rafmacalaba/datause-encoder-data huggingface dataset:
and how they have evolved since the early 1990s. There are also striking changes in individual countries over time. Singapore shows a dramatic rise in service intensity, while China’s trade explodes in the direction of manufacturing, from 13.7 percent on a gross value basis in 1992 to almost 40 percent in 2007. In Tables 11 to 19, we focus on just four countries: Brazil, China, Korea, and the United States.

Table 11 provides a broad overview of services exports on a gross and value added basis for Brazil, China, Korea, and the United States. For the United States, there was some rise between 1992 and 2007, with a period of stagnation between 2001 and 2004, on a gross value basis. However, this is likely because of the rising levels of gross trade relative to value added in manufacturing sectors, as shown in Table 2. There has not been the same rise in roundaboutness for services, and as such gross trade data understate the importance of services exports to the United States. When we focus instead on direct value added content, services (excluding trade and transport) accounted for 28.4 percent of exports in 2007, up from 17.2 percent in 1992. China has seen a dramatic drop in share terms, but as can be seen in Figure 1 this is not something linked to the level of services exports per se, but rather to the very dramatic increase in manufacturing exports since 1992. In 1992, almost half of exports were textiles and clothing, while services were a large share of a small base. While services exports have grown little since, China’s exports are now dominated by machinery exports, on both a value added and gross value basis.

For both Korea and Brazil, though commercial services are a relatively low share of exports on a gross value or direct value added basis, they are clearly a major factor in the total cost structure of industry. For example in Korea in 2007, commercial services accounted for only 6.9 percent of exports on a gross value basis, and 13 percent on a direct value added basis, but for 28 percent of all value added contained in exports when embodied exports are included. In contrast, as China has opened up and became a dominant exporter of manufactured goods (especially machinery) the contribution of services to total export value added decreased. Indeed there is an interesting dynamic to China’s service intensity. As China has proceeded to open up and undertaken a dramatic increase in exports of manufactured goods, the initial stages used for productions saw a drop in total domestic value added in exports, with the rising share of manufactured goods. However, as China has progressed 19
# **Table A29:  U.S. Department of State Trafficking in People Report (TPR)**

|**Data Provider**<br>United States Department of State||||||||
|---|---|---|---|---|---|---|---|
|**Description**<br>Foreign affairs department of the Unit|ed Stat|es Gov|ernmen|t.||||
|**Website**<br>www.state.gov||||||||
|**Data Source**<br>Trafficking in People Report||||||||
|**Type**<br>Expert assessments<br> <br>||||||||
|**Respondents**<br>United States embassy staff worldwid<br> <br>|e subje|ct to ce|ntraliz|ed revie|w|||
|**Frequency**<br>Annual since 2001||||||||
|**Coverage**<br>Global country sample||||||||
|**Public Access**<br>Yes||||||||
|**Description**<br>This report scores countries on a fou<br>to combat "severe trafficking in p<br>commercial sex act is induced by forc<br>perform such act has not attained<br>transportation, provision, or obtainin<br>force, fraud or coercion for the purpo<br>bondage, or slavery. Note that the re<br>lag the data from this source by one y<br>|r-point<br>ersons"<br>e, frau<br>18 yea<br>g of a<br>se of su<br>ports re<br>ear.<br>|scale b<br>defin<br>d, or co<br>rs of<br>person<br>bjectio<br>fer to d<br>|ased o<br>ed as<br>ercion,<br>age; o<br>for lab<br>n to in<br>ata fro<br>|n the ex<br>(a) sex<br>or in w<br>r (b) th<br>or or se<br>voluntar<br>m the pr<br>|tent of<br>traff<br>hich th<br>e recr<br>rvices,<br>y servi<br>evious<br>|gover<br>icking<br>e perso<br>uitmen<br>throug<br>tude, p<br>year:<br>|nment efforts<br>in which a<br>n induced to<br>t, harboring,<br>h the use of<br>eonage, debt<br>we therefore<br>|
|2006|2005|2004|2003|2002|2000|1998|1996|
|**Voice and Accountability**<br>NA<br>..|..|..|..|..|..|..|..|
|**Political Stability**<br>NA<br>..|..|..|..|..|..|..|..|
|**Government Effectiveness**<br>NA<br>..|..|..|..|..|..|..|..|
|**Regulatory Quality**<br>NA<br>..|..|..|..|..|..|..|..|
|**Rule of Law**||||||||
|Trafficking in People<br>X|X|X|X|X|X|..|..|
|**Control of Corruption**||||||||
|<br>NA<br>..|..|..|..|..|..|..|..|
|**Country coverage**<br>**149**|**149**|**142**|**131**|**116**|**82**|**..**|**..**|
|**Year of Publication**<br>**2006 **|**2006 **|**2005 **|**2004 **|**2003 **|**2001**|**..**|**..**|

