rafmacalaba/data-use-mentions-extended huggingface dataset:
the most appropriate (though imperfect) approximation of whether a country has successfully exported a given product, relative to the rest of the world and export basket composition. The definition of export success in this case does not distinguish between instances of artificial competitiveness (for instance subsidized exports) and genuine ones, another limitation of the RCA and other trade outcome measures. Another downside of the indicator of success used in this analysis is that it is discrete despite being constructed from a data set with continuous and richer information. Using trade volumes or market shares would not be appropriate because it reflects the size of the exporting economy rather than specialization. Taking into account the RCA value or specific RCA levels in the variable would introduce information from this key variable that escapes easy interpretation: as it would pick up significant variations of RCA levels that can just as well be a reflection of the lack of economic diversification in other products rather than a reflection of success in the product in question on global markets. The constructed binary variable is our dependent variable in the empirical analysis.

# III. <u>Country indicators data</u>

The indicators of the characteristics of each economy are collected from World Bank TCdata360, complemented by IMF WEO data for several macroeconomic indicators. Indicators are grouped in 4 broad categories: demand, production factors, key inputs and institutions. The latter three categories are further decomposed into subcategories summarized in Table 1. These indicators were selected to correspond to the country characteristics a potential investor or trader might consider when targeting a

> 17 Authors observed that this data seems often informed by public announcement of investments. Realized investments often differ quite considerably in scope. This was confirmed in discussions with World Bank colleagues working with this data.

> 18 <u>http://www.cepii.fr/CEPII/en/bdd_modele/presentation.asp?id=37</u>

> 19 The empirical investigation could be conducted at a more disaggregated level, e.g. HS-6 digit.

10
Supply: Practical Lessons from the Decade_ (Washington: The World Bank and UNDP).

- Canning, David and Esra Bennathan, 2000, “The Social Rate of Return on Infrastructure Investments,” Working Paper No. 2390 (Washington: The World Bank).

Collier, Paul, Stefan Dercon and John Mackinnon, 2002, “Density versus Quality in Health Care Provision: Using Household Data to Make Budgetary Choices in Ethiopia,” _World Bank Economic Review_ , Vol. 16, No. 3: pp. 425-448.

- Corbacho, Ana and Gerd Schwartz, “Mexico: Experiences with Pro-Poor Expenditure Policies,” IMF Working Paper No. 02/12 (Washington: International Monetary Fund).

21
India.

Table A.2 describes the characteristics of the 628 districts covered by our NFHS estimation sample using election and census data. On average, 9 percent of constituencies in a district had a female winner; 3 percent of constituencies had a close election between a male and a female candidate; and 1 percent of constituencies had both. Table A.3 presents summary statistics for the 245 districts included in our DLHS sample and describes the DLHS data on public health infrastructure in 7,726 villages.

individuals aged 25 years or older surveyed in the 2008 National Sample Survey of India, 89 percent of women and 93 percent of men lived in the state where they were born.

> 9In Section 4.1, we show that our results are robust to alternative bandwidth selections for close elections.

5
CHNS in the fraction of the sample with some form of coverage, although a continued decline of the traditional schemes

> 6  It seems likely that the short (4-week) recall period used in the CHNS would result in a larger annualized out-of-pocket spending figure than the long (12-month) recall period used in the other two surveys. This is borne out by analysis of data from the 1998 Vietnam Living Standards Survey, which inquires about health spending over a 12-month period, and about use of services and medical expenses during the last 4 weeks. Responses to the spending questions covering the last 12 months produce a budget share (share of per capita household consumption) of 5.2%, while the questions covering the last 4 weeks produce an annualized budget share of 9.1%. The fractions of the sample with ‘high’ and ‘catastrophic’ expenses, defined along the lines of the present paper, are also higher when the 4-week questions are used.
equipment) that connects the User Terminal (e.g. mobile phones) to the core of the network. Relay sites communicate with mobile phones in their vicinity using electromagnetic signals. The quality and availability of this communication link can be affected by several factors such as distance between the relay site and the mobile phone or the presence of obstacles (e.g. hills or buildings). A geographical area is considered _covered_ when the signal of any relay site is strong enough for mobile phones in that area to establish a usable connection link with that relay site. The aggregated coverage of a mobile network is calculated by adding up the coverage of all the relay sites in its Radio Access Network.

