rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 1. Overall determinants of investment location decisions (% of respondents)**

**Question:** How important were the following factors in your parent company's decision to invest in this country?

<!-- Start of picture text --> Political stability 49% 35%<br>Macroeconomic stability 49% 36%<br>Legal & regulatory environment 42% 42%<br>Talent/skills 41% 43%<br>Low taxes 41% 35%<br>Market size 39% 43%<br>Physical infrastructure 39% 41%<br>Ability to export 38% 32%<br>Intellectual property protections 35% 35%<br>Investor protections 35% 38%<br>Low labor & input costs 33% 41%<br>Supply chain coordination 30% 44%<br>Local input sourcing 27% 42%<br>Resource endowments 18% 32%<br>Local acquisition targets 14% 36%<br>0% 20% 40% 60% 80% 100%<br>Critically important Important<br><!-- End of picture text --> _Source_ : Computation based on the 2019 GIC Survey.

# _Heterogeneity across investor types_

More export-intensive respondents place lower importance on host countries’ legal and regulatory environments. In the preferred ordered logistic specification, the coefficient on export percentage of sales is negative and statistically significant at the p<0.05 level. This finding is robust to the exclusion or inclusion of country and subsector fixed effects as well as alternative specifications using OLS (Table 11, Column 8). A 10-percentage point change in the portion of sales from export is associated with a 1- percentage point increase in the likelihood of considering legal and regulatory environments to be ‘critically important’ (Table 12). Although further research is required to explain this pattern, this coefficient may reflect exporters being less subject to regulations governing sales within their
# **Appendix 1: Data and Statistical Appendix**

# **Sample**

The study uses a sample of 104 surveys from the September 2016 vintage of the Global Micro Database (GMD); only surveys between 2009 and 2014 are selected. The sample is composed of 89 developing countries. The GMD sample is lined up to represent the estimates of extreme poverty in 2013. When a survey of 2013 is unavailable, two surveys are selected, one before and one after 2013, and their results are weighted according to the relative distance to 2013 (Castañeda et al, 2016); if only surveys before 2013 are available, we use the latest available. The countries and years selected are showed in Table A1.

|**Table A1: Developing countries and survey years in GMD sample**|
|---|

|**Countries (89)**|**Survey years**|
|---|---|
|Armenia, Bulgaria*, Chile, China, Czech Republic, Dominican Republic*, Estonia,<br>Georgia*, Honduras*, Croatia, Hungary, Kazakhstan*, Kosovo, Lithuania, Latvia*,<br>Moldova, Montenegro*, Pakistan*, Poland*, Romania*, Serbia, Slovak Republic,<br>Slovenia*, Ukraine (24)|2013 only|
|Argentina, Bolivia, Brazil*, Colombia, Costa Rica*, El Salvador, Ecuador,<br>Mexico*,Paraguay*,Peru*, Uruguay, Vietnam(12)|2012 and 2014|
|Guatemala*,Indonesia (2)|2011and2014|
|Nicaragua (1)|2009 and2014|
|Albania*, Bhutan, Democratic Republic of the Congo*, Djibouti, Guinea*, Haiti,<br>Kyrgyz Republic, Cambodia, Lao PDR, Mongolia, Mauritius*, Panama*,<br>Philippines,Russian Federation, Sri Lanka*,Thailand,Turkey, Uganda*
6 300<br>.4 200<br>.2 100<br>0 0<br>Q1 Q2 Q3 Q4 Q5<br>Per-capita expenditure quintile<br>2035 current percentage change in CO2 emissions<br>Percentage change<br>CO2 emissions level (metric tons per capita)<br><!-- End of picture text -->

# **6 Conclusion**

This paper aims to understand how economic development impacts transport demand and the consequences for carbon emissions from the transport sector. Using household surveys data, we estimate the determinants of transport demand. We find that transport expenditures and vehicle ownership will increase significantly as households and countries get richer. As countries get richer, households will increase their relative spending on transport, be more likely to own a car, and switch from public to private transport mode. In poorer countries, a large share of households, currently not spending on transport, will start using transport services or want to own a vehicle. The demand for private vehicles is therefore expected to increase significantly as countries get richer. These results suggest a future steep growth of emissions as incomes expand. According to our simulations, higher predicted 2035 incomes will increase vehicle ownership and usage, leading to an average increase of 52% in transport CO2 emissions for our sample of 18 countries. Finally we show that the distribution of emissions across income groups is highly concentrated among the richest households. This concentration is expected to slightly decrease for the whole sample of countries, increase in some countries, but decrease in others.

