rafmacalaba/data-use-mentions-extended huggingface dataset:
in at least three relevant ways. First, it uses direct, satellite-based observations of CO2 rather than estimated emissions inventories. Previous studies have risked introducing some technical element of simultaneity _by construction_ , because their emissions inventories derive entirely from measures of activities that provide components of income. Second, the present study uses local, spatially-referenced data rather than national or regional aggregates. Third, we take a different approach to energy as a link between income and CO2 emissions. Most previous studies of the simultaneity issue have focused on the energy sector as the key determinant of simultaneity between income and CO2 emissions, because CO2 emissions increase with energy use and energy use contributes to economic growth. Researchers’ views have differed substantially on the potential importance of this problem. Csereklyei and Stern (2015) argue that the bias is fairly small, so estimated emissions-income elasticities will be close to effects for exogenous changes in income. In any case, the modeling exercise in this paper takes an entirely different approach to the energy sector, incorporating georeferenced data for the coal-, gas- and oil-fired power plants that create sectoral CO2 emissions while excluding other power facilities (e.g., nuclear, solar, wind, hydro) that do not. In the previous subsection, we have discussed and illustrated the implications of our approach to energy facilities.

As previously noted, the EKC component of our model relates to non-industrial, income-related activities (e.g., motor transport) that cannot be observed directly. The relationship between income and CO2 emissions from these activities may also include elements of simultaneity, such as the joint emission by some activities of CO2 and local air pollutants (NO2, SO2, CO) that affect health, productivity, and therefore income (Van Ewijk and Van Wijnbergen 1995).

For econometric estimation, instrumental variables (IV) provide the standard correction for such simultaneity problems. In recent work on EKC estimation for local pollutants, Lawell and Liscow (2013) have identified the age dependency ratio as a plausibly-exogenous instrument that affects
50% Industry val add<br>40% 800 (% of GDP)<br>600<br>30%<br>20% 400 Agriculture, val<br>add(% of GDP)<br>10% 200<br>0% 0<br>GDP per capita<br>1965 1985 2005 (Maddison)<br>Khamis $)<br>GDP per capita (1990 Intl. Geary-<br>Share of sector in GDP (%)<br><!-- End of picture text --> Sources: For sectoral contributions to GDP, World Bank, World Development Indicators database; for GDP per capita, Maddison.

The sustained decline in the agricultural share of the labor force that is one of the stylized facts of economic development has not been observed in Sub-Saharan Africa. The region‘s economies
and meeting user needs. Improvements in performance can be represented as a virtuous data cycle that can become self-sustaining.

# _Further description of SPI pillars and dimension_

A quick primer on names. We refer to the 5 rows in the framework in Figure 7 as pillars. We refer to the 22 cells in the framework in Figure 7 as dimensions. Finally, each dimension may be composed of multiple indicators. For instance, the dimension on censuses and surveys is made up of indicators on whether population censuses have been conducted, agriculture censuses, labor force surveys, etc.

# **Data use**

The data use pillar is segmented by user type. The tiles on the Dashboard provide an indicator of use of statistics respectively by the legislature, executive, civil society (including sub-national actors), academia and international bodies. A mature system would score well across the tiles. Areas for development would be highlighted by weaker scores in that domain enabling questions to be asked about prioritization among user groups and why existing services are not resulting in higher use of national statistics in that segment.

# **Data services**

The data services pillar is segmented by service type. The tiles on the Dashboard provide an indicator of the quality of data releases, the richness and openness of online access, the effectiveness of advisory and analytical services related to statistics and the availability and use of data access services such as secure microdata access. Advisory and analytical services might incorporate elements related to data stewardship services including ethical consideration of proposals and calling out misuse of data in accordance with the Fundamental Principles of Official Statistics.

# **Data products**

The data products pillar is segmented by topic and organized into social, economic, environmental and institutional domains using the typology of the Sustainable Development Goals. This approach enables comparisons across countries and anchors the system in the 2030 agenda so that a global view can be generated while enabling different emphasis to be applied in different countries to reflect the user needs of that country.

