rafmacalaba/data-use-mentions-extended huggingface dataset:
<!-- Start of picture text --> Electricity Aggregation Land Supply<br>aggregated electricity sugar soybean other crop<br>forest livestock<br>wind<br>hydro nuclear<br>coal gas oil biomass land endowment<br><!-- End of picture text -->

**Figure 1 (Continued).** Utility and Production Functions

# **III  DATA**

The data needed for the analysis can be classified into three types: 1) the Brazilian inputoutput and national accounting data to construct the Social Accounting Matrix (SAM); 2) the sectoral energy consumption and CO2 emissions data for defining emission coefficients from fuel consumption; and 3) the elasticities of substitution that characterize how different inputs can be substituted by other inputs, and the elasticities of transformation that represent how different outputs can be converted to other outputs.

Based on Brazil‟s national accounting data for 2007 and the approach developed by Guilhoto and Sesso (2004), Pereira (2010) builds a 25-sector SAM for Brazil. An aggregated version of SAM is presented in **Table 1** , and the 25 industrial sectors are presented in **Table 2** . Further, following some existing studies such as Timilsina and Shrestha (2007), Pereira disaggregated the electricity sector into 7 subsectors including electricity generations from hydro, nuclear, coal, gas, oil, wind, and biomass. The biomass generation uses bagasse, the by-product from the sugarcane industry, as the feedstock and is assumed to be carbon neutral as hydro, nuclear, and wind generations. Thus, this study can better address the distinct impacts of achieving the goal of low-carbon growth on various generation technologies with different carbon footprints. The 13
its long‐term Hodrick‐Prescott filtered trend. Similarly, episodes of investment slowdown are defined as years in which the investment‐to‐GDP ratio is at least one standard deviation below its Hodrick‐Prescott filtered trend.<sup>27</sup> Investment surges in AEs occurred with credit booms more often than in EMDEs, with a more rapid rise in investment. In EMDEs, about 40 percent of credit booms were accompanied by investment surges around the peak year of a credit boom (Figure 1.2). More than 65 percent of investment surges that coincided with credit booms during the peak year qualified as investment booms in advanced economies, but only 56 percent of such investment surges turned out to be investment booms in EMDEs.

After the global financial crisis, the coincidence between credit booms and investment surges during the peak year of a credit boom dropped significantly (Figure 1.2). By 2007, about half of the EMDEs in a credit boom were also in an investment surge. However, from 2010 onwards, there is virtually no EMDE that was both in a credit boom and in an investment surge. The number of EMDEs in a credit boom increased from two in 2010 to ten in 2015 (Azerbaijan, Bolivia, China, Cote d’Ivoire, Kenya, Kuwait, Oman, the Philippines, Qatar, and Turkey) while the number of EMDEs in investment surges dropped from eight to four.<sup>28</sup> In AEs, both the number of countries in a credit boom and the number of countries in an investment surge fell from around five to almost zero.

> 26 The directly affected EMDEs include China, Indonesia, Malaysia, Mongolia, the Philippines, and Thailand.

> 27 The results are similar when investment growth, instead of the investment‐to‐GDP ratio, is used.

> 28 The four countries are Colombia, Namibia, the Philippines, and Saudi Arabia. The identification of Saudi Arabia is not supported by investment growth data
for tour guides and language teachers from LDCs under the LDC Services Waiver, wherein the objective is to provide unilateral preferences to semi- and unskilled Mode 4 laborers in LDCs. While the first channel would classify as trade liberalization, channels two and three would constitute trade facilitation.

Using the data collected by the OECD for 45 countries as a part of their STRI database, Singh (2019) analyzes the nature and characteristics of services trade facilitation-related measures. Distinct from the measures _prima facie_ restrictions such as licensing systems, onerous visa requirements, or labor market tests, the OECD STRI data also includes 16 measures that relate to providing regulatory transparency for MNP. This is illustrated by discussing various attributes related to business visa applications, all of which translate into costs for service suppliers (see table 6). Singh (2019) finds that the average cost of obtaining a business visa is slightly higher at US$87 for logistics services compared to the sample average of US$84 for the countries covered by the OECD STRI database; for all the other sectors these monetary costs are lower than the sample average.

