rafmacalaba/datause-encoder-data huggingface dataset:
14 than preserving information about the distribution of emissions over components of the cycle (acceleration, deceleration, etc.), information very useful in the design of vehicle.targeting strategies. It has also been criticized recently (Stedman, 1989) for significantly underestimating RC and CO emissions, on average. Indeed, efforts at EPA and other agencies are now underway to improve forecasting of emissions from vehicles. Most parties in the debate agree that MOBILE4 does a good job predicting emissions of pre-catalytic vehicles.8 As these dominate in urban areas of developing countries, the recent criticisms of MOBILE4's accuracy may not be particularly important at present.

MOBILE4 estimates hydrocarbon emissions (VOCs, HC, NMHC), as well as NOx and CO emissions on a per mile basis from gasoline autos and trucks, diesel autos and trucks, and motorcycles. Motorcycles do not appear to be differentiated by four stroke and two stroke engines, although their emissions factors would be very different. Buses are ignored and alternate-fueled vehicles have not as yet been incorporated. It provides emissions by vehicle age class (including pre-emissions control vehicle classes). Emissions of BC and NMHC are estimated from exhaust, evaporative, running loss, and refueling activities, considering the reid vapor pressure (RVP) of the fuel, average ambient temperature, and altitude. CO and NOX emissions from exhausts are estimated by temperature and altitude. Exhaust emissions are produced for cold start, hot start, stabilized, and idle operation. Data on trips per day and miles per day are incorporated in the model and vary by vehicle age. Speed correction 8. Very recent results from a vehicle 3crappage program run by Unocal Corporation challenge this agreement. Of 74 pre-1971 vehicles tested, tailpipe HC emissions averaged 16 g/mi versus MOBILE4's top estimate of uncontrolled emissions of 9 g/mi. Some of the vehicles tested by Unocal emitted over 50 g/mi.
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Mofijur, M., H.H. Masjuki, M.A. Kalam, M.A. Hazrat, A.M. Liaquat, M. Shahabuddin and M. Verman, 2012. “Prospects of biodiesel from Jatropha in Malaysia”, _Renewable and Sustainable Energy Reviews_ 16: 5007-5020.

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Parajuli, R., 2009. “Fostering Jatropha Curcas for Reducing Fossil Fuel Dependency: A Study on Potentiality of Jatropha Curcas in Nepal”, <u>http://www.environmentalexpert.com/Files%5C0%5Carticles%5C72737%5CFostering.pdf</u> Parajuli, R., 2014. “Economics of biodiesel production in the context of fulfilling 20% blending with petrodiesel in Nepal”, _International Journal of Sustainable Energy_ 23(2): 435-447.

Parawira, W., 2010. “Biodiesel production from _Jatropha curcas_ : A review”, _Scientific Research and Essays_ 5(14): 1796-1808.

Shinoj, P., S.S. Raju, P. Kumar, S. Msangi, P. Yadav, V.S. Thorat and K.R. Chaudhary, 2010. “An Economic Assessment along the Jatropha-based Biodiesel Value Chain in India”, _Agricultural Economics Research Review_ 23: 393-404.

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WECS, 2010. “Energy Sector Synopsis Report 2010”, Water and Energy Commission Secretariat (WECS), Government of Nepal, Kathmandu.

27
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Ravallion, M. (2005). Inequality is Bad for the Poor (Policy Research Working Paper No. 3677). Washington 25
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interested in improving the signal-to-noise ratio of their multidimensional poverty measure when aiming at Objective 2.

# **3.4 Combining data from separate surveys**

One of the main data constraint at global level is the missing distribution between incomes and key non-monetary outcomes such as health (Section 2.4). A priori, this constraint is very consequential because both health and income are arguably among the most important dimensions of well-being.<sup>41</sup> The classical approaches to deal with this data constraint “waste” substantial amount of available data. One popular solution consists in constructing an objective well-being indicator based on a single survey. If income data comes from one survey and health data comes from another survey, this solution wastes all the data in the ignored survey. This is the solution used by global multidimensional poverty measures.<sup>42</sup> An alternative solution is to construct a synthetic indicator that ignores inequalities in health and inequalities in incomes. This is the solution used by the HDI, at least when interpreting mortality data as a signal on a population’s health. This solution wastes the data on income inequalities that is available in the income survey.

