications that if the prescribers are fully involved in the selection and quantification process, and if they have experienced a situation of helpkuncs due to lack of even basic eenXtl drugs, they are often wiling to change their prescribing behavior.

Ibo potential pitsfrom improved prescribing are enormou. Much of what **ha** been said about secion above appLies to prescription; whereas selection ki done DUectively, by a group of prescnbers who have both the helth of patients and the coUactive health of the community in mind, prescription i selecion of drugs done on an idividual basis by a single prescriber. Yet too often the prescriber fails to take accunt of the ecnomic circumstances of his patient and of the community.

Reducing overprescription may be a major field for potential savings In Mall, for example, a survey found that the average prescription conaia 10 dru; it may include duplication in the form of the same antibiotic under different brand names In most cams the patient in quetion really needs only one or two drugs, and if the average price of al the druo is roughly equivalent (which is not usually the case, but for the **sake** of illustration we will assume it to be so), then a 10.drug prescription represents a potential waste of 80-90%. In fact, however, :be patient seldom recves all 10 drugs since neither the health services nor his own financial situation permit this extravagance.

Experience shows that a realistic figure for savings from improved prescription may be on the order of 60-75%. A study in Ghana in 1980 examined prescriptions at several health centers and found that 96% of visits were treated with at kast one injection, and that an average of 3.9 items was gien on each prescription. When actual prescriptions from a number of health centers were compared with best practice' for the condition as established by the health authorities, the drugs cost would have fallen by 70%. (Barnett, Creese, and Ayivor, 1980) Alternatively, approximately 3 times as many patients could have been treated for the same budget Similarly, in Nigeria, of 299 cases of diarrhea, poor prescription and over of antibiodta resulted in a wastage of N2526, or 97% of the amount spent on treating the cases Similarly, prescription of a brand name antibtic which was three times more expensive than a generic which was also availabk, resulted in an estimated over-cxpenditure of 78%. Despite the fact that 7 of the 11 doctors at the hospital knew that the brand name wa more expenshe, they prescribed it in 77% of cases Curiously, the
Figure A5: Specification Charts for Deviation in Total Rainfall <!-- Start of picture text --> Ethiopia Malawi<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.0015 .002<br>.001 .001<br>.00050-.0005 0-.001<br>* Weather Product * -.001 * Weather Product * -.002<br>TAMSAT TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Niger Nigeria<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.005 .0015<br>.001<br>0 .0005<br>0<br>-.005 -.0005<br>* Weather Product TAMSAT* -.01 * Weather Product TAMSAT* -.001<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Tanzania Uganda<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.001 .003<br>.002<br>.0005 .001<br>0 0<br>-.001<br>* Weather Product * -.0005 * Weather Product * -.002<br>TAMSAT TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br><!-- End of picture text --> _Note_ : The figure presents specification curves, where each panel represents a different country, with three different models presented within each panel. Each panel includes 36 regressions, where each column represents a single regression. Significant and non-significant coefficients are designated at the top of the figure. For each Earth observation product, we also designate the significance and sign of the coefficient with color: red represents coefficients which are negative and significant; white represents insignificant coefficients, regardless of sign; and blue represents coefficients which are positive and significant.

53
<!-- Start of picture text --> Waste; a) Industry_ b) Forestry; » c) 3004 Agriculture°° . d)<br>Pee 300 ‘ 6004 2 5<br>Sk. . * x 200 x .<br>Bs 10 : 200 : 400 : a<br>£57 100 200 :<br>2025 2050 2075 2100<br>Year<br>Transport Energy Buildings<br>3004. e) . f) 3004 8)<br>* 1000 : * ____ Policy suite 1:<br>c x . First-best<br>aT * 800 : . Policy suite 2:<br>€ 5 200 . : 200 * —-- Energy decarbonization<br>2 i 600 : : effort relaxed<br>g 5 : ‘ : Policy suite 3:<br>BE 5 400 : : —— Energy decarbonization<br>s = 100 : 100 : effort delayed<br>me “ 200 : a _.... Policy suite 4:<br>“= NS? . Economy-wide delay<br>2025 2050 2075 2100 2025 2050 2075 2100 2025 2050 2075 2100<br>Year ‘Year Year<br><!-- End of picture text -->
WPS4213

# **Remittances and the real exchange rate**

## **Humberto Lopez, Luis Molina and Maurizio Bussolo**<sup>*****</sup>

## **Abstract**

Existing empirical evidence indicates that remittances have a positive impact on a good number of development indicators of recipient countries. Yet, when flows are too large relative to the size of the recipient economies, as those observed in a number of Latin American countries, they may also bring a number of undesired problems. Among those probably the most feared in this context is the possibility of a real exchange rate appreciation and a loss of competitiveness in the tradable sector. This paper explores the empirical evidence regarding the impact of remittances on the real exchange rate. Our findings suggest that indeed remittances appear to lead to a significant real exchange rate appreciation. The paper also explores policy options that may somewhat offset the observed effect.

## World Bank Policy Research Working Paper 4213, April 2007

_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._

> * World Bank, Bank of Spain and World Bank respectively.