Similarly, in 2017 the average number of documents needed to obtain a business visa was 9 (the OECD average is a little higher at 10 and the non-OECD lower at 7). Nineteen of the 45 countries were found to have the number of documents required greater than the average, with Austria and France both needing the most at 16 and the UK the least at 2. At the same time, there is considerable heterogeneity in the types of documents that are required to be submitted in support of a business visa or work permit applications. The visa processing time (in number of days) in 2017 varied from as high as 32 for Canada and as low as 1 for Japan. The average for the sample was found to be 13, with the OECD average higher at 15 and the non-OECD lower at 9. Interestingly, 21 of the 45 countries (all OECD) needed 15 days on average to process a
<!-- Start of picture text --> d. Government Take (taxes plus<br>c. Maintenance costs dividends)<br>200 200<br>150 150<br>139<br>100 100<br>50 50<br>20<br>4 7 12 5 5 12<br>0 0<br>Power Airlines and airports Roads Railways Power Airlines and airports Roads Railways<br>Percent of revenues Percent of revenues<br><!-- End of picture text --> _Source_ : Authors, based on data from the World Bank Infrastructure SOEs Database. _Note_ : Data are for 2009–18.

As a share of revenues, employment costs are higher at fully owned SOEs than at partially privatized companies (figure 7). In all four sectors, employment costs at partially privatized firms represent less than 22 percent of revenues, a figure that is in line with that of private firms around the world. A plausible explanation of the large differences between fully owned and partially privatized SOEs is that fully owned SOEs in the infrastructure sector are heavily unionized and have less flexibility to reduce their labor force during downturns. Employment costs thus increase during good times and remain high during bad times. Politicians also use some SOEs as employment vehicles, creating massive companies whose financial performance deteriorates over time.

13
The mean and the median relative errors (which are same for population density and count predictions) are reported in columns (7) and (8), respectively.<sup>23</sup> The mean and median relative errors of the model are 37% and 28%, and 35% and 29%, without and with the additional weight correction. Using only public imagery, the mean and median REs are 34% and 28%, and 34% and 28% without and with weights correction, respectively. These mean REs are lower than most top-down estimates, except Facebook and WorldPop 2015. The median REs are higher than only Facebook and Worldpop 2015, and comparable to GHSL and GPW 2015 and much lower compared to GPW, Worldpop, and Landscan (all in 2010).<sup>24</sup> The coefficient of variation (CV) of predicted density estimates for each sub-district based on the HIES model in combination with satellite imagery is low, at about 11%. This is four times lower than the CV of the survey estimates themselves, which stands at 45.2%. The increase in precision due to the incorporation of satellite imagery is tantamount to increasing the share of sampled villages in the population from 17%, which is currently the case, to a remarkable 80%.<sup>25</sup> This is striking but not surprising because the model predicts with an out-of-sample _R_<sup>2</sup> of about 0.80 and the imagery covers all villages.

# **5 Discussion and Conclusion**

Existing methods of estimating local population use a “top-down” dasymetric mapping approach to distribute census data based on a set of covariates derived from satellite imagery. These are only as accurate as the source data—the census—and hence their accuracy declines as the census itself ages. We propose a “bottom-up” technique by pairing survey data with satellite imagery using Poisson regressions employing variables selected based on LASSO regularization.
<!-- Start of picture text --> 900 12%<br>800<br>10%<br>700<br>600 8%<br>500<br>6%<br>400<br>300 4%<br>200<br>2%<br>100<br>0 0%<br>1 2 3 4 5<br>Wealth Quantiles<br>Average Income Electricity Subsidy as % of income<br>Water Subsidy as % of income Electricity + Water subsidy as % of income<br>Percentage from Household Income<br>Household Average Monthly Inomce (USD)<br><!-- End of picture text -->