41
Figure 7. Colima, Veracruz, Guanajuato, and Chiapas show the highest negative net migration balances _Net internal migration balance by states_ <!-- Start of picture text --> 200000<br>Men Women<br>100000<br>0<br>-100000<br>-200000<br>-300000<br>Source: Own estimates based on Population and Housing Census 2020. Abbreviation codes in Annex 1.<br>Figure 8. There is a high correlation between net internal migration flows and labor market outcomes<br>16000<br>14000 COA<br>BCS<br>12000 SON<br>COL NL<br>10000 BC<br>80006000 VER TABMEX SLPMICHZACCAMDGOSINTAMNAYCHHGROCDMXTLAPUEMORAGCJAL HGOYUC QROOQRO<br>GTO OAX<br>4000 CHS<br>2000<br>0<br>-250,000 -200,000 -150,000 -100,000 -50,000 0 50,000 100,000 150,000 200,000<br>Net internal migration flow (immigration minus emigration)<br>emigration)<br>(immigration minus<br>Net internal migrant flow NL BC QRO QROO YUC HGO COA JAL BCS AGC MOR PUE TLA CDMX GRO CHH NAY SON TAM SIN DGO CAM ZAC MICH SLP OAX MEX TAB CHS GTO VER COL<br>income<br>(Mexican pesos)<br>Average monthly labor<br><!-- End of picture text --> Figure 8. There is a high correlation between net internal migration flows and labor market outcomes Source: Own estimates based on Population and Housing Census 2020. Abbreviation codes in Annex 1.

# 4.3. Reasons for migration

## **Reasons for migration statistics are essential to distinguish between labor migrants and individuals**

**moving for other reasons and to understand the push and pull factors driving migration decisions.** Depending on the prevailing reasons, specific policy options can be formulated to
# **E Reason for Not Registering in RAMV**

Table E.1. Reason for Not Registering in RAMV

||Non-RAMV|Non-RAMV|
|---|---|---|
||Referrals|Organizations|
||(1)|(2)|
|Did not know about RAMV|51.23%|51.71%|
|Was not in Colombia|19.51%|23.43%|
|No proof of Ven. nationality|11.16%|9.71%|
|Was working|8.23%|5.24%|
|Too expensive or complicated|5.99%|5.71%|
|Fear of deportation|2.59%|2.38%|
|Other|1.29%|1.81%|

Table E.2. Reason for Not Having PEP

||Non-RAMV<br>Referrals<br>(1)|Non-RAMV<br>Organizations<br>(2)|RAMV<br>NO PEP<br>(3)|
|---|---|---|---|
|Lack of information or misinformation|58%|58.71%|29.93%|
|Did not register in the RAMV census|11.04%|11.23%|0.00%|
|Was not in Colombia|10.70%|12.75%|2.19%|
|RAMV registration got lost|6.19%|7.71%|23.36%|
|Did not plan to stay in Colombia|5.18%|3.43%|0.00%|
|Did not have access to Internet|1.80%|1.52%|5.84%|
|Did not need it|0.45%|0.57%|0.
link individuals across these three data sets, preventing us from connecting individual enrollment decisions or household characteristics with test results. We therefore use district and school characteristics to analyze treatment effect heterogeneity. At the district level, we construct three measures using the 1998 IHS. We define “low enrollment” districts as those that fell below the national median enrollment rate for secondary school-aged children (ages 13–18).<sup>10</sup> “Rural” districts are above the median percentage of population living in a rural area. “Wealthy” districts are above the median level of average household assets, with assets measured as the first principal component of ownership dummies for bicycle, car, refrigerator, motorcycle, sewing iron, television, radio, and VCR. At the school level, “distant” schools are located beyond the median distance from a main road. Importantly, all of these characteristics are predetermined with respect to the policy. All are also dummy variables, allowing for easy interpretation of interactions with the treatment indicator.