In this section, we outline three alternative methods that waste less data by combining information from separate surveys using a hybrid index. The first two methods rely on making assumptions on the missing joint distribution. These methods seem relevant when cross-regional well-being comparisons are expected to depend mainly on the partial distributions, with the joint distribution playing a limited role. The third method assumes equal non-monetary outcomes on small areas to compute household’s “total” consumption. This method seems relevant when non-monetary inequalities across areas are expected to be larger than non-monetary inequalities within areas.

## **3.4.1 Assuming the overlap between two types of poverty**

We start with the simplest method to estimate the fraction of multidimensionally poor individuals from two separate surveys. We first present the method and then show that it works surprisingly well when comparing Nigerian states in 2019.

For exposition purposes, assume (counterfactually) that the joint distribution is available. That is, there exists a comprehensive survey that collects the relevant outcomes in all dimensions, monetary as well as non-monetary. Assume that an individual is identified as multidimensionally poor in two cases: either she is below the monetary line or she is above the monetary line but cumulates enough deprivations in the other (non-monetary)

> 41Both income and health are among the dimensions most correlated with subjective well-being (Diener et al., 2018).

> 42The global MPI ignores incomes and the World Bank’s MPM ignores health (see Appendix 6.1).

24
Lichbach (1995:156) details the role of geographic isolation for dissident communities as one that results from distance, poor transportation, inaccessible terrain, and fluid boundaries. In addition, he states that it should follow that as distance from national authorities' increases, the collective dissent should increase – and states with poor transportation networks should experience higher levels of dissent. Relatedly, Herbst (2000) describes the political geography of a small country as `favorable' to a government.

The heterogeneity and military explanations have the same empirical implication:

**Proposition 2** **_Distance from Capital:_** _The risk of civil war events at a location increases with the distance from the location to the capital of the country._

Related to this aspect of geographical distribution is the importance of national borders. Rebel groups may operate more easily in border areas since neighboring countries may provide (actively or tacitly allow) safe zones for rebels. Again, it is impossible to distinguish fully between military and cultural factors. Conflicts which begin in areas proximate to borders may also be linked to irredentist movements in defiance of, for instance, the state's ethnic character. Also secessionist movements that are not inspired by neighboring governments are more likely to rise in border areas, since the prospective new state can avoid being an enclave of the former mother country.

**Proposition 3** **_Distance from Border:_** _The risk of civil war events at a location is higher in border zones._

A third location aspect that impacts where conflicts take place is the location of rebel group headquarters. Even though rebel groups often rely on hit-and-run strategies, they are as constrained by logistics as government forces and find it difficult to operate

> might expect to see a large number of events close to the capital. Simulations reported in Weidmann, Hegre & Raleigh (2006) indicate, however, that in these cases the rebel group tends to be so strong relative to the government that the war ends quickly, with few observable events.

7
**Table 1: Baseline scenario – main results**

||**Real GDP**<br>**at market**<br>**price**|**Urban P**|**remium**||**Skill pre**|**mium**||
|---|---|---|---|---|---|---|---|
||Average<br>annual %<br>change|2005|2030|Average<br>annual %<br>change|2005|2030|Average<br>annual %<br>change|
|**EUR**|1.3|2.9|2.9|0.0|1.5|0.8|-2.3|
|**USA**|1.8|2.6|2.6|0.0|1.5|1.1|-1.4|
|**XHY**|1.5|3.4|3.4|0.0|1.6|1.0|-1.9|
|**CHN**|7.0|1.9|1.1|-2.2|5.9|4.3|-1.3|
|**XEA**|4.5|5.6|3.9|-1.4|1.7|1.3|-1.0|
|**SAS**|5.0|3.6|3.5|-0.1|3.0|2.4|-0.9|
|**ECA**|4.1|2.0|1.3|-1.5|2.4|2.3|-0.1|
|**MNA**|3.0|2.9|3.1|0.3|1.4|1.0|-1.4|
|**SSA**|3.8|5.5|4.6|-0.7|3.7|2.9|-0.9|
|**ARG**|3.5|1.5|1.0|-1.5|2.3|1.6|-1.5|
|**BRA**|2.6|1.4|1.0|-1.2|3.1|2.7|-0.5|
|**CHL**|2.7|1.7|1.2|-1.4|2.0|1.4|-1.4|
|**COL**|2.5|1.3|1.0|-1.2|2.5|2.2|-0.6|
|**MEX**|1.9|2.0|1.3|-1.7|2.6|2.3|-0.5|
|**PER**|4.6|1.8|1.5|-0.9|1.8|1.5|-0.5|
|**CAC**|3.3|1.5|1.2|-0.9|2.5|2.2|-0.6|
|**XLC**|2.5|1.2|1.2|0.0|2.4|1.2|-2.5|