1
_VCR_ (or DVD), suggest that when households’ possess these home electronic devices, the social skills of the children increase by 0.31and 0.22 standard deviations, respectively. This table also provides information as to how important household income is as a determinant of the development of infants’ social skills. Due to the initial differences in the control and treatment group, we use differences in differences estimator with panel data. Employing this method increases precision in assessing the treatment’s net effect.

# _Differences in Differences using panel data_

# <u>Handwashing behavior</u>

According to Table 6, the treatment group had lower hand washing habits than the control group at the beginning of the study. This means people did not wash their hands after using the toilet. The inclusion of controls should increase our precision, as they included household basic characteristics and socio-economic characteristics, including income, just as in previous models. It is observed, however, that there is not a significant variation of the coefficient of interest. The table also shows that the addition of household assets does not improve the hand washing practices of the treatment group. Both the control and treatment group decreased their handwashing behavior after the treatment. It is worth noting that TV and VCR appear to have positive impacts on handwashing behavior in the presence of the program. This suggests that information assets and wealth may drive hygienic behavior.

# <u>Panel data graphical analysis</u>

Figure 4 shows the differences in _Communication Skills_ between the baseline and follow up cohorts for those that reported handwashing in both baseline and follow up surveys. The differences in cohorts appear to be more pronounced at early stages of infancy, favoring the treatment group. It can also be observed that this difference begins to decrease and reverse favoring the control group steadily from ages 28 months old onwards.

21
_COTE D’IVOIRE: Volatility, Shocks and Growth_

## **Figure 17:Cycles in De-Trended Output per Worker, Cocoa and Coffee Prices**

# **De-Trended Per Worker Output, Cocoa and Coffee Prices Cycles in Deviations from Trend**

<!-- Start of picture text --> 0.8<br>0.6<br>0.4<br>0.2<br>0<br>-0.2<br>-0.4<br>-0.6<br>Output/Worker Cocoa Price Coffee Price<br>Log of Ratio of Actual to Trend<br>Year 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001<br><!-- End of picture text -->
Policy Research Working Paper 5327

# **Abstract**

The Delivering Service Indicators seek to provide a set of indices for benchmarking service delivery performance in education and health in Africa in order to track progress in and across countries over time. It seeks to enhance effective and active monitoring of service delivery systems and to become an instrument of public accountability and good governance in Africa. The main perspective adopted by the Delivering Service Indicators index is one of citizens accessing services and facing potential shortcomings in those services available to them. The index is thus presented as a Service Delivery Report Card on education and health. However, unlike traditional citizen report cards, it assembles objective information from micro level surveys of service delivery units.

This paper—a product of the Poverty Reduction and Economic Management, Africa Region; and Public Sector and Governance, Middle East and North Africa Region—is part of a larger effort in the departments to better understand governance issues in service delivery.. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The authors may be contacted at bgauthier@worldbank.org, or wwane@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
# A.2.   Telephone survey sample

A total of 717 households were interviewed in the telephone survey. These were households that had been part of the GHS panel and had been interviewed in wave 3, visit 2.

All of the households had been residing in the same LGA during the time of the GHS survey; thus, no migration occurred among the households selected for the phone survey. Migration in general was low during the other waves: out of 4,916 households in wave 1, visit 1, for the entire country, only 142 households had moved by the end of wave 2, visit 2 (that is, 2.9 percent). The majority of the households that had moved were from the South West zone (Lagos and surrounding states), which is outside of our geographical sample. Within the three zones studied, only 52 households moved between wave 1, visit 1, and wave 2, visit 2. Between wave 2, visit 2, and wave 3, visit 2, another 212 households moved; tracking surveys were administered to these households in lieu of the regular survey procedures (NBS 2014, 2016). Because our sample consists of households that were part of the panel, we do not have any movers in the sample. However, given the low rates of migration in our survey areas, we do not consider this to be a major limitation for the study.

The attrition rates between the GHS waves 1 and 2 and waves 2 and 3 were 5.7 percent and 8.4 percent, respectively. Some attrition between wave 2 and wave 3 resulted from not being able to reach some areas: a total of 14 enumeration areas could not be visited in the states of Borno and Yobe during wave 3, leading to the loss of 139 households from the sample. Furthermore, some households dropped from the sample because they refused to be interviewed, they were untraceable, or all members had died (NBS 2014, 2016). Since we are conducting analysis based on households that were part of the GHS wave 3, our sample does not in and of itself have conflict-induced attrition. However, we remain wary of the fact that this conflict-induced attrition could have biased our sample selection in such a way that we are not capturing the households that were most severely affected by conflict in Borno and Yobe. This attrition would therefore induce our victimization figures to be downward biased.

Table A.3 lists the sample sizes in each wave, showing that a vast majority of the households had been visited in all three waves. In the telephone survey, a total of 1,030 households from the GHS wave 3, visit 2 were attempted to be reached. Most of the nonresponses came from nonfunctioning phone numbers, as only 2.7 percent refused to answer. The survey first attempted to reach only 742 households, of which 529 could be reached and interviewed. In order to increase the sample size to a level that was considered adequate for the survey, an v