The nature of the scholarship program poses a potential challenge regarding data quality. Because the Ministry of Education remitted fees to schools for each girl enrolled, schools have an incentive to over-report enrollment in order to attract more resources (Sandefur and Glassman 2015).<sup>11</sup> Our reliance on exam records alleviates this concern, because the examination body WAEC is independent of the Ministry and requires separate student registration rather than automatically enrolling those on school rosters. The Ministry 9
are the budget shares of food and nonfood items (excluding rent) for the food price index and the budget share of rent for the rent index. The formula of the overall index is expressed below:<sup>14</sup>

- where : median price of food item observed from individual households in the urban Southeast : median price of food item paid by households in primary sampling unit

- pp<sup>0,ii</sup> mm : rental value paid by or implicit rent for household<sup>PP,ii</sup> h

- pp<sup>P</sup> mm FFP : median rental value paid by or implicit rent for individual households in the urban Southeast

- cc<sup>h</sup> : share of household h ’s food budget devoted to food item cc<sup>0</sup> h : share of household h ’s total budget devoted to food and nonfood, nonrent expenditures

- h,ii

- ww mm : share of household h ’s total budget devoted to rent

- FF,NNFF

- ww rr<sup>h</sup>

- ww

Each household’s welfare aggregate was then divided by the household-specific overall price index. Table 3 presents the average indices by region. Compared to the Southeast-Urban reference, prices are lower in all other regions. Differences are smallest for South-Urban price indices and larger for price indices in the Northeast and North. These results are in line with differences in economic performance across the regions. Moreover, within major geographic region, urban-rural differences are as expected, with urban price indices being higher. Differences in food prices are relatively smaller. This result is in line with earlier work on Brazil and Brazil’s level of economic development (World Bank, 2007; Deaton and Dupriez, 2011). The largest differences in terms of urban-rural as well as regional differences in prices are observed in the cost of rent.

Finally, using the spatial and temporal deflated
# **3.2. Empirical approach**

The main question we attempt to address is whether, and how, the CPI price data is useful for spatial price adjustment in poverty measurement, given the importance of detailed product quality information. To empirically examine it in this case study of Ghana, we compare the results of various spatial price measurement methods. The main methods we test are (a) the bilateral/multilateral price index approach, (b) the CPD approach, and (c) the Engel curve approach. The main data sets include (a) the market price survey data collected in parallel to the household budget survey and (b) CPI raw price data. The Engel approach requires no price data.

## _Price index approach_

We first calculate several bilateral price indexes, such as the Paasche index (Equation 1), Laspeyres index (Equation 2), and Fisher index (Equation 3), as described in Section 2.1, and compare the spatial price patterns indicated based on each approach. We also compare these results with those based on a multilateral price index (GEKS-Fisher index in Equation 8) to assess how transitivity (or lack thereof) influences the results.

## _CPD approach_

We examine the two types of CPD methods. The first approach is to use the weighted CPD (Equation 12) to calculate the multilateral price index by applying it to the item-level price data for which budget share information is available as the weight. While we apply both variable weights (Equation 13) and fixed weights (Equation 14), following Gibson, Le, and Kim (2017), we report only the results based on variable weights, as no substantial difference is found in the results depending on the weights.<sup>21</sup> The result of this approach is compared with a multilateral price index calculated by a GEKS-Fisher index method. Of particular interest in this comparison is how handling missing values by the CPD approach affects the spatial price measures.

#
the models also account for regional fixed-effects in addition to the local quality of institutions described above. At the country level and in addition to the measurement of decentralization, the empirical models include proxies for the overall corruption control with data from the World Governance Indicator and the share of natural resources rents as a percentage of GDP from the World Development Indicators.

**4. Empirical Strategy** Equation (1) is our baseline model. We denote by i i the response of an individual **_i_** living in locality of country and which has been interviewed in survey round **.** Depending on the yy specification, the variable i i indicates the assessment of how _well_ or _badly_ does the government ii ii ii handle public services or the expectation of an individual regarding his/her future living yy conditions. **_NR_active_** is a binary variable that takes the value of _1_ if the respondent is located

> 2 See Appendix B for additional details

12
of the self‐employed is greater than that of employed workers, and as a result the self‐employed are subject to a higher progressive income tax schedule.