Source: World Bank staff calculations.

Note: ARG-Argentina, BRA-Brazil, CAC-Central America & Caribbean, CHL-Chile, CHN-China, COL-Colombia, ECA-Europe & Central Asia, EUR-EU27 & EFTA, MEX-Mexico, MNA-Middle East & North Africa, PER-Peru, SAS-South Asia, SSA-Sub-Saharan Africa, USA-United States, XEA-Rest of East Asia, XHY-Rest of high-income, XLC-Rest of Latin America

# **3.4 Alternative economic growth scenarios**

The previous section has discussed the mechanisms through which the skill and urban premia changes are linked to structural change, which in turn depends on the rate of income growth and differential productivity growth across sectors. To test how strong are these links and whether our conclusions regarding declining skill premia are sensitive to the economic growth assumption, we perform some robustness checks by varying the rate of economic growth. We consider two alternative scenarios of economic growth and their implications for skill and urban premiums. In these scenarios, we respectively add or subtract a 5-year moving average of the standard deviation of real GDP growth in a respective country/region over 1990-2009 from the baseline growth projections (see Figure 7).

18
the business environment on firm growth. Using subjective firm-level data measures of the business environment these studies show the importance of finance, corruption and property rights (Batra, Kaufman and Stone, 2003; Ayyagari, Demirgüç-Kunt and Maksimovic, 2006). Some other studies examine the relationship between business environment and firm growth using the newer Enterprise Survey, but for individual or small groups of countries (Dollar, Hallward-Driemeier and Megistae, 2005, for India, Pakistan, Bangladesh and China; Fisman and Svensson, 2007, and Rienikka and Svensson, 2002, for Uganda; and Bigsten and Soderbom, 2006, which reviews the literature for Africa).

A central area of focus for this paper is whether there are significant differences in the impact across firm types, particularly by firm size. The literature has largely examined this issue with regard to access to finance, generally finding smaller firms are more constrained (Galindo and Micco, 2007; Clark, Dollar, and Micco, 2004; Love and Mylenko 2005). Beck et al. (2005) is of particular interest here, as these authors include additional measures of corruption and property rights using the WBES firm-level dataset. Using firms’ perceptions on potential constraints, they also find patterns across countries, with small firms benefiting most from greater financial and institutional development.

Our work expands on this literature in several dimensions. This paper nearly doubles the number of countries covered, with country samples approximately five to 10 times larger. Whereas Beck et al. (2005) use subjective firm responses as measures of the business environment at the firm level, we use objective measures (i.e., time spent with officials dealing with permits and licenses rather than a ranking of how burdensome red tape is on a scale of 1-5). Beck et al (2005) include a final table to address potential endogeneity of business environment conditions on firm growth using location-size averages of responses as instruments. However, as the averages are based on current size, we show that this strategy does not actually address endogeneity. Once we control for endogeneity, matching average conditions based on initial size, we no longer find that smaller firms benefit most from improved access to finance. We also use narrower bands on firm size categories, which lead to some interesting extensions on their results, and look at how results vary across country groupings, focusing on measures of institutional development.

round primarily collected perception data regarding constraints and some information on firm performance. In some regions firms only provided information on their employment range, i.e., small, medium, large, making it impossible to use that data to study differences between small firms below and above 10 employees.