# **Data sources
s_ ; _θ_ ), where the expectation is with respect to the conditional distribution of the non-sampled data _yc,r_ given the sample data _yc,s_ , determined by the nested error model. Under normality of the random terms in the model, the conditional distribution is also normal. In step 2 of the above procedure, welfare values for non-sampled households are generated from that conditional distribution, but with estimated model parameters. Hence, the above ˆ MC simulation procedure actually approximates the EB estimator _τc_<sup>_EB_</sup> = _E_ ( _τc|yc,s_ ; _θ_<sup>ˆ</sup> ), which is the best predictor with model parameters replaced by the corresponding estimates based on the original sample survey data _θ_<sup>ˆ</sup> 0 = ( _β_<sup>ˆ</sup> 0 _,_ ˆ _ση_<sup>2</sup> 0<sup>_,_ˆ</sup><sup>_σ_</sup> _e_<sup>2</sup> 0<sup>).Notethat,unliketheELLbootstrapprocedure,heretheseestimates</sup> are kept constant across simulations because there is a unique DGP. Hence, this simulation procedure delivers a simple MC approximation for the EB predictor ˆ _τc_<sup>_EB_</sup> = _E_ ( _τc|yc,s_ ; _θ_<sup>ˆ</sup> ) of _τc_ , where the expectation is replaced by the corresponding MC average.

If the target parameter is just the area mean _τc_ = _Nc_<sup>_−_1</sup> � _Nh_ =1 _d_<sup>_ych_,andthereisnolog-transformation</sup> of welfare, then the best predictor reduces to _E_ ( _τc|yc,s_ ; _θ_ ) = _x_ ̄ _cβ_ + _γc_ ( ̄ _yc,s − x_ ̄ _c,sβ_ ), which corrects the regression synthetic estimator _x_ ̄ _cβ_ by accounting for the area effect using the survey data.
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-3<br><!-- End of picture text -->

<!-- Start of picture text -->
-4<br><!-- End of picture text -->

<!-- Start of picture text --> Market<br>Income<br><!-- End of picture text --> <!-- Start of picture text --> Disposable Consumable Final Income<br><!-- End of picture text --> <!-- Start of picture text --> Income<br><!-- End of picture text --> <!-- Start of picture text --> Income<br><!-- End of picture text --> Note: Poverty is measured at the national poverty line. Inequality is measured by the Gini Index. Market income includes all income from wages and earnings, capital incomes and rents, and private remittances. Disposable income subtracts direct taxes and adds direct transfers. Consumable income subtracts indirect taxes and adds indirect subsidies. Final income adds in-kind health and education spending.

**_Table 3. Fiscal Loss or Gain at Consumable Income by Poverty Line_**

||**Fiscal Impoverishment**|**Fiscal Gains**|
|---|---|---|
||Post-fiscal losers<br>(percent total<br>population)|Post-fiscal gainers<br>(percent total<br>population)|
|_At consumable income_|||
|PPP $1.9|0.0|0.7|
|PPP $3.2|0.2|2.6|
|PPP $5.5|3.1|12.7|
|Nationalpovertyline|4.2|14.7|

Note: The table shows loss and gains based on households’ consumable income, which
- 50 -

Civilian wage bill in the Central Govemment is taken from IMF Staff Country Report No. 96/43-Burundi Statistical Annex and refers to 1995. GDP at market price is an estimate from the same report for 1995. Accordingly, GDP per capita, Average Central Govemment wage, Wages and salaries as percent of GDP as well as Average Govemment Salary are based on this data.

# Cameroon

Data on Central Govemment and employment come from Marc Stephens (AF5CO) and is for 1993. Education data are provided by Herbert Bergmann (AF3PH) and are for 1994/95. Total Teaching staff is estimated at 67,530, of which 40,970 are in primary schooling, and 14,917 in general secondary. Administrative staff is estimated at 650 for the central level, and 200-300 people at the provincial level (20-30 staff for each of the 10 provinces). At lower levels, sub-delegations have been created but the number of employees is unknown. There are 67 Inspectorates at departmenta **l** evels with 3-5 staff each (201-335 people) and 200 sub-inspectorates (number of employees unknown). Total administration staff is therefore estimated at 1,235, which will be included among central and noncentral govemment employment respectively and not in education employment. Health employment is from WHO and is for 1993. GDP is taken from World Tables 1995 and refers to 1993. Data on Central Govemment wage bill (civilians) are projections from Mark Stephens for 1995, based on IMF Background document of December 11, 1994 and mission calculations. Average monthly civilian rate is also from the same document.