67
a government’s debt portfolio. In some countries, mostly emerging and developing, DMOs still manage domestic and external debt portfolios using separate software. This structure is largely the legacy of a time when emerging market countries could mainly borrow from multilateral and bilateral external sources and had shallow domestic markets. As these countries started to develop their domestic market for government securities, some decided to monitor domestic debt in Excel,<sup>8</sup> as their DMS did not support domestic securities. Separate databases are also sometimes a result of external and domestic debt managed by separate teams and units. This kind of debt database fragmentation significantly reduces a DMO’s ability to undertake debt management operations in an integrated fashion.

A DMS should also capture data on debt-like instruments, such as the on-lending of government debt and sovereign guarantees, as these structures create credit risk for the government and ultimately have an impact on its finances if not repaid. Capturing this data in a DMS also enables holistic policy analysis. Coverage may further be extended to include debt obligations arising from other public-sector players such as local and state governments and financial and non-financial public corporations. Some countries also record private sector external debt and record and monitor their on-lending portfolio through the DMS. For a DMS to keep a comprehensive database of all other activities of the DMO, the software package should have a flexible architecture that allows the addition of new functions, and the DMO should have the resources required to maintain the expanded system.

# **ii. Core functions**

A DMS should be a user-friendly, secure, accurate and reliable software solution. It should, at a minimum, be able to (i) securely record and maintain all debt-related transactions such as commitments, disbursements and debt service payments; (ii) produce payment projections of principal, interest, and other fees; and (iii) generate reports at individual instrument and portfolio levels. This basic DMS functionality delivers essential debt management support to all three of a DMO’s offices (front, middle and back) (See Figure 1).

# **Table 2: Core functions and degree of importance**

|Recording, validatingand maintainingdebt data|Mandatory|
|---|---|
|Producing paymentprojections|Mandatory|
|Generatingreports for analyses and decision|Mandatory|
|Conducting portfolio and risk analyses|Relevant|
|Planningfuture borrowings|Relevant|
|Resource mobilization|Relevant|
|Connection with FMIS|Relevant|
|Straight throughprocessing|Desirable|

Source: Authors

> 8 In this study, we use system, solution, software and software package interchangeably.

9
- Resource and preference costs are jointly and simultaneously determined when the following conditions both hold:

_ai2 =_ 0; and c,, and **1ty,,** are correlated.

- Resource costs and preference costs are recursively determined when the following conditions both hold:

_ai2 *_ 0; and c,, and **g,,** are correlated.

From these cases we define four models: (a) Model I estimates equation (11) independently assuming ai2 =0; (b) Model 2 estimates equation (12) independently; (c) Model 3 estimates equations (11) and (12) simultaneously assuming _ai2 =0;_ and (d) Model 4 estimates equations ( 11) and (12) recursively with the estimated value of q in (12) feeding into equation (I 1).

We explicitly test the hypotheses described so far in order to conclude on the impact of decentralization on resource and preference costs. We undertake the detailed analysis of observed differences in model specifications in a companion paper which develops the same concepts using detailed case studies, where the actual nature of determining resource and preference costs is known (see Humplick and Azadeh, 1996).

For empirical analysis, defining basic local good units entails normally serious problems since it is difficult to make units of output and input measurable or operational.14 For our analysis, the measure of preference costs is based on Lancaster's (1966) abstractproduct approach, which allows us to define demand for roads indirectly in relation to certain of its attributes. _1_<sup>_5_</sup> For roads, the condition of the network can serve as a proxy for demand for roads. 1<sup>6</sup> Resource costs are measured in terms of unit costs of road activity. It follows that unit cost is just the first derivative of cost with respect to quantity of roads provided, which serves as a basic measure of efficiency of road provision.

