rafmacalaba/data-use-mentions-extended huggingface dataset:
the merging process. By doing this, traversability will be accounted as well through ranking of split lines, and additional data sources such as the building delineation derived from satellite imagery (San and Turker 2010; Vakalopoulou et al. 2015) would supplement road data, especially in rural areas and could contribute to more targeted EAs by clustering building locations. Identified buildings could also help to inform the delineation of rural EAs, as well as providing spatial variables (building size and layout) that may be linked to socioeconomic indicators.

Based on the experience in the present study, we aim to develop a software tool that can be used to generate optimal and practical EAs with minimal user interaction and tailored to a wider set of needs. The tool has the capability of working directly with shapefiles and a wider. The tool will have the ability to consider several varieties of parameters to produce constructive EAs, as well as the integration of socio-economic variables as described above. It could also have broad development impact since it can update existing EAs with the condition if the old EA boundary should be kept or not or generate the new set of EAs and turning them into national sample frame within time, cost and quality constraints.

# **Conclusion**

Somalia presents an example of a country where the need for basic spatial demographic data such as EAs is crucial to continued development, but where on-the-ground logistical constraints, security and data limitations remain significant barriers. Here, we show how a novel approach to creating predefined census EAs in such contexts where the census data are badly outdated and EA data are needed to progress development efforts. In addition, by highlighting existing freely-available, up-to-date highresolution gridded population data and settlement maps, we hope to help remove a further barrier to broader uptake of this method. Future research will aim to improve the accessibility of the approach by providing user-friendly tools that will automate the data access, splitting and merging process according to user specifications. By presenting the approach in the challenging context of Somalia and providing the first up-to-date
) Night time lights from the Visible Infrared Imaging Radiometer Suite (VIIRS) at a resolution of 750 m per pixel. We use the maximum and mean intensity of two months, namely March and September, 2014; (2) Global Forest Change data based on Hansen et al. (2013), from which we use mean tree cover in 2000, and gain and loss in forest area between 2000 and 2014, at a resolution of 30 meters per pixel; (3) Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)’s elevation and slope data at a resolution of 30 meters per pixel.

We also use publicly available built-up area measures based on higher resolution imagery. The Global Urban Footprint (GUF) (year 2012) and Global Urban Footprint plus (GUF+)

> 11 Detailed information on the input population, ancillary data, and the redistribution methodology for each source is presented in the supplementary Table S1.

6
The discretized transaction dynamics of the model are as follows: where, It = UIt + DIt, and Rt = URt + DRt. The typical dynamics of the basic SIR model and our model are shown in Figure 1.

Similar to Cakmakli et al. (2020), we assume that effective vaccination (Vt ) yields immediate treatment for COVID-19 and directly reduces the number of susceptible people. The number of effectively vaccinated individuals is calculated as the assumed number of people receiving vaccinations multiplied by the estimated effectiveness of COVID-19 vaccines. In all scenarios, the effectiveness of COVID-19 vaccines is assumed to be 85 percent—broadly in line with the average of recently reported effectiveness—to accommodate the rollout of several vaccines of varying effectiveness (Fitch 2020; Moderna 2020; Pfizer 2020). Moreover, in the baseline scenario, 71.5 percent of a country’s population is assumed to be amenable to inoculation based on global survey evidence (Lazarus et al. 2020). Lastly, given the lack of precedent for the emergency rollout of vaccines on a global scale, Vt is assumed to follow a pre-set exogenous path rather than a stochastic process. Specifically, the vaccine rollout is assumed to follow a sigmoid curve: a slow initial rollout gives way to large-scale vaccination efforts and a subsequent tapering as the population amenable to vaccination rapidly declines (Figure 2.A).

The SIR model is converted into a state-space form that can be summarized as follows: where yt denotes the observable variable, which in our case, is the seven-day moving average of new daily confirmed COVID-19 cases reported by the Center for Systems Science and Engineering at Johns Hopkins University (Dong et.al, 2020).<sup>4</sup> xt summarizes the evolution of the five possible states: St, UIt, DIt, URt, and DRt . The function p(xt|xt−1, θ) denotes the system associated with moving from xt−1 to xt, which in our application is the SIR model described
42

# **7.4 Economic and Institutional Regime**

Lastly, we turn to examine the effects of the economic and institutional regime, in addition to the rest of the knowledge variables, on economic growth. Tables 4 and 4a present the results of regressions where we included as additional regressors the SachsWarner (1995) index of economic openness and the institutional quality index from Bosworth and Collins (2003). As in the previous sections, Table 4 presents the results when the non-per-worker specification (Equation 7) is estimated and Table 4a presents the results when the per-worker specification (Equation 8) is estimated.