# Cape Verde

Population data are taken from the World Bank's Social Indicators of Development 1996 and refer to 1994. Labor Force and unemployment data are taken from IMF Report SW96/212 Recent Economic Developments of August 12, 1996 and relate to 1995. Data on Central Government, Non-central Govemment, Education and Health and State-owned enterprises employment are taken from IMF Report No. SM/96/212 of August 12
with head-teachers on school demographics, operational costs, management and student outcomes. The survey was not designed to be nationally representative.

For private schools, an additional module on possible partnership with the government was included. Private schools were randomly selected to be administered one of two versions of this module. The first version phrases questions in a personalized manner, asking respondents directly their opinions on partnering with the government. The second version phrases questions in a generalized manner, asking respondents their opinions on partnerships of the average private entrepreneur.

Average PSLE marks and CSEE grade point averages (GPA) from 2013 to 2016 were merged with the 2015 Morogoro data to assess differences between public and private schools over time. PSLE marks and CSEE GPAs were provided by the National Examination Council of Tanzania (NECTA).

**3. 2017 SMS survey of parents** undertaken by TWAWEZA, a prominent civil society organization focused on improving education service delivery in Tanzania.  This is referred to henceforth as the _<u>2017 SMS data</u>_ <u>. Every year TWAWEZA conducts a Sauti za Wananchi (Voices of</u> Citizens) survey using mobile phones to regularly collect information from a nationally representative panel of mainland Tanzanian citizens. To help avoid bias toward the wealthiest households (who are more likely to own mobile phones) and those in urban areas (who are more reliably able to charge them), all respondents recruited for the survey were offered a simple mobile phone and solar charger.

The 2017 survey, used in this paper, collected information from 1,396 households across Tanzania who had a child in primary school and/or a child in secondary school. The respondent for the questionnaire was the head of the household. Of the households surveyed, 1,251 had at least one child in public school, 77 had at least one child in private school, and 68 households had children in both public and private schools. This was the 23<sup>rd</sup>
Finally, in the countries studied, ear-marking of intergovernmental transfers characterized much of fiscal decentralization: this feature was found in Argentina, Brazil, Colombia, Germany, and the Philippines. Whether ear-marking improves performance or not is not quite clear. Consistent with evidence, we find revenues ear-marked in developing countries more often disappear probably because they are connected in ways that are not actually desirable locally. In Germany, ear-marking has proven useful for keeping uniform road quality standards across the country but it has also brought about large cost inefficiencies. Whether ear-marking accompanied with fiscal decentralization is desirable or not depends again on the specific country objectives. In Germany we see that the equity objective overrides the efficiency objective.

This paper has provided empirical evidence and guidance in the selection of optimal structures for providing road services. A companion paper examines the same issues econometrically with a larger data set (cross-country panel, time series, and state and local data) and qualifies some of the findings in this paper (see Humplick and MoiniAraghi, 1996).

33
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Why infrastructure matters

Most African countries enjoyed increased growth in the 2000s. The overall improvement in per capital growth rates since the 1990s has been estimated at just less than two percentage points, of which 1.1 percent is attributable to better structural policies and 0.9 percent to improved infrastructure. In West Africa, the ICT revolution contributed one percentage point of additional per capita growth, while deficient power and transport infrastructure has held growth back by 0.1 percentage points per year. Although there was not enough data to address the specific growth impact for Liberia, the experience of other post conflict countries—such as Cote d’Ivoire, Democratic Republic of Congo, Rwanda, Sierra Leone, and Sudan—indicates that the infrastructure growth effect also holds in fragile environments (figure 1a).