**Figure 6. Electricity and water subsidies as percentage of income by wealth quintiles (households with private connections Addis Ababa, 2016)**

Households in all five wealth quintiles pay between 2% and 3% of their income for their combined electricity and water bills. Most of this expenditure is for electricity. Across all five wealth quintiles, monthly water bills are equivalent to only about 0.3% of income. Households in the poorest wealth quintile receive combined subsidies amounting to about 7% of their income, a value that increases to more than 9% for households in the second-poorest quintile. This percentage falls to about 5% for households in the richest wealth quintile.

# **6. Concluding remarks**

Ours is the first paper to examine the combined effects of IBT structures for both electricity and water on a given set of households in a city of the Global South. It is also the first to match data on water 25
that this came at the cost of reducing overall investment and installing less productive capital. After providing very rich descriptive statistics on in-house electricity generation in 25 Sub-Saharan African countries, Foster and Steinbucks (2009) estimate that the weighted average costs of power own-generation for large firms remains relatively small and that the main victims are both existing informal firms and the formal ones that were not created as a results of the prevailing constraints. They also allude to the potential benefit of allowing firms with generation capacity to resell power into the national grid. Steinbucks (2008) uses firm-level data from Sub-Saharan African countries
) of the WTO, which provides improved information on members’ notifications of NTMs and other sources of WTO-specific information on non-tariff measures. Yet another initiative is called PRONTO, which brings together a number of academic and policy institutions with the goal of improving data availability and methodologies to assess the trade impact of NTMs.

# **2.2.2 Some stylized facts on NTMs**

> 8 The ITC database on NTM business surveys is accessible at http://ntmsurvey.intracen.org/ntm-survey-data/. The European Commission’s database on trade barriers faced by exporters from the European Union is accessible at <u>http://madb.europa.eu/madb/indexPubli.htm.</u>
# **Figure 2:** Households with Access to the Internet

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>%<br>KSACANKORSWEBELNDLUSAJPNDEUAUSMYSCHLMLTURYKAZARGRUSFRAHUNAZEMARPOLBRACRIROUHRVTHATURDOMPRYMEXEGYTUNCHNVNMZAFCOLPERECUPANBWADZABOLGHASLVIDNPHLHNDINDHNDKENRWAPAKBGDETH<br><!-- End of picture text --> Source: ITU data, author calculations.

29
to another destination within West Africa (UN DESA, 2020). Such movements have serious development and political implications and are currently animating public debate in many developed and developing countries, bringing renewed attention to the study of migration both in the scientific research community and among policy makers.

Despite this revamped interest, however, systematically measuring migration and its underpinnings remains a challenge, as also highlighted in the recent Global Compact for Safe, Orderly, and Regular Migration.<sup>3</sup> This is due to multiple factors but, above all, is the result of a paucity of accurate and regular data on migration, especially in low-income settings, and difficulties in reconciling information collected at different temporal and spatial scales regarding potential drivers and other important contextual factors (Parrish et al., 2020).

In 1990 the IPCC argued that the biggest impact of the changing climate might be on human displacement. Since then, the role of climate change in driving – directly or indirectly – migration has been progressively

> 1 Throughout this paper, the term “migration” is used in a broad sense, referring to all people who have moved away from their home (or usual place of residence) to live temporarily or permanently in another area in the same country or in another country, unless otherwise specified (e.g., internal migration, international migration, etc.).

> 2 Based on UN DESA data on international migrant stock in 2020, 65 percent of international migrants worldwide have high-income countries as their destination. In terms of origin, 65 percent of all immigrants in high-income countries come from middle-income countries, 25 per cent from other high-income countries, and only 6 percent from lower-income countries.

> 3 See: https://www.iom.int/resources/global-compact-safe-orderly-and-regular-migration/res/73/195.