# 2. Quantitative Analysis for TIETP

## Methodology and Data

TIETP’s effects were analyzed using micro‐simulation methods through counterfactual analysis adopted by Song and Sung (2012). This study updated the Song and Sung (2012) approach, using 2014 the HIES data set. Three different scenarios were set up to evaluate policy effects by establishing the counterfactual and the observable. When estimating true business income in each scenario, income data obtained from HIES were adjusted under the assumption that they were also underreported, as in tax reporting.<sup>52</sup> Real business incomes were estimated by

> 52 . Hurst, Li, and Pugsley (2014) showed that in U.S. household surveys the self‐employed systematically underreport their income by about 25 percent.

35
<mark>consumption aggregate has three main components: (i) The Aggregate Nominal Consumption Expenditure: which includes all food and non-food expenditures of the household converted into the same time unit, for instance annual, monthly or weekly expenditures, (ii) The Cost of Living Index (COLI): a price index used to adjust for the cost of living differences across space and time, and (iii) The Equivalence Scale: to adjust for differences in the size and age composition of households.</mark>

#  Aggregate Nominal Consumption Expenditure

The consumption aggregate includes all food and non-food expenditures that are incurred on a recurrent basis, converted to annual expenditures. NISR elicits both cash and in-kind expenditures for food items directly purchased in the market, self-produced and consumed, received as wages or salaries, or received as gifts or assistance. The monetary value of all these expenditures is converted to a common time unit (year) and then aggregated to arrive at total consumption expenditure. Prior to aggregation, outliers in consumption are removed using region-level mean and standard deviation. In that, all observations that are 3.5-standard deviations above the mean are considered outliers and are replaced by the ‘mean’ value. These ‘adjusted’ values are then used to arrive at final annual consumption expenditure (NISR 2015).

The key non-food items included in the consumption aggregate include expenditures on clothing, footwear, housing, education, health, fuel, utilities, transport, recreation, and communication. Payments received by employees in kind and subsidized housing benefits are also included. Another important component of nonfood expenditures is house rent, where the actual rent for renters and self-reported rent for owners is included. Lastly, estimated consumption flows derived from durable goods (based on their current value and rate of depreciation) is added to the consumption aggregate (NISR 2015).

#  Cost of Living Index

As the cost of basic food and non-food needs varies across regions within a country, the aggregate nominal expenditure needs to be adjusted for spatial
# **3 Data and Summary Statistics**

We use UN Comtrade data that record bilateral exports in 5203 HS6 products. We aggregate into biennial (24-month) intervals (2014/2015; 2016/17; 2018/19), and refer to each 24-month period by its ending year. We restrict our sample to the top 50 exporting countries, excluding oil exporters. The resulting sample covers 95.9% of global trade (or 70.5% excluding US and China). We analyze exports from each of these countries to three destinations: the United States ( _US_ ), China ( _CH_ ), and the aggregate of all destinations except the US and China ( _RW_ ). We classify products into nine sectors: agriculture, apparel, chemicals, materials, machinery, metals, minerals, transport, and miscellaneous. Figure A.1 reports countries’ export shares by sector prior to the trade war.

We consider four sets of tariff changes as part of the US-China trade war: i) imposed by the US on China (the “US tariffs”), denoted as _TCH_<sup>_US_</sup> , _ω_<sup>, where</sup><sup>_ω_denotes an HS6 product code; ii) imposed</sup> by China on the US, _TUS_<sup>_CH_</sup> , _ω_<sup>(the “China tariffs”); iii) imposed by the US on each country</sup><sup>_i_other than</sup> China, _Ti_<sup>_US_</sup> , _ω_<sup>(e.g.,steeltariffsonMexico);andiv)most-favored-nation(MFN)tariffsimposedby</sup> China on all countries but the US, _Ti_<sup>_CH_</sup> , _ω_<sup>.Bown et al. (2019) argue that China’s MFN tariff cuts were</sup> likely influenced by the trade war with the US
# 3 Data

We employ a balanced dataset, from COMTRADE, covering the trade of 42 countries at the SITC rev. 2 3-digit level of manufacturing industries from 1986 to 2001. The sample consists of 28 OECD countries and 14 developing countries that are the largest manufacturing exporters outside the OECD (and have complete sectoral import data).<sup>17</sup> Inevitably, there exist some null observations since not all countries trade in each industry every year, and so it is appropriate to use the Tobit model for estimation.

