rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4: Percentage change in real GDP relative to baseline (2020)**

|**Carbon tax**||**US$1**|**0/tCO2**|||**US$2**|**5/tCO2**|||**US$5**|**0/tCO2**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Biofuels subsidy**|**0%**|**10%**|**25%**|**50%**|**0%**|**10%**|**25%**|**50%**|**0%**|**10%**|**25%**|**50%**|
|**World total**|-0.16|-0.17|-0.18|-0.27|-0.43|-0.44|-0.45|-0.53|-0.85|-0.85|-0.87|-0.95|
|**High-income**|-0.02|-0.02|-0.03|-0.10|-0.07|-0.07|-0.08|-0.15|-0.15|-0.15|-0.16|-0.23|
|**Aus-NZ**|-0.13|-0.13|-0.14|-0.15|-0.30|-0.30|-0.30|-0.31|-0.52|-0.52|-0.52|-0.53|
|**Japan**|-0.01|0.00|0.00|-0.01|-0.02|-0.02|-0.02|-0.02|-0.06|-0.06|-0.06|-0.06|
|**Canada**|-0.08|-0.08|-0.09|-0.11|-0.22|-0.22|-0.23|-0.25|-0.45|-0.46|-0.46|-0.48|
|**United States**|-0.06|-0.06|-0.08|-0.25|-0.15|-0.15|-0.17|-0.34|-0.30|-0.30|-0.32|-0.50|
|**France**|0.03|0.03|0.04|0.06|0.06|0.07|0.07|0.10|0.10|0.10|0.11|0.14|
|**Germany**|0.03|0.03|0.02|0.00|0.07|0.06|0.06|0.03|0.09|0.09|0.08|0.06|
|**Italy**|-0.02|-0.02|-0.03|-0.04|-0.06|-0.06|-0.06|-0.07|-0.13|-0.13|-0.13|-0.15|
|**Spain**|0.01|0.01|0.00|-0.01|0.01|0.01|0.00|-0.02|-0.02|-0.02|-0.02|-0.04|
|**UK**|0.02|0.02|0.02|0.02|0.04|0.04|0.04|0.04|0.05|0.05|0.05|0.05|
|**Rest of EU & EFTA**|0.00|-0.01|-0.01|-0.03|-0.03|-0.03|-0.03|-0.05|-0.10|-0.10|-0.10|-0.12|
|**Middle & Low-income**|-0.41|-0.42|-0.45|-0.56|-1.08|-1.09|-1.12|-1.21|-2.09|-2.11|-2.14|-2.24|
|**China**|-0.84|-0.85|-0.88|-1.02|-2.10|-2.11|-2.14|-2.25|-3.87|-3.88|-3.92|-4.04|
|**Indonesia**|-0.16|-0.16|-0.16|-0.16|-0.45|-0.45|-0.45|-0.45|-0.94|-0.95|-0.95|-0.95|
|**Malaysia**|-0.09|-0.10|-0.14|-0.25|-0.35|-0.37|-0.41|-0.51|-0.89|-0.91|-0.95|-1.06|
|**Thailand**|-0.01|-0.01|-0.01|-0.03|-0.08|-0.09|-0.09|-0.11|-0.30|-0.30|-0.30|-0.32|
|**Rest of EAP**|-0.01|-0.01|-0.01|-0.02|-0.06|-0.06|-0.06|-0.08|-0.21|-0.21|-0.21|-0.24|
|**India**|-0.34|-0.35|-0.38|-0.49|-0.86|-0.87|-0.91|-1.00|-1.65|-1.66|-1.69|-1.79|
|**Rest of SA**|-0.26|-0.26|-0.27|-0.30|-0.64|-0.64|-0.64|-0.67|-1.19|-1.20|-1.20|-1.23|
|**Argentina**|-0.35|-0.35|-0.35|-0.36|-0.91|-0.91|-0.91|-0.89|-1.74|-1.74|-1.75|-1.72|
|**Brazil**|-0.06|-0.13|-0.24|-0.49|-0.16|-0.24|-0.37|-0.59|-0.35|-0.43|-0.56|-0.78|
|**Rest of LAC**|-0.18|-0.20|-0.23|-0.35|-0.50|-0.51|-0.55|-0.66|-0.98|-1.00|-1.03|-1.14|
|**Russia**|-0.64|-0.66|-0.70|-0.84|-1.96|-1.98|-2.03|-2.10|-4.17|-4.20|-4.25|-4.33|
|**Rest of ECA**|-0.27|-0.26|-0.25|-0.24|-0.72|-0.71|-0.70|-0.67|-1.45|-1.44|-1.43|-1.40|
|**MENA**|-0.41|-0.44|-0.49|-0.72|-1.23|-1.27|-1.33|-1.50|-2.57|-2.61|-2.68|-2.86|
|**South Africa**|-0.34|-0.35|-0.38|-0.46|-0.87|-0.89|-0.92|-0.98|-1.59|-1.61|-1.64|-1.71|
|**Rest of SSA**|-0.29|-0.31|-0.34|-0.46|-0.79|-0.81|-0.84|-0.92|-1.52|-1.54|-1.58|-1.67|