To produce the aggregate coverage data for Nigeria, first we collected network infrastructure data directly from three of the largest four MNOs in Nigeria.<sup>9</sup> For each individual relay site we collected

> 8 Source: GSMA Intelligence. A mobile connection is a unique SIM card (or phone number, where SIM cards are not used) that has been registered on a mobile network. Connections differ from subscribers as a unique subscriber can have multiple connections.

> 9 During the period of our analysis, the average combined market share for the three operators that we collected data for was around 75% and one of them had the highest 3G coverage in the market, with 70% of the population covered. While we were unable to collect primary data for the fourth MNO (MNO-4), we collected secondary data on the coverage of MNO-4 in 2015 and 2016 and overlapped this with our coverage data set for the other operators. We did not find any locations that were covered only by MNO-4. To test if MNO-4 could have deployed in those overlapping locations before the other three MNOs, we compared the coverage of MNO-4 with the coverage of the other operators in 2012. This highlighted nine enumeration areas (less than 1%) from the GHS data set that were covered only by MNO-4. This is
|**Degree of State**<br>**participation**|**Definition**|
|---|---|
|Minority|Firms in which the state holds 10-24.9% of the stakes|
|Blocking minority|Firms in which the state holds 25-49.9% of the stakes|
|Majority ownership|Firms in which the state holds 50-99.9% of the stakes|
|Full ownership|Firms in which the state holds 100% of the stakes|
|Direct|Firms directly owned by a Public Authority (national or subnational), which holds at<br>least 10% participation.|
|Indirect|Firms participated by the government through another firm or chain of firms owned<br>by the State where it holds at least 10% participation.|

Source: Author’s elaboration based on World Bank (BOS) database and (Dall'Olio, et al., 2022).

# **5. EMPIRICAL METHODOLOGY**

This paper explores two main econometric exercises. First, it estimates the conditional correlation of the state participation and the performance differentials between BOSs and POEs. For this purpose, we analyze the role of state ownership on firm performance through the following specification: Where, Yi denotes the performance indicator for the firm i using several performance measurements to have a comprehensive overview of the soundness of BOSs vis-à-vis POEs. First, we assess the operational performance in terms of productivity, followed by the financial performance measured by indicators of profitability, liquidity, and returns on investment. Thus,

> 14 Alternative government interventions in the form of regulation can be suitable in these sectors to achieve public objectives.

> 15 This is denoted as the layer of ownership. For instance, a firm linked to the state through another BOS has 2 layers or degree of connection to the state.

10
Table B.2. Results using Sample of Attrited Individuals

||_Likeliho_<br>|_od of having chil_<br>|_dren of_<br>||
|---|---|---|---|---|
||(1)<br>0years of age|(2)<br>1year of age|(3)<br>2years of age|(4)<br>3years of age|
|PEP [=1]|-0.057***|-0.017|-0.007|-0.001|
||(0.022)|(0.020)|(0.016)|(0.005)|
|Observations|880|880|880|880|
|Wave FE|No|No|No|No|
|HH FE|No|No|No|No|
|Geographic Sampling|Yes|Yes|Yes|Yes|
|Pre-migration controls|Yes|Yes|Yes|Yes|

_Notes:_ The table presents the estimates of the specification described in equation (1) but restricted to individuals who were not contacted in VenRePS round 2. Department corresponds to the five departments in which the sample was collected and geographic sampling corresponds to the four geographic levels at which the sample is representative, including three main cities and a fourth group that accounts for nine smaller urban centers with prevalent migration from Venezuela. Premigration control variables include: (i) individual controls for the head of household (gender, age, and education); (ii) labor history for the head of household (probability of being employed, type of job, probability of having a written contract, and the time gap between the last job and the migration episode); (iii) household characteristics (number of children, household size, access to public services, owning dwelling, and having a smartphone); and (
often already rich. This limits the ability to track, compare or understand education patterns in developing countries – the countries that might have the most potential to gain from human capital formation.