2
Policy Research Working Paper 10466

# **Abstract**

Despite a vast literature documenting the harmful effects of climate change on various socio-economic outcomes, little evidence exists on the global impacts of hotter temperature on poverty and inequality. Analysis of a new global panel dataset of subnational poverty in 134 countries finds that a one-degree Celsius increase in temperature leads to a 9.1 percent increase in poverty, using the US$1.90 daily poverty threshold. A similar increase in temperature causes a 0.8 percent increase in the Gini inequality index. The paper also finds negative effects of colder temperature on poverty and inequality. Yet, while poorer countries—particularly those in South Asia and Sub-Saharan Africa—are more affected by climate change, household adaptation could have mitigated some adverse effects in the long run. The findings provide relevant and timely inputs for the global fight against climate change as well as the current policy debate on the responsibilities of richer countries versus poorer countries.

This paper is a product of the Development Data Group, Development Economics and the Poverty and Equity Global Practice. . It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at hdang@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments
I POLICY RESEARCH WORKING PAPER 2412

# Summary findings

To find out why African countries' experience with urbanization and sustained growth appeared to differ from that of other countries, Fay and Opal investigated the determinants of urbanization across countries over 40 years.

Rather than studying individuals' decisions to migrate, they relied on macroeconomic data and cross-country comparisons. A central hypothesis of their study: that individuals move (with varying degrees of ease) in response to economic incentives and opportunities. If location incentives are distorted, so is growth.

The authors find that urbanization levels are closely correlated with levels of income. But urbanization continues even during periods of negative growth, carried by its own momentum, largely a function of the level of urbanization. From that viewpoint, Africa's urbanization without growth is not a puzzle.

Factors other than income that help predict differences in _levels_ of urbanization across countries include:

- Income structure.

- Education.

- Rural-urban wage differentials.

- Ethnic tensions.

* Civil disturbances.

In addition, the relationship between economic incentives and urbanization is weaker in countries with fewer civil or political liberties.

Factors other than initial urbanization level that help explain the _speed_ of urbanization include:

* The sector from which income growth is derived.

- Ethnic tensions.

* Civil disturbances and democracy (these two slow the pace of urbanization if all else is constant).

* Rural-urban wage differentials, whether they represent an urban bias or simply lower productivity in agriculture relative to other sectors.

The weak relationship that this study shows between urbanization and traditionally accepted migration factors suggests that in Africa economists are overlooking part of the urbanization story. The fact that the informal sector appears to provide a significant source of income for urban migrants, coupled with the overlap between rural and urban activities, may shed light on the nature of urbanization in Africa.

This paper-a product of the Urban Development and Transportation Division, Private Sector and Infrastructure Vice Presidency-is part of a larger effort in the Bank to develop its urban economics and strategy work program. Copies of the
_2. THE BELT AND ROAD INITIATIVE_ khstan, real wages will grow 5 times more in the locations around Almaty than in the northern locations at the border with the Russian Federation.

2. Complementary investments in trade facilitation accentuate economic gains around hubs while investments in domestic transport networks help in spatially spreading the benefits.

3. Barriers to domestic labor mobility exacerbate spatial inequalities whilst dampening overall welfare.

4. Domestic policies and investments that support spatial mobility of labor and internal connectivity can mediate potential spatial efficiency equity tradeoffs within countries.

The rest of the paper is organized as follows. In Section 2, we provide details on the Belt and Road Initiative investments and the countries of interest. In Section 3, we describe our modelling strategy to develop economic geography counterfactuals of the BRI in Central Asia. We discuss results on BRI winners and losers, and discuss risks from spatial inequalities in Section 4. Section 5 concludes.

# **2 The Belt and Road Initiative**

The Belt and Road Initiative roughly follows and expands the old Silk Road on the land side and complements it with a maritime part to build a series of economic corridors across Asia, Europe and Eastern Africa. The range of initiatives is very wide, including policy coordination, infrastructure, trade and investment, financial and people-to-people exchanges. In this paper, we focus on the consequences of transport infrastructures linked with the Belt and Road Initiative. Given that there is no official list of projects and countries in the Belt and Road Initiative, we rely on the list created by the World Bank (Figure 1, de Soyres et al. (2018)).