When part of the carbon tax revenue is used to subsidize biofuels in each country/region (i.e., carbon tax cum biofuel subsidy case), the global GDP loss is even bigger. This result is nontrivial. This is because subsidizing biofuels, which are relatively expensive as compared to their 23
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WPS4147

# **Trade in Health Services in the ASEAN Region**

_By_ Jutamas Arunanondchai and Carsten Fink<sup>_*_</sup>

## **Abstract**

Promoting quality health services to large population segments is a key ingredient to human and economic development. At its core, healthcare policymaking involves complex trade-offs between promoting equitable and affordable access to a basic set of health services, creating incentives for efficiencies in the healthcare system, and managing constraints in government budgets. International trade in health services influences these trade-offs. It presents opportunities for cost savings and access to better quality care, but it also raises challenges in promoting equitable and affordable access. Drawing on a research project of the ASEAN Economic Forum, this paper offers a discussion of trade policy in health services for the ASEAN region. It reviews the state of healthcare in the region, existing patterns of trade, and remaining barriers to trade. The paper also identifies policy measures that could further harness the benefits from trade in health services and address potential pitfalls that deeper integration may bring about.

## World Bank Policy Research Working Paper 4147, March 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._

* Jutamas Arunanondchai is a Senior Researcher at the Fiscal Policy Research Institute Foundation, Bangkok, Thailand.  Carsten Fink is a Senior Economist at the World Bank Institute.  This paper is based on a research project on trade in health services undertaken by the ASEAN Economic Forum research network and supported by the World Bank Institute.  Comments by Rudolf Adlung, Nick Drager and Karin Timmermans are gratefully acknowledged.

1
# **3.1 Data Description**

The study relies on two main sources of data: _(i)_ pooled data from the DHS datasets from almost all African countries, which provides data on individual labor outcomes as well as individual and household characteristics; and _(ii)_ the SPEI database with information on monthly climatic water balance at the global scale with a 0.5 degrees spatial resolution. As mentioned, the SPEI departs from other climate databases in that it combines data on temperature with data on precipitation to measure aridity and monitor soil moisture and drought conditions.

## **Demographic and Health Surveys**

The DHS provide information on respondents’ employment status, our main outcome of interest. These are repeated cross-section surveys collected in most developing and middle-income countries since the mid-1980s. The surveys are representative at the national and sub-national level and are comparable across countries and years for most variables. The DHS Program also provides georeferenced data (longitude and latitude) in selected surveys, making it possible to combine these surveys with a wide range of spatial data from other sources. For this analysis, the paper uses data on over half a million individuals from 16 countries across Sub-Saharan Africa over the period from 2005 to 2018. The surveys use a two-stage sampling technique, selecting clusters (or census enumeration zones) at the first stage and households at the second stage. The data also provide information on demographic and socioeconomic status (e.g., age, education, occupation, etc.), which is collected for all household members. In addition, selected sub-samples of respondents in the DHS data provide information on a range of other significant variables of interest, including health status. Lastly, the DHS data are collected over a year and the surveys report the month of the interview, allowing us to match with the corresponding climate variables for the given month. Since our main outcome of interest is the employment status at the time of the interview, this allows us to more accurately estimate the impacts of contemporaneous effect of variation in climatic water balance on employment.