7
facilities, cooking fuel, or assets of the household. Individual questionnaires record information on a range of health-related issues such as fertility, family planning, reproductive health, or nutrition. Data files follow a standardized format to maximize comparability across surveys . In contrast to HCES surveys, DHS does not collect information on HHs’ income, consumption, or expenditure. The welfare variable that DHS provides is aggregated based on the various assets that HHs own (Rutstein 2015).<sup>9</sup>

## 3.2.3 Prindex

We also draw on Prindex data which offers comparable data on perceived tenure security – a variable usually unavailable in most multi-purpose surveys. Prindex is a joint initiative of the Overseas Development Institute (ODI) and Global Land Alliance (GLA), carrying out nationally representative surveys of adults in 140 countries measuring how secure individuals feel on their property. In addition, Prindex collects data on a range of demographic and socioeconomic characteristics of respondents, and on land-related variables that may influence the perception of tenure security such as documentation or ownership status. Prindex collects data at individual level and not at household level, therefore, aiming at capturing a fully representative and comparable assessment of individual perceptions on tenure security, not just the heads of households who are most likely to hold official titles.

We impute perceived tenure security reflected in Prindex into HCES based on three variables that are common across both surveys: income (quintiles), location (rural / urban), and ownership of the property (own / rent / other). In both surveys, we assign HHs into groups based on these three variables. We then include the average perceived tenure security of that group to the respective ownership variable available in HCES. As a result, we obtain a tenure security variable at household level that includes tenure perception provided by Prindex and ownership status provided by the most recent household consumption and expenditure survey.<sup>10</sup>

# **_<mark>3.3 Summary Statistics of Adequacy Dimensions</mark>_**

This section explores housing adequacy at dimension level. Table 1 below provides summary statistics of the seven adequacy dimensions embedded in the _AHI_ . Across all 64 economies, except for five countries,<sup>11</sup> all seven adequacy dimensions are available and harmonized across various variables according to the definitions provided in Section 3.1. The mean of each dimension

> 9 For a concise overview on the calculation of the wealth quintile, consult <u>https://dhsprogram.com/data/Guide-toDHS-Statistics/Wealth_Quintiles.htm.</u>

> 10 As we are averaging perceived tenure security across income groups (HH level), rural / urban areas (country-level), and ownership status (HH level) in both data sets, the difference between the individual-level data that Prindex presents and the HH-level data of the HCES housing modules does not present an issue. 11 Kazakhstan, Russian Federation, Ukraine, Uzbekistan, and Vietnam.
in savings rates appears more closely related to the broadening of external imbalances in the periphery since the advent of the euro.

**Figure 3: Savings-Investment imbalances in the Eurozone core and periphery (19902011)**

<!-- Start of picture text --> (a) Gross investment rate (in % of GDP) (b) Savings rate (in % of GDP)<br>25<br>25<br>20<br>20<br>15<br>15 10<br>Average Core Average Periphery Average Core Average Periphery<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010<br><!-- End of picture text --> Source: WDI database.

The investment boom and decline in savings in the Eurozone periphery would in turn be expected to boost domestic demand and GDP growth, thus validating the expectation of real convergence. Strong GDP growth was indeed observed in the periphery, consistently surpassing GDP growth in the core by 1-2 percentage points per year for about a decade prior to the crisis (Figure 4a). Strong growth in the periphery seems associated with vigorous domestic demand rather than with a surge in labor productivity growth. Except for a temporary spike in the late 1990s, labor productivity growth in the periphery was generally on a declining trend throughout the time period considered and was surpassed, before the crisis, by a surge in productivity growth in the core (Figure 4b).

Faster growth in the Eurozone periphery compared to the core seems weakly related to a divergence in fiscal positions. Primary fiscal balances have not significantly diverged among the Eurozone periphery and core countries from the mid-1990s to the 2008-09 financial crisis (Figure 4c). In the second half of the 1990s, reflecting the Maastricht criteria for euro membership, fiscal policy was tightened, with primary fiscal surpluses increasing in both core and periphery countries. However, both core and periphery countries took advantage of the lower interest payments on public debt after the introduction of the euro to ease the fiscal stance as indicated by the decrease in the primary fiscal surplus. This was not reflected into a larger overall fiscal deficit thanks to reduced interest payments on public debt. Fiscal balances diverged markedly in the aftermath of the global financial crisis, which hit to a much larger extent the 7
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