The economic openness and institutional quality variables are included as additional regressors in Regs 4.2 through 4.11 and Regs 4a.2 through 4a.11. For convenient comparison, Regs 4.1 and 4a.1 present the results of regressions where the economic and institutional variables were not included, which we have already examined in detail in the preceding sections. Regs 4.2 through 4.6 and Regs 4a.2 through 4a.6 employ the human capital stock measures based on the population aged 15 years or older with various innovation and ICT indicators. Regs 4.7 through 4.11and Regs 4a.7 through 4a.11 re-estimates the regressions with the human capital based on average educational attainment of persons 25 years and older.

Firstly, we note that all of our ‘new’ regressions exhibit R-squared values that are around 20 percentage points higher than our control regressions. This does strongly suggest that economic and openness and institutional quality do play large role in determining rates of economic growth. Next, we notice that the institutions variable consistently produces estimated coefficients that are positive, which is theoretically consistent, and highly statistically significant. Similarly, the economic openness variable consistently returns an estimated coefficient that is positive and statistically significant.

The next striking observation is that upon the inclusion of the economic openness and institutional quality variables as regressors, many of knowledge indicators, which had produce highly statistically
# **Figure 6-1 Almost all fiscal instruments in the West Bank and Gaza are either inequality-reducing or have no effect**

Marginal contribution to inequality reduction of each fiscal instrument, in Gini points, measured at Consumable Income <!-- Start of picture text --> Inequality reduction<br>VAT (indirect effect)-0.1<br>Fuel excise (indirect effect) -0.0<br>Customs (direct effect) -0.0<br>Fuel subs. (direct effect) -0.0<br>Fuel subs. (total) -0.0<br>Property -0.0<br>Fuel subs. (indirect effect) 0.0<br>PIT 0.0<br>Pension contributions 0.1<br>Pension income 0.1<br>Elec. subs. (direct effect) 0.1<br>Near-cash transfers 0.2<br>Fuel excise (total) 0.4<br>Fuel excise (direct effect) 0.4<br>Primary healthc. 0.4<br>Secondary educ. 0.6<br>VAT (direct + indirect effect) 0.8<br>VAT (direct effect) 0.9<br>Cash Transfer Program (CTP) 1.2<br>Hospital healthc. 1.3<br>Basic educ. 1.4<br>0 .5 1 1.5<br>Marginal contribution at Consumable income (Gini points)<br>Source: Authors' estimates based on PECS 2016/2017; LFS 2017.<br>Notes:<br>[1] Marginal contributions are calculated as the change in a poverty or inequality indicator that results with the inclusion of a tax or transfer of interest.<br>[2] A positive bar represents a reduction in poverty or inequality, while a negative bar represents an increase.<br
15<br>6 60<br>10 69<br>3 5 80<br>0 0 100<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2000 2002 2004 2006 2008 2010 2012 2014<br>Greece Ireland Portugal Spain Greece Ireland Portugal Spain<br><!-- End of picture text --> Values for Spain are on the right axis. Source: Berne Union via Joint BIS‐OECD‐IMF‐WB External Debt Hub.

Corruption perception index. Source: Transparency International.

After the 2008‐09 crisis, Fed and ECB policies diverged. ECB policy was relatively tight in comparison (Fig 11a, 11b) when GIPS entered the sovereign crisis. In 2010, in response to the sovereign debt crisis, the ECB launched its Securities Market Programme (SMP)<sup>50</sup> and the European Financial Stability Facility (ESFS) was announced by the European Council. SMP purchases began in 2010, and were mostly effected during May 2010 and end 2011‐ the period when GIP were under stress. In 2010, the ECB bought Greek, Irish and Portuguese bonds and by 2011, it was buying Spanish bonds. However, SMP purchases were sterilized to avoid the risk of inflation and the ECB monetary base did not increase for a year ‐ between July 2010 and July 2011‐ even though the sovereign debt crisis had begun. Between January and August 2011, SMP purchases were put on hold<sup>51</sup> and the Fed’s balance sheet expanded much faster than the ECB’s until end‐2011. Though its reference rate was already higher than that of the US Fed, the BOJ or the BOE (Figure 12a), the ECB, to counter rising inflation in core countries, raised its reference