We bridge this gap, constructing a globally comparable database of 164 countries from 20002017, representing more than 98 percent of the global population and over two-thirds of countries included are developing countries. We document a series of motivating stylized facts in a first application of the data. First, we show that global learning progress has been relatively limited to

> 10 This does not mean that schooling is not useful, but might lead to growth largely through the channel of learning.

14
Most of the data concerning high‐skilled migration that we discuss here are taken from global bilateral migration database as described in Docquier et al. (2009), in addition to the recently completed update of the Database on Immigrants in OECD Countries (DIOC) for 2010 (Arslan et al. 2014).<sup>1</sup> These databases report the stock of migrants by education level from each home country that have moved to each destination country. High‐skilled workers, our main focus, are defined as those with at least one year of tertiary education. The data come from decennial national censuses conducted in 1990, 2000 and 2010 in the destination countries (where available) or from labor force surveys (when necessary). The data presented cover people of working age (25+) and are matched according to individual countries’ definition of migration, which typically pertains to country of birth.<sup>2</sup> Figure 1 compares migration to OECD countries along multiple dimensions. There were about 28 million high‐skilled migrants residing in OECD countries in 2010, an increase of nearly 130 percent since 1990. By comparison, the growth rate for low‐skilled migrants in the OECD countries from 1990 to 2010 was only 40 percent. (Throughout, we use the term ‘low skill’ to designate migrants with only primary education and ‘other skill’ to refer to those not classified as high‐skilled via their education.) While OECD countries constitute less than a fifth of the world’s population, these countries host two‐thirds of high‐skilled migrants (OECD Factbook 2013; Artuç et al. 2015).

The exceptional rise in the number of high‐skilled migrants to OECD countries is the result of several forces, including increased efforts to attract them by policymakers as they recognize the central role of human capital in economic growth, positive spillovers generated by skill agglomeration, declines in transportation and communication costs, and rising pursuit of foreign education by young people
81.8|90.1|
|Sant del Est|1996|199.6|*81.1|227.3|*84.4|
|Tucuman|1996|261.7|66.7|262.9|70.2|
|Average|.|199.5|**46.1|239.7|**66.1|

*The asset data for Santiago del Estero implied that their residual entity was 135% of pre-privatization assets, an

obviously unrealistic estimate. The figures reported here for Santiago del Estero are, therefore, based on their reported assets in the year of their privatization (1996). See footnote 17 for further discussion of the Santiago del Estero data. **Calculation excludes Santiago del Estero.

**Table 3: Fiscal Situation of Provinces in 1996**

|Sample|Avg. Expenditures<br>(million of pesos)|Avg. Revenues<br>(millions of pesos)|Deficits<br>(millions of pesos)|
|---|---|---|---|
|1. All Provinces|2,195.2|2,263.3|68.1|
|2. Provinces Except<br>Buenos Aires|1,075.0|1,105.2|30.2|
|3. Provinces that have<br>Privatized Provincial<br>Banks|698.8|719.9|21.2|

Source: Ministry of Economy (National Directorate for Fiscal Coordination with the Provinces)

# III THE **PRIVATIZATION PROCESS AND ITS FISCAL IMPLICATIONS**

**The** quality of the bank's portfolio, in practice, determines the size of the residual entity.

Although a poor portfolio largely reflects past performance and, therefore, **has** nothinig to do with the privatization process per se, opponents are likely to claim, at least in
14 Table **1.9.** Moscow households who privatized their dweLlings by tpe of category of housing (% of total in each group).

|Type of housing|1994|1996|
|---|---|---|
|Communal apartments|5.3|5.5|
|Separate apartments, average|40|43|
|of which:|||
|Old low-rise stock|36|46|
|Khrushchev-era buildings|29|32|
|Stalin-era buildings|65|66|
|Panel buildings (1970s design)|36|41|
|Brick buildings (1970s design)|45|46|
|Modem panel buildings|38|38|
|Modem brick buildings|63|65|
|Average for**city**|37|41|

Source: Ihe Moscow Longitudinal Household Survey in Dec.1994 and Jan-1996.

Table 1.10. Moscow households who privatized their dwellings by location of housing

||(% of total in e|ach group).|
|---|---|---|
|Location|1994|1996|
|City center|49|59|
|Near the center|49|52|
|Far from the center but not outskirts|36|38|
|Outskirts|32|36|
|Outside the Moscow ring road|36|32|
|Averagefor|_ity<br>37|41|

Source: The Moscow Longitudl Household Survey in Dec.1994 and Jan-1996.