Simulations suggest that if West Africa were to improve infrastructure to the level of the African leader—Mauritius—annual per capita growth rates would increase by 2 to 4 percentage points. About half of this total would be a result of improvements in the power sector, which could contribute as much as 2.5 percentage points to the per capita growth rate (figure 1b). Although lack of data prevented us from performing the simulation for Liberia, the experience of other post-conflict countries illustrated below suggests that a similar effect applies. Overall, evidence from enterprise surveys suggests that infrastructure constraints are responsible for about 40 percent of the productivity handicap faced by African firms (figure 2).

**Figure 1. Infrastructure has contributed much to economic growth—but could contribute much more** a. Infrastructure’s contribution to annual per capita economic growth across various subregions of Africa, 2003–07, in percentage points

<!-- Start of picture text --> telecom electricity roads<br><!-- End of picture text --> 3
, which means that there are (in principle) over 100 years of consistently reported data (Hall, Jaffe and Trajtenberg (2002)).

However, the use of patent data to measure the level innovation activity is not without problems. The most obvious limitation is that not all inventions are patented and hence not all innovation activity is captured. One of the reasons for innovation not being patented is that not all inventions meet the criteria set by the U.S. Patent and Trademark Office (USPTO). USPTO requires that the invention be novel, nontrivial and have commercial application. Another reason for non-patented inventions is simply that the inventor did not take the initiative to apply for a patent, and may have decided to rely on secrecy to prevent duplication of the invention by competitors. Given that there is no known available data on inventions that are not patented, little is known of the extent to which patents are representative of the entire population of inventions.

> 35 Utility patents may be granted to anyone who invents or discovers any new and useful process, machine, article of manufacture, or compositions of matters, or any new useful improvement thereof.

> 36 Design patents may be granted to anyone who invents a new, original, and ornamental design for an article of manufacture.

> 37 Plant patents may be granted to anyone who invents or discovers and asexually reproduces any distinct and new variety of plant.

> 38 See Griliches (1990) for a survey of research using patent data.
<mark>Table 10 shows the budget shares for each of the categories of good and services using both the standardized and national consumption aggregates. This table shows that Maldivian households only spend 37 percent of consumption expenditure on food on average, and 53 percent on nonfood non-durables.</mark><sup>18</sup> <mark>Households in the rest of the countries spend between 43 to 59 percent of their consumption expenditure on food, and somewhere between 19 to 32 percent on non-food, non-durables. The countries spend less than 10 percent of their consumption on health, and on education and durable goods respectively. Housing, which includes rent, imputed rent (if included in the national consumption aggregate), and expenditure on utilities, shows wide variation, from a low of 6 percent for Nepal to as high as 33 percent in Maldives.</mark> <mark>Within India, rural households spend 8 percentage points more on food, reflecting lower levels of well-being in rural India. There is some difference between housing expenditures - while rural Indian households report spending 12 percent of their average budget on housing, urban India spends 16 percent (excluding imputed rent). This implies that much of the differences in poverty rates between rural India and the rest of South Asia is driven by the lack of housing expenditure (imputed rent) data in the national consumption aggregate for rural India.</mark>