We identify the industries, countries and the time period that are a¤ected by each directive or MRA drawing upon the relevant o¢ cial documents, and then construct the variables displayed in Table 1. The harmonization directives and MRAs are not directly related to speci...c products but to product attributes.<sup>18</sup> Thus a single industry (e.g. electronic products) may be a¤ected by multiple directives (e.g. those pertaining, respectively, to low voltage equipment and electromagnetic compatibility). Di¤erent approaches can be taken to quantify the harmonization measures, depending on how these measures are believed to a¤ect trade. It is simplest, and seems reasonable, to assume that the impact is linearly related to the number of directives in each industry, i.e. each additional directive in any industry has an identical incremental a¤ect on trade. Alternative assumptions are possible, e.g. that additional directives have a diminishing e¤ect on trade, or that the nature of the impact di¤ers in each industry. We established that the results are not sensitive to the choice of approach.

HARijrt counts the number of directives applicable to industry r between exporter i and importer j in year t. HAR_Mijrt counts the number of directives applicable to industry r between importer j and any country other than exporter i in year t. For instance, HAR_Mijrt = 1 for imports of United Kingdom
7 input prices and other factors. The next sections explore in more detail the underlying structure of one such model and its input requirements.

# **III. The FCC Approach**

The Federal Communications Commission (FCC) of the U.S.A.<sup>7</sup> has analyzed the use of forward-looking economic cost methodologies as the basis to determine the universal service cost, and it has concluded that the models that are being used are promising regulatory tools. The major actors in the telecommunications industry in the U.S.A. submitted their own versions of forward-looking models to the FCC for the Commission to analyze them. These models are the _Cost Proxy Model_ (CPM) of Pacific Blue and INDETEC International; the _Benchmark Cost Model 2_ (BCM2) of Sprint and US West; and the _Hatfield Model_ (Hatfield) in different versions, of AT&T and MCI. The FCC developed and adopted a model as an alternative to the models proposed by the industry players. The FCC model, known as _Hybrid Cost Proxy Model_ (or HCPM), combines appropriate principles of engineering design for different network elements with economic principles of cost minimization.<sup>8</sup> The model draws freely from engineering principles displayed in other models, thus the term 'hybrid'.

In what follows, we describe the most relevant features of the cost proxy model approach. We give an overview of the HCPM and leave the discussion on input requirements and data problems to the next section.

## **Structure of the HCPM**<sup>**9**</sup>

The 14CPM has from the beginning been designed to use sources of geocodedl' customer location data. The model can also handle Census block level data as an alternative source of publicly available data. The last version of the FCC model incorporates some minor modifications to improve the performance of the model under 7See Atkinson et al. (1997). ' See Bush et al.
by assuming that good health means the absence of disease and bad health means the presence of disease. To measure latent health status, the ECA-HCI focuses on the factors that may cause disease. A low prevalence of these risk factors implies a lower disease burden; a high prevalence could imply a higher disease burden. The risk factors that are relevant as indirect measures of latent health status depend on the types of disease prevalent in each context. Smith and Nguyen (2013) show that in Europe and Central Asia, cardiovascular disease, followed by external causes (mainly alcoholrelated road traffic injuries), explains most of the differences in adult life expectancy. Data from the COVID-19 pandemic also show that people with underlying cardiovascular conditions have a higher mortality rate than people without them (Wu and McGoogan 2020; Zhou and others 2020). In view of these findings, the ECA-HCI uses the prevalence of three health risk factors associated with cardiovascular disease: obesity, tobacco smoking, and heavy alcohol consumption. The higher the prevalence of these risk factors, the higher the probability of disease and the worse the health status. The prevalence of these risk factors increases the probability of suffering from noncommunicable diseases and increases the mortality and morbidity consequences of some infectious diseases like COVID-19. The health benchmark in the ECA-HCI with which countries are compared is zero prevalence of obesity, smoking, and heavy drinking.

The impact on productivity of specific health conditions is difficult to estimate. There is more evidence on the productivity effects associated with the risk factors behind such health conditions. The literature has quantified the effects on productivity of obesity, tobacco smoking, and heavy drinking, making it possible 17