## **Global Standardised Precipitation-Evapotranspiration Index**

The paper uses the SPEI to measure water availability in a given location. The index was derived using the CRUTS3.0 dataset at a spatial resolution of 0.5 degree. The SPEI is a widely used drought index which captures the effect of both rainfall and temperature on the climatic water balance. Note that the SPEI is a normalized variable, expressed in units of standard deviation from the average climate conditions experienced within a given grid cell over the 7
The main objectives for introducing BOOT contracts in wastewater treatment are to make the operation and management of the plant more efficient, to attract new ideas and technologies, which could lower costs, and to finance the investment without public guarantees in any form. A detailed description of the BOOT model is given by Idelovitch and Ringskog (1997), along with ways of sharing risks between the public and private partners. In general, risks should be shared between the private and public sectors following the principle that whoever can control or manage the risk best should assume it and receive adequate compensation for doing so. The private sector is better able to assume the commercial risk, and also the technical risk deriving from design shortcomings.

Many examples now exist of DBO and BOOT/BOO agreements for wastewater treatment and use for irrigation. Table 5.2 provides information on seven such examples, including investment costs, form of private sector participation, and sources of financing. Specific details of how the wastewater treatment DBO and BOOT schemes were structured in Mendoza (Argentina), Santiago (Chile), Monterrey (Mexico), Amman and Zarqua (Jordan), and Tehran (Iran) are described in Box 5.9, along with descriptions of how treated wastewater is utilized for irrigation in Monterrey and Tehran. This experience with private sector participation cuts across lower-middle-income countries (Iran and Jordan), upper-middle-income countries (Argentina, Chile, and Mexico), and high-income countries (Kuwait and Qatar). While most of the plants described include conventional technologies such as activated sludge, and tertiary treatment options, waste stabilization ponds have also been utilized (Mendoza).

These examples show that it is possible through private sector participation to introduce wastewater treatment in countries with little prior experience (such as Argentina, Iran, and Jordan), to mobilize private sector investment, and to design and operate the treatment plants so as to meet water quality standards for wastewater irrigation.

The importance of the private sector participation in wastewater treatment has also been confirmed in a low-income country. A recent survey of about 70 mostly decentralized wastewater treatment plants in Ghana highlighted the relative success of plants built and operated by the private sector, while plants operated by or handed over to the public sector failed (Murray and Drechsel, 2010).

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## _Table 2:_ Main questions and approaches for analysis.

|**Issue**|**Approach**|**Estimate of interest**|**Proxy**|**Availability**|
|---|---|---|---|---|
||Q1: Measuring<br>different types of<br>losses and building a<br>compound estimate|Losses due to reductions<br>in planted area ("planted<br>area losses")<br>Losses due to<br>productivity decline<br>among harvested crops<br>("plant productivity<br>losses")|Approximately what percent of [PLOT]<br>planted with [CROP] was harvested?<br>What is the percentage of damage on<br>[CROP]?|Malawi: IHPS19<br>Ethiopia: ESS13,<br>ESS15, ESS18<br>Ethiopia: ESS13,<br>ESS15, ESS18|
|**Quantification**|Q2: Using losses<br>assessed by an 'expert'|Consistency of self-<br>reports and expert<br>assessments|What percentage of the potential maize<br>harvest was lost due to damage on [PLOT]?|Uganda: MAPS|
||Q3: Exploiting the cross<br>sectional or time<br>dimension of survey<br>data|Attainable yield based<br>on past yield or<br>unaffected districts|Past yield realizations or yields from similar<br>but unaffected plots||
||Q4: Relying on farmers'<br>self-reported harvest<br>expectations|Attainable yield based<br>on farmers’ post-<br>planting expectations|How much of [CROP] do you expect to<br>harvest during the main rainy season?|Malawi: IHPS10,<br>IHPS13, IHPS16,<br>IHPS19<br>Ethiopia: ESS15,<br>ESS18|
|**Attribution**|Q5: Relying on farmers’<br>self reported shock<br>identification|Consistency of self-<br>reported shock exposure|Plot-level reports of losses cause;<br>Household-level shock reports; Community<br>reports|Malawi: IHPS10,<br>IHPS13, IHPS16,<br>IHPS19<br>Ethiopia: ESS13,<br>ESS15, ESS18|
||Q6: Index-based shock<br>identification|Consistency of self-<br>reports with geospatial<br>rainfall data|Geospatial/sensor data on rainfall intensity|Malawi: IHPS10,<br>IHPS13, IHPS16,<br>IHPS19<br>Ethiopia: ESS13,<br>ESS15, ESS18|