Table 1.11. Percentage of Moscow households who privatized their dwellings by

||demogra|phic categories (% of total in each group).|
|---|---|---|
|De<br>og pic category|1994|1996|
|...................|||
|single|70.3|
index shows that a significantly higher proportion of voucher respondents compared with nonvoucher respondents were from the poorest 40 percent of households (52 percent vs. 37 percent; _P_ <0.01; see table 2). No analysis was done to place voucher beneficiaries into a national poverty scale. The wealth quintiles reported here refer only to the study population.

Table 2 Percentage Distribution of HealthyBaby Voucher and Nonvoucher Respondents, by Household Wealth Index

|Household wealth index|Voucher<br>respondents|Nonvoucher<br>respondents|All women|
|---|---|---|---|
|Poorest quintile|28.3|18.8|20.8|
|Poorer quintile|23.3|17.9|19.1|
|Middle quintile|14.5|20.8|19.4|
|Richer quintile|18.1|20.7|20.1|
|Richest quintile|15.7|21.9|20.5|
|Number of respondents|502|1,811|2,313|

_Source:_ Population Council, Household Survey 2010–11.

_Note:_ Percentages may not sum to 100 in some cases because of rounding.

**<<B>>** Knowledge of Maternal Health Services and Conditions

Awareness of the HealthyBaby voucher increased over time. In particular, among women who were interviewed in the 2008 household survey, 21 percent had heard about the HealthyBaby voucher, which was promoted on radio in late 2008 before voucher sales commenced and well before the first delivery using the voucher in late January 2009. This more than tripled to 76 percent among women interviewed in the 2010–11 survey. The proportion of women who were from villages included in both the 2008 and 2010–11 surveys and who had heard about the HealthyBaby voucher also increased from 22 percent in 2008 to 75 percent in 2010–11. In addition, 78 percent of those living in villages
# **1. Introduction**

This paper analyzes the drivers of regional poverty reduction and poverty convergence in Turkey.<sup>1</sup> First, the paper discusses imputation techniques to estimate poverty at the NUTS2<sup>2</sup> sub-national level.<sup>3</sup> Regional poverty studies at the NUTS2 level of disaggregation have not been previously available in Turkey since accessible surveys do not have sufficient regional identifiers. The study finds that Turkey has been able to reduce poverty over the last decade, but the progress has been spatially uneven. The paper moves on to estimate poverty convergence across regions. Over the recent period of 2006 to 2013, Turkey has experienced some poverty convergence in terms of its population living under the poverty line, but there is no parallel trend at the regional level. The convergence has also been uneven and inconsistent over time. Moreover, economic mobility has been divergent across the country, with higher levels of churning in the East and Southeast regions of the country. The analysis shows that people living in these regions are also subject to a higher rate of chronic poverty than the rest of the country. Lastly, the paper explores determinants of regional poverty reduction. The primary economic drivers of poverty reduction are the growth of employment in the services sector, and agriculture GVA growth in the East.<sup>4</sup> From a fiscal standpoint, central government spending is correlated with poverty reduction, but more so for the Western regions There has been consistent poverty reduction (consumption based) in Turkey since the early 2000s. Remarkably, 89 percent of this decline has been driven by growth (Azevedo and Atamanov, 2014). The bottom 40 has also benefitted from growth, and the proportion of the middle-class increased by 20 percentage points. At the household level, poverty reduction has been primarily driven by labor markets, demographics, pensions, and social assistance.

This paper, which uses imputed income based poverty for its analysis, shows
of industrial country insurers.<sup>34</sup> Further measures could involve hospitals and medical boards collecting data on outcomes and costs so that physician performance could be tracked using tiered network programs such as the ones used by insurers in the US (see, e.g., Fronstin, 2003, and Robinson, 2003).<sup>35</sup> Familiarity with doctors from certain regions could also help in removing concerns about the quality of care. For example, many British nationals reportedly seek treatments in India because they are familiar with the expertise of doctors of Indian origin in the UK.<sup>36</sup> It may be possible for US insurers to offer a health plan option, for example, where an immigrant consumer is offered the option of undergoing certain treatments in his/her home country, where she/he is familiar and comfortable with the health care system, physicians, culture.