## **2.1 What is the Belt and Road Initiative?**

**Main corridors** The BRI is organized around six economic corridors: (1) the China-MongoliaRussia Economic Corridor; (2) the New Eurasian Land Bridge; (3) the China-Central AsiaWest Asia Economic Corridor; (4) the China-Indochina Peninsula Economic Corridor; (5) the China-Pakistan Economic Corridor;
2010s. The DSM approach was piloted by the Integrated Seed Sector Development (ISSD) initiative, a project led by Wageningen UR, in two districts of the Amhara region. In 2012, seven additional districts were brought under DSM, while the two original districts in Amhara briefly suspended the program in 2012 but resumed it in 2013 (Mekonen et al. 2019). In 2013, the ATA and MoA expanded the approach to cover additional districts.

The DSM rollout coincided with three rounds of the Ethiopian Agricultural Commercialization Clusters (ACC) survey, the main data source used in this study (and described in the next section). The first ACC round was conducted in 2012, followed by a second round in 2016 and a third in 2019. By 2016, the number of DSM districts had reached 100, and by 2019, it had expanded to 290 across Ethiopia’s four main agricultural regions (Amhara, Oromia, Tigray, and the Southern Nations, Nationalities, and Peoples (SNNP) regional state), covering about 43 percent of Ethiopia’s 670 rural districts. Only one district from the initial DSM rollout in 2011-12 was included in the 2012 ACC survey round. Due to the lack of pre-intervention data, this district was excluded from the analysis.

The number of crops covered by DSM increased from one crop (maize) in 2011 to ten crops in 2019 (barley, chickpea, faba bean, horse bean, lentil, maize, sesame, sorghum, teff, and wheat), even though maize and wheat represented 98 percent of seed marketed through DSM. DSM has also grown considerably in the number of private agents (individual input dealers) participating in seed marketing over the years, from 29 in 2012 to about 1,400 in 2019 (Table 1). The approach continues to be used today.

6
Source: Own estimates based on Turkey 2016 HBS. Note: changes calculated from Market income plus pensions to Disposable income.

Panel (a) of Figure 8 shows the Kakwani coefficient and the marginal contribution to inequality of all direct taxes in Turkey in 2016. In general, direct taxes are progressive; the Kakwani coefficient for most of the direct taxes is positive. They are also broadly inequalityreducing as shown by a positive marginal contribution to the Gini coefficient. However, there is substantial heterogeneity across tax categories. For instance, payroll income tax is progressive and the most inequality-reducing, while agricultural income tax is regressive and does not contribute to reduce inequality.

Relative to its peers, the equalizing effect of direct taxes in Turkey is similar to Chile, Poland and Mexico, but lower than Russia, Croatia and the United States (panel (b) of Figure 8). Most countries’ direct taxation is more progressive than Turkey’s. It is also the case that, with the exception of Mexico and Croatia, all peer countries collect more direct taxes than Turkey, as a share of GDP.<sup>15</sup>

**Figure 8. Progressivity and redistributive impact of direct taxes**

# **a. Direct taxes in Turkey, 2016**

<!-- Start of picture text --> 0.5 Kakwani Coefficient Marginal Contribution 0.020<br>0.015<br>0.3 0.010<br>0.005<br>0.0 0.000<br>-0.005<br>-0.3 -0.010<br>EQUALITY<br>KAKWANI INDEX<br>(CHANGE IN GINI POINTS)<br>MARGINAL CONTRIBUTION TO<br>REGRESSIVE <-------------> PROGRESSIVE (property) Payrol Inc. Tax All direct taxes contributions<br>Agricultural Inc. Tax Private Pension Inc. Tax Residence Prop. Tax