As a measure of decentralization (the variable ,), we employ both the aggregate definition of total decentralization and the disaggregated one, which distinguishes between various types of decentralization by function. As already mentioned, this disaggregation is important because depending on the technology, road works may require the involvement of various entities. Further, it allows us to develop both measures of fiscal and functional decentralization. We distinguish between: a) total decentralization

> _14_ Hirsch ( 1968 ) provides a survey on measurable units of outputs and inputs for public services. Hirsch, W. (1968) The Supply of Urban Services"; Issues in Urban Economics; based on papers presented at a conference in January 1967.

> _15_ Lancaster, K. "A new Approach to Consumer Theory", Journal of Political Economy, vol. 74 (April, 1966). p. 132-57.

_16_ Ben-Akiva et al (1993) develop a framework for substituting the condition of roads as a proxy for user costs while utilizing roads of a given condition.

13
29 Table 3. Variables and Descriptive Statistics for Determinants of International Migration

||Variable|Mean<br>(StandardDeviation)|
|---|---|---|
||Distance (miles) from country to labor-receiving region<br>(US, OECD or Arab Gulf)|7.25<br>(1.04)|
||Economic variables||
||Gini coefficient|-0.92<br>(0.28)|
||Per capita GDP (constant 1995 dollars)|6.98<br>(0.97)|
||Poverty headcount ($1/person/day)|2.37<br>(1.54)|
||Rate of consumer inflation|2.91<br>(1.34)|
||Rate of unemployment in labor force|2.08<br>(0.79)|
||Demographic variables||
||Population density (people per sq km)|3.88<br>(1.20)|
||Share of population over age 25 with|<br>1.99|
||secondary education|(0.87)|
||Political, Financial variables||
||Government stability|1.74<br>(0.34)|
||Country credit rating|3.22<br>(0.48)|
|Notes:|All variables expressed in logs.||

Sources: All poverty and inequality data from Annex Table A1. Data on per capita GDP, consumer inflation, unemployment, population density, and secondary education from World Bank, SIMA database. Government stability data from PRS Group, International Country Risk Guide. Country credit ratings from International Investor.
Table 2: Deforestation intensity results using the penalized kernel regression.

||(means)|(25%)|(50%)|(75%)|
|---|---|---|---|---|
||_Dependent: _|_Log deforestat_|_ion intensity of _|_1000 GDP p.c._|
|Log 1000 GDP per capita<sup>_∗∗_</sup>|-0.453***<br>(-16.962)|-0.610***<br>(-22.856)|-0.476***<br>(-17.83)|-0.289***<br>(-10.822)|
|Population density|-0.001***|-0.003***|-0.002***|0.001***|
||(-5.351)|(-18.303)|(-9.578)|(8.449)|
|Undernourishment rate|0.001|-0.008***|0.002|0.011***|
||(0.371)|(-4.48)|(0.919)|(6.171)|
|Poverty 1.90$ rate|-0.005***|-0.013***|-0.004***|0.005***|
||(-4.118)|(-10.353)|(-2.878)|(4.297)|
|Manufacturing GDP share|-0.015***|-0.055***|-0.016***|0.023***|
||(-5.459)|(-19.402)|(-5.551)|(8.04)|
|Services GDP share<sup>_∗∗_</sup>|-0.032***|-0.051***|-0.036***|-0.016***|
||(-16.57)|(-26.109)|(-18.436)|(-7.961)|
|Urban population share|0.006***|-0.008***|0.009***|0.019***|
||(5.597)|(-6.929)|(7.767)|(17.111)|
|Bottom 40% income share<sup>_∗_</sup>|-0.027***|-0.06***|-0.033***|0.003|
||(-5.416)|(-12.304)|(-6.671)|(0.55)|
|N = 1520|R<sup>2</sup>= 0.922|_λ_=0.691.|_∗_p_<_.1; _∗∗_p|_<_.05; _∗∗∗_p_<_.01|
|_Constant omitted, t-statistics _<br>_Model controls for mean, min _<br>_∗Inner 50% of signifcant mar_<br>_∗∗Inner 50% of marginal signi_|_in parenthesi_<br> _and max ND_<br>_ginal efects _<br>_fcantly excl_|_s. Optimal m_<br>_VI, forest co_<br> _same sign, bu_<br>_udes zero._|_odel contained n_<br>_ver, and country _<br>_t range includes _|_o fxed efects._<br> _size._<br> _zero._|

Table 3: Pollution intensity results using the penalized kernel regression.