# **8.  Conclusion**

Our study has shown how existing health insurance plans inhibit the consumption of health care abroad by consumers. Modifications of these health insurance contracts to adequately cover the costs of care received abroad would give consumers adequate incentives to undergo tradable treatments overseas. We should emphasize that we are not suggesting that insurers should force patients to seek treatments overseas in order to cut costs but that insurers should offer patients the _option_ of obtaining care overseas.

Using price data from the Medicare program and Vanbreda International, we undertook a worldwide price comparison of 15 low-risk, highly tradable surgeries. We included only non-emergency and routine surgeries, such as knee and shoulder arthroplasties, skin lesion excisions, toe-deformity repairs, and cataract extractions. The magnitude of the gains from trade in just these 15 surgeries was estimated to be over $1.4 billion, even if only one in ten US patients chose to undergo treatment abroad rather than in the US. Of these annual savings, $690 million would accrue to the Medicare program alone.

The scope for trade would be
**Figure 5. 2017 Innovation Output index vs 2016 GDP per capita**

<!-- Start of picture text --> Switzerland<br>SwedenNetherlands<br>China Ireland<br>United States<br>Germany<br>Israel Finland<br>Singapore<br>Vietnam<br>Malaysia<br>Thailand<br>Philippines<br>Indonesia<br>Cambodia<br>3 3.5 4 4.5 5<br>Log GDP per capita (PPP 2011)<br>80<br>60<br>40<br>Knowledge/Technology Output index<br>20<br>0<br><!-- End of picture text --> Source: Own elaboration based on data from GII dataset and WDI dataset. Innovation output performance is captured by the “knowledge and technology output pillar.

7
a panel data set of year/station observations.

# 2.2 Agricultural Productivity and Land Use

To measure changes in agricultural productivity, we use a satellite‐based estimate of net primary production (NPP). NPP is linearly related to the amount of solar energy that plants absorb over a growing season (Running _et al_ . 2004). There is substantial evidence that there is a strong positive correlation between satellite‐derived estimates of NPP and crop yields (Lobell _et al_ . 2002; Lu and Zhuang, 2010; Tum and Gunther 2011), and for this reason it is a frequently used proxy of yields in the economics literature (Strobl and Strobl, 2011; Blanc and Strobl, 2013; Blanc and Strobl, 2014; Zaveri, Russ, and Damania 2019).<sup>2</sup> Our time‐varying NPP data come from the moderate resolution imaging spectroradiometer (MODIS), whose data start in 2000. We use the annual MOD17A3 measures from 2000‐2013 generated by the Numerical Terradynamic Simulation Group (NTSG) at the University of Montana (Zhao _et al_ ., 2005) which corrects for cloud contamination prevalent in MODIS land products.<sup>3</sup>

> 2 A closely related measure to NPP is the normalized difference vegetation index (NDVI). NDVI when combined with growing season data of different crops can also provide a measure of plant health and physical productivity that is directly related to NPP. For instance, MODIS‐NPP is determined using NDVI along with other factors (Running et al. 2004), and in general, NDVI is considered a good predictor for NPP. However, without knowing the time‐varying distribution of crops underlying the global land cover data, we cannot accurately estimate the corresponding growing season data and therefore, cannot measure the maximum NDVI during the growing season.

> 3 The improved MOD17 by the NTSG is a post‐reprocessed data set that corrects for cloud‐contamination in NASA’s MOD17 data set.

4
model cannot determine causality, so the results should be interpreted as conditional associations, not causal relationships.

The data on flows of greenfield FDI into developing countries for the period from 2003 to 2012 are obtained from the fDi Markets database, a Financial Times databank tracking cross-border investment in new projects and expansions of existing ventures. The data are collected through Financial Times newswires, internal information and other media sources, project data acquired from industry organizations and investment agencies, and data purchased from market research and publication companies. Each project is cross-referenced against multiple sources. The data set includes 51,800 greenfield investments in developing countries, amounting to US$ 4.62 trillion. Annual FDI inflows are aggregated to the sector level of the receiving country. To test hypotheses three and four, we split 17