# **4.1. Quantification: How high are losses?**

## <u>Question 1: Can we separately measure planted area and plant productivity losses in survey data and form a robust, compound loss estimate?</u>

In the stylized framework set out in Section 3, losses can manifest in two complementary ways capturing different types of losses. The first are losses due to reductions in the size of the harvested 12
declaration. The import declaration provides details about one or more import transactions, – and for each transaction provides information for a large number of data fields including country of origin, a detailed product classification, and a complete listing of firms involved in the transaction and in the logistics supply chain.<sup>15</sup> The risk management system applies predetermined weights to data in each of these fields, as well as information on the scale of possible harms associated with a non-compliant shipment of this type, to generate a ‘risk score’ that is used to determine whether or not the goods will be inspected. Inspectors conduct the search when the system recommends it.<sup>16</sup> The outcome of the search is recorded electronically, and communicated to the central committee for the purposes of updating the risk model.

# **2.1 Effects of information technology on system performance**

In our view the four contributions of IT listed above can be understood as effectively reducing the ambiguity surrounding the inspecting agency’s _ex ante_ expectation of the probability that a shipment with given observable characteristics will not be compliant with import regulations. Improved capabilities of storage and recall allow the history of a shipment with given characteristics to better inform the agency’s forecast of non-compliance for shipments with the same bundle of characteristics. Information from frequently observed shipments can be used to estimate the marginal contribution of specific shipment characteristics to compliance, thereby improving the agency’s forecast of compliance probabilities for less frequently-observed shipments with similar characteristics. The combination of nationally-collected information on relationships between shipment characteristics and non-compliance, together with a common well-defined understanding of possible harms, reduces heterogeneity in the inspection decision that might otherwise exist across individual inspectors or inspecting offices. The IT system’s ability to facilitate the rapid flow of ac-

> 15Most declarations document details for a single transaction, but some declarations contain many. We use transactions as our unit of analysis since transactions map to a single combination of product, origin country and firm.

> 16Inspectors usually have the capacity to deepen the search based on their initial findings. In some contexts they may also have autonomy to initiate a search without guidance from the system, though this would be rare when compared to searches initiated because of guidance from the IT system.

9
|Indigenous or Newar ethnic group (1 = yes, 0<br>otherwise)|20.92|0.59|
|---|---|---|
|Madheshi ethnicgroup (1 =yes,0 otherwise)|19.86|0.70|
|Respondent migrated to site from another location (1 =<br>yes,0 otherwise)|-20.99|0.52|
|Uses biogas(1 = use,0 otherwise)|-73.20<sup>*</sup>|0.03|
|Uses LPG(1 = use,0 otherwise)|48.95|0.35|
|Uses improved biomass cooking stove (1 = use, 0<br>otherwise)|11.30|0.83|
|Uses firewood(1 = use,0 otherwise)|10.60|0.78|
|Household owns land(1 =yes,0 otherwise)|-34.11|0.54|
|Walking distance from respondent’s house to road < 2<br>hours(1 =yes,0 otherwise)|-22.47|0.55|
|Respondent says they are very likely or extremely likely<br>to benefit personally from REDD+ (1 = yes, 0<br>otherwise)|-9.94|0.62|
|Reported firewood usedper month(kilograms)|-0.04|0.39|
|Number in formal CFUGs(number ofgroups)|-4.53|0.81|
|Constant|91.93|0.41|