**Table** **<u>9. Average daily per capita expenditure by category of goods and services (USD 2011 PPP)</u>**

|||**Banglade**|**sh**|**Bhutan**||**India**||**Maldives**|**Neal**|**Pakistan**|**Sri**|
|---|---|---|---|---|---|
World Bank (BOOST) to ensure that the instrument is not picking up community-level variation in the supply of public education. Matching both datasets is imperfect because the availability of the budget data was not anticipated at the time of the household survey (see appendix S.1 for a detailed description of the data and matching procedure). We include the school-level executed budget in several expenditure categories as additional explanatory variables.<sup>29</sup> The strong negative effect on bribes remains even after adding the additional controls, which approximately halves the sample size.<sup>30</sup> Schools’ wage bills, which closely correspond to the schoolteachersper-pupil ratio (cf. figure S.4), teaching material and schools’ maintenance funds are not significantly correlated with household educational expenditures (table S.11: columns 1-6). By contrast, schools’ expenditures on utilities and transports, where community leaders often report lacking funds, exhibit signs of substitutability of private and public expenditure. There is also some tentative evidence of substitution between the parental investment of time and the time teachers could allocate to individual children (column 7).<sup>31</sup> The strong correlation between migration and informal payments to teachers is also robust when controlling for an index of infrastructural quality of the school (table 4: column 1). We furthermore tested whether the migration-induced reduction in informal payments is lower in worse-funded schools where informal payments may be less controversial but did not find any robust differences (results available upon request). Sending students to schools with funding for school buses and attending a more distant school, both of which proxy secondary and advanced secondary schools that cover larger areas, correlate positively (although statistically insignificantly) with informal payments. Using school fixed-effect regressions to compare students within schools, we find that better off parents pay more to teachers and buy more tutoring from their children’s teachers (table 4: columns 3 and 4).<sup>32</sup> This
||0<br>0.05<br>0.1|0.15<br>0.2<br>0.25|
|---|---|---|
|**Air strike warning**<br>|||
|**Regime military action, Air strike**|||
|**Foreign intervention**<br>|||
|**Children, civilians**<br>|||
|**Liberation army, Regime military**<br>|||
|**Religion, texts**|||
|**Air strikes, damage, civilians**|||
|**War news, war reporting**<br>|||
|**Description, names**<br>|||
|**Aleppo news**|||
|**ME politics, Assad, Lebanon**|||
|**Crimes, investigations**|||
|**anti−ISIS campaign**|||
|**News reports**|||
|**Goods, coronavirus, numbers**|||
|**Regime military actions**<br>|||
|**Idlib, roads, governance**<br>|||
|**Economy, weather**|||
|**Afrin, Turkish−Kurdish politics**<br>|||
|**Horoscopes, description**|||
|Return Only|||
|<br>IDPs Only|0<br>0.05<br>0.1|0.15<br>0.2<br>0.25|
|Neither<br>Both|Estimated|topic proportion|

Figure 3: Topic prevalence based on presence of returnees and IDPs, `Location Mentions` dataset.
580|5,149|7,164|2,997|5,502|
|R-squared|0.875|0.875|0.861|0.872|0.886|0.886|

Source: OECD ICIO database. *** p<0.01, ** p<0.05, * p<0.1. Notes: Robust standard errors in parentheses. _FVAX_ and _DVAR_ lagged. All level variables in natural logarithms.

# **_Social Standards_**

The identical intuition holds for social standards; table 10 shows that the empirical evidence is in line with the evidence for labor standards. A basic welfare system, measured through pension and unemployment insurance proxies, facilitates the positive effects of GVC integration as a seller on domestic value added (columns 2 and 3); and these effects are not driven by any particular income group. The positive mediating effect on the selling side supports the findings by Jetha and Linsen (2015) and the World Bank (2012).

29
# **Figure 6.2.  Real per capita expenditure by selected expenditure per capita deciles, Thailand, 2007:M1 – 2010:M6**

Source: Thailand Socio-Economic Surveys of 2007, 2008, 2009, and 2010. Shaded area marks period of recession.

<!-- Start of picture text --> 7000<br>6000<br>5000<br>4000<br>3000<br>2000<br>Urban<br>HH size = 2<br>1000<br>HH size = 6+<br>0<br>2007m1 2007m7 2008m1 2008m7 2009m1 2009m7 2010m1<br>Figure 7.1. Real per capita expenditure by urban areas, and selected household sizes,<br>Thailand, 2007:M1 – 2010:M6<br>Source: Thailand Socio-Economic Surveys of 2007, 2008, 2009, and 2010. Shaded area marks period of recession.<br>7000<br>6000<br>5000<br>4000<br>3000<br>Nominal income<br>2000<br>Nominal expenditure<br>Real Expenditure<br>1000<br>0<br>2007m1 2007m7 2008m1 2008m7 2009m1 2009m7 2010m1<br>Real expenditure/capita, baht/month<br>Real expenditure/capita, baht/month<br><!-- End of picture text -->

**Figure 7.2.  Real per capita income (dotted line) and expenditure, Thailand, 2007:M1 –**

Losers and Winners in Recession: Thailand 2008-09 14