||(means)|(25%)|(50%)|(75%)|
|---|---|---|---|---|
||_Dependent: _|_Log pollution _|_intensity of _|_1000 GDP p.c._|
|Log 1000 GDP per capita<sup>_∗∗_</sup>|-0.691***|-0.842***|-0.692***|-0.567***|
||(-47.899)|(-58.388)|(-48.026)|(-39.307)|
|Population density<sup>_∗∗_</sup>|0.002***|0.001***|0.002***|0.003***|
||(18.523)|(6.658)|(17.063)|(30.846)|
|Undernourishment rate|-0.002**|-0.008***|-0.003***|0.003***|
||(-2.172)|(-8.675)|(-3.256)|(3.267)|
|Poverty 1.90$ rate<sup>_∗_</sup>|0.003***|0.000|0.003|0.008***|
||(4.493)|(0.371)|(4.842)|(11.603)|
|Manufacturing GDP share|-0.009***|-0.023***|-0.01***|0.004***|
||(-6.436)|(-15.78)|(-6.756)|(2.492)|
|Services GDP share<sup>_∗_</sup>|-0.007***|-0.012***|-0.007***|-0.001|
||(-7.00)|(-11.68)|(-6.753)|(-1.286)|
|Urban population share|-0.006***|-0.015***|-0.008***|0.001**|
||(-10.746)|(-24.929)|(-14.101)|(2.547)|
|Bottom 40% income share|-0.019***|-0.045***|-0.015***|0.012***|
||(-7.776)|(-18.221)|(-6.086)|(4.682)|
|N = 1520|R<sup>2</sup>= 0.978|_λ_=0.691.|_∗_p_<_.1; _∗_|_∗_p_<_.05; _∗∗∗_p_<_.01|

_Constant omitted, t-statistics in parenthesis. Optimal model contained no fixed effects. Model controls for mean, min and max NDVI, forest cover, and country size. ∗Inner 50% of significant marginal effects same sign, but range includes zero. ∗∗Inner 50% of marginal significantly excludes zero._ 19
11 measure. We preferred consistent data series, preferring the same survey and the same adaptation of it. In some cases, we had no choice but to switch survey, e.g. because a survey was discontinued or because we had limited access to the surveys. After excluding datapoints on quality grounds, and after selecting among the remaining datapoints, we retained 612 of the 1,000 surveys, covering 146 of the 154 countries. The estimates presented below are for the latest year for which we have data; country-level trends will be analyzed in a future paper.

# **New global estimates of out-of-pocket health expenditures – results**

In this section we present our new global estimates of out-of-pocket health spending using the measures in Table 1. We use maps to show the international variation in our measures and, for selected measures, tables showing the means by region and World Bank income group. In addition, to shed light on the sources of international variation in our measures, we present the results of multiple regressions that show the correlations between each of our measures and selected macroeconomic and health system variables.<sup>10</sup> We discuss the charts and regressions results below, going through the measures in Table 1 in turn.

## <u>Expenditure in absolute terms</u>

The top left map in Figure 2 shows per capita out-of-pocket health expenditures in 2011 international dollars for the latest year for which we have survey data. These numbers, it should be reiterated, are based entirely on our estimates from household surveys. They contrast with estimates presented in international health accounts databases, which are sometimes interpolations between datapoints, and sometimes estimated from non-survey sources such as user fees revenues reported by providers; the latter are not always reliable, with providers often reporting less than households say in surveys they spent (see e.g. Lieberman and Wagstaff 2009).