***, **, * indicate significant at 1%, 5% and 10% levels.  N= 608, R<sup>2</sup> =0.023, F=1.77, prob>F=0.05 Assumes normally distributed payment vehicle and fuelwood reduction attributes

# **<u>4</u>** **~~6.~~ Discussion and Conclusions**

In this paper, we use a choice experiment conducted in 2013 to estimate household-level willingness to participate in a REDD+ program that requires reductions in fuelwood collections, as a function of CO2 prices. We find that for both CF and non-CF households, robust participation occurs at prices that are higher than much of the literature and grazing reduction opportunity costs per ton of CO2 are similar to those for fuelwood reductions in non-CF settings. Rather than prices of $1.00 to $5.00 incentivizing participation, we find that relatively little carbon would be supplied at such prices. This basic finding is in line with two recent published papers focusing on REDD+ pilots in Nepal. These findings in combination with our results, which use very different techniques, suggest that optimism regarding carbon supply in community forestry settings could be somewhat misplaced. Our use of an experimental method to elicit WTA is novel in the recent literature, which has largely used bottom-up methods to estimate WTA within the context of REDD+.

33
opportunities (Munshi 2003). By analyzing consumption we focus on the joint and final effect of all such mechanisms.

The observed divergence between migrants and non-migrants in these data also persists within extended family networks (Beegle, De Weerdt, and Dercon 2011), which violates the full risk-sharing hypothesis (Townsend 1994), and does not support the notion that migration is the result of a household level maximization strategy (Stark and Bloom 1985; Rosenzweig and Stark 1989; Grimard 1997). It could, however, be consistent with other reciprocity-based models (e.g. limited commitment, moral hazard, or hidden income) that permit the co-existence of divergent consumption growth and risk sharing. In our empirical analysis, we find that migrants are affected by shocks to others in the network whereas non-migrants are not. Such unilateral insurance leads us to reject the reciprocity-based risk-sharing models.

One explanation for this observed lack of reciprocity could be that migrants insure non-migrants in exchange for other benefits (Lucas and Stark 1985; Hoddinott 1994). These benefits could accrue to the migrant later in life and outside the purview of our survey data. We consider, but reject a number of such longer-run transactional motives for the observed unilateral insurance. The results are, however, very much in line with findings from the diverse literature on social norms (Platteau 2000; Cox and Fafchamps 2007; Burke and Young 2011), where those who move ahead remain obligated to their extended family in the home community.

Our results speak to an emerging literature that worries about home communities imposing a stifling ‘kin-tax’ on the upwardly mobile. Baland, Guirkinger, and Mali (2011) show how people take out costly loans in order to conceal their income, while Platteau (2012) sees migration as a means to escape the implied prying eyes and incessant demands of the kinship group. The kinship poverty trap model of Hoff and Sen (2006) predicts possible resistance from the home communities as they feel threatened by productive forces leaving and severing links with home to escape taxing demands for assistance. Anticipating this, the home community may set up subtle exit barriers, which could lead to below-optimal levels of migration. Jakiela and Ozier (2012) report laboratory evidence from Kenya that women feel obliged to 4
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles

**Figure 1: More than 50% of civil servants are men between the ages of 30 and 50 ***

<!-- Start of picture text --> 3.00<br>2.50<br>2.00<br>1.50<br>1.00<br>0.50<br>0.00<br>17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60<br>Age<br>Male Female<br>Number of employees (000')<br><!-- End of picture text -->

**Notes:** * Age distribution of civil servants (2003/2004) **Source** : MOFPED

**Figure 2: More than 30% of all teachers are women between the ages of 17 and 60 ***

<!-- Start of picture text --> 6.00<br>5.00<br>4.00<br>3.00<br>2.00<br>1.00<br>-<br>17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60<br>Age<br>Male Female<br>Number of employees (000')<br><!-- End of picture text -->

**Notes:** * Age distribution of teachers (2003/04). **Source** : MOFPED.

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