The map reveals large differences across countries. Tables 3 and 4 suggest the differences are driven in large part by international differences in per capita income. High-income countries tend to spend more out-of-pocket than poorer ones. Table 3 also reveals differences across regions even within income groups. Among the high-income countries, per capita out-of-pocket expenditures range from Sweden’s $32 to Switzerland’s $1,200. Among the low-income countries, expenditures range from Madagascar’s $6 to over $100 in Cambodia, Haiti and Nepal. The regression results in Table 4 suggest larger per capita out-of-pocket expenditures are also positively correlated with the share of GDP spent on health, and negatively correlated with the share of THE channeled through government financing ‘schemes’ (like the UK’s National Health Service) and nonprofit schemes.
|**Table (B.1)**<br>Priming Effcacy<br>(1)<br>(2)<br>**COVID-19 listing one**<br>**COVID-19 listing two**<br>0.060**<br>0.073***<br>(0.026)<br>(0.012)<br>0.002<br>0.011<br>3,278<br>3,413<br>trol Group)<br>1.420<br>0.102<br>n the last 10 years (the COVID-19 pandemic, drug traffcking, and internal confict). It gives a score<br>COVID-19 was the second-worst crisis, and 1 if COVID-19 was listed as the third-worst crisis. The<br>n of what was the worst crisis in the last 50 years. It takes a value of 1 if the individual mentioned<br>ses._∗∗∗_signifcant at the 1%, _∗∗_signifcant at the 5%, _∗_signifcant at the 10%. Standard errors are<br>**(B.2)**<br>Multiple Hypothesis Testing (q-values)<br>(2)<br>(3)<br>(4)<br>(5)<br>**Policy Altruism**<br>**Opinion on Effort**<br>**Opinion on Economy**<br>**Opinion on Taxes**<br>-0.021<br>-0.071**<br>-0.067**<br>-0.188***<br>(0.035)<br>(0.035)<br>(0.034)<br>(0.034)<br>[0.286]<br>[0.072]<br>[0.072]<br>[0.002]<br>0<br>0<br>0<br>0<br>0.000<br>0.001<br>0.001<br>0.009<br>3384<br>3346<br>3360<br>3413<br> individual _i_ as measured in the survey and standardized using the mean and standard deviation of<br>e assigned treatment status to COVID priming of the individual. We present q-values for multiple<br>1%, _∗∗_signifcant at the 5%, _∗_signifcant at the 10%. Standard errors are reported in parentheses.|
|---|
|Dep. Variable<br>Treatment (Priming=1)<br>R-squared<br>Observations<br>Mean Dep. Variable (Con<br>_Notes:_ The COVID-19 listing one variable ranks three crises i<br>of 3 to anyone who ranked COVID-19 as the worst crisis, 2 if<br>COVID-19 listing two variable codes an open-answer questio<br>COVID, the pandemic, or Coronavirus in their written respon<br>reported in parentheses.<br>**Table**<br>(1)<br>**Altruism**<br>Treatment (Priming = 1)<br>-0.036<br>(0.036)<br>[0.186]<br>Mean Dep. Variable (Control Group)<br>0<br>R-squared<br>0.000<br>Observations<br>3131<br>_Notes:_ The dependent variable _Yi_ represents the outcome for<br>the control group. The variable _Treatment (Priming=1)_ is th<br>hypotheses testing correction in brackets. _∗∗∗_signifcant at the|

26
# **5.2 Only marginal distributions**

We consider the case for which two separate surveys exist, one on monetary achievements and one on health achievements. These separate surveys provide the marginal distribution in each dimension, but no information on the correlation that may exist between monetary and health achievements.

It is literally not possible to identify a subset of households as poor when relying on two marginal distributions coming from separate surveys. Indeed, identifying the poor requires partitioning one set of households into two categories. Such partition cannot be done because the two separate surveys contain not one but two sets of households, which may overlap or not. Of course, one could identify the poor by only considering one of these two sets of households. This can for instance be done by entirely ignoring one survey, or by only using this survey in order to (somehow) impute the missing achievements in the other survey. We consider the former case in the next subsection and the latter case is equivalent to having no data constraint, at least if one assumes that the imputation is reliable.

In this section, we study the implications of using two separate surveys for poverty measurement. We focus on the poverty index most directly related to identification, namely the (multidimensional) head-count ratio. An important question is whether the two separate surveys contain most of the relevant information for precisely estimating the true fraction of poor individuals. In other words, we ask to which extent the ignored correlation between achievements may affect the head-count ratio.

A standard approach consists in computing upper- and lower-bounds on the head-count ratio by looking at all distributions that match the marginal distributions observed. We consider a stylized example. For simplicity, assume in line with Section 4 that achievements in each dimension are either extreme deprivation (E), moderate deprivation (M) or non-deprivation (N). An individual is identified as poor if she has one extreme deprivation or two moderate deprivations. In each survey, three individuals are surveyed, one is extremely deprived, one is moderately deprived and one is non-deprived, which yields the following two marginal distributions where subscripts refer to dimensions. The following two allocations are consistent with these two marginal distributions with respective head-count ratios H(x) = 1 and H(x<sup>′</sup> ) = 1/3.

This reveals that the ignored correlation may have big implications on the head-count ratio, which in our example lies somewhere in the wide bracket [1/3, 1]. This example also reveals that the head-count ratio is not 27