> 47 In contrast, the Fed has an explicit dual mandate: price stability and full employment. The Fed has also had a wide range of supervisory and regulatory powers over financial institutions and activities
>0<br>2000q1 2001q3 2003q1 2004q3 2006q1 2007q3 2009q1<br>-40 -30 -20 -10 0 10 20 30 40<br>HPgap OOSgap<br>Source: CNB, IMF, authors' calculations Source: IMF, authors' calculations<br><!-- End of picture text --> Source: CNB, IMF, authors' calculations The estimated parameters of the model were applied to data for the Czech Republic to obtain values of the ―equilibrium‖ credit ratio.<sup>19</sup> The OOS calculations imply significantly different conclusions regarding excessive credit compared to the calculations using the HP filter (Chart 20). According to the HP filter, the credit-to-GDP gap indicates for the Czech Republic excessive credit in the recent period, whereas the econometric estimate does not confirm this excessive credit level (values in the positive part of the chart indicate excessive private credit-toGDP ratios). Although statistical filtering techniques such as the HP filter do have a role to play in the analysis as a first step in the interpretation of the available data, a broader set of indicators and methods should be employed to determine a country’s position in the credit cycle.

An analysis of relationship between an increase in nonperforming loans during the crisis year 2009 and credit-to-GDP gaps estimated by OOS before the crisis (in the second half of 2008) for all CEE countries of the EU indicates that the most indebted countries experienced the largest increase in credit risk (Chart 21). After developing further the methods based on economic fundamentals such as the OOS, we are considering using the results in calibrating countercyclical capital buffers once Basel III is implemented.

> 19 We applied the parameters also to other CEE countries. The results and the accompanying discussion of the consequences for the countercyclical capital buffer are discussed in Geršl and Seidler (2011).

18
Literature_ , 36 (2): 818-860.

Baccaro L. and D. Rei (2005). Institutional determinants of unemployment in OECD countries: A time series cross-section analysis (1960-98). DP/160/2005 _Decent Work Research_ , International Institute for Labour Studies: Geneva.

Bajada, C. and F. Schneider (2009). Unemployment and the Shadow Economy in the OECD. _Revue Economique_ 60, 1033–1067.

Bassanini, A. and R. Duval (2009), Unemployment, institutions, and reform complementarities: re-assessing the aggregate evidence for OECD countries, _Oxford Review of Economic Policy_ , 25(1): 40-59.

Baum, C.F., M.E. Schaffer and S. Stillman (2007). _ivreg2: Stata module for extended instrumental variables/2SLS, GMM and AC/HAC, LIML and k-class regression_ . http://ideas.repec.org/c/boc/bocode/s425401.html Beck, N. and J. N. Katz (1995). What to do (and not to do) with time-series cross-section data. _American Political Science Review_ 89: 634–647.

Behar, A. (2009). Tax Wedges, Unemployment Benefits and Labour Market Outcomes in the New EU Members. _AUCO Czech Economic Review_ , 3(1): 69-92.

33
the 6 countries in the sample where the number for each country, in turn, is a simple average of the sampled firms. For each country in the sample, data from the most recent enterprise survey are used. The corresponding Z-scores for the numbers in Table 5 indicate the number of standard deviations away from the mean a particular value is. Hence, a Z-score of −0.40 implies that for hotels and restaurants, the share of “large” firms is 0.40 standard deviations below the mean. In contrast, a Z-score of 1.10 implies that for electronics and communications equipment, the share of “large” firms is 1.10 standard deviations above the mean.

The data appear to be normally distributed, because 95% of the observations lie within two standard deviations of the mean. Given that the data are normally distributed, around 50 percent of all observations fall within 0.67 standard deviations above or below the mean. This establishes unique threshold points – observations that are more than 0.67 standard deviations above the mean are classified as ‘high’, while those that are more than 0.67 standard deviations below the mean are classified into the ‘low’ category. The remaining are classified into the ‘medium’ category. Using this formal statistical technique as a means of classification, following Nayyar (2013), we classify the different sectors into the following categories:

> 6 Basic metals & metal products; fabricated metal products; non-metallic mineral products; food; wood products; plastics and rubber products; leather products; garments and textiles; chemicals and chemical products; electronics and communications equipment; machinery and equipment; motor vehicles; construction; hotels and restaurants; IT services; wholesale and retail; transport and communications.

8
**Figure 5: Russian Exports by Destination, 2013**

<!-- Start of picture text --> MENA NorthAmerica Rest of the<br>Kazakhstan 2% 2% World 2%<br>4%<br>Belarus<br>4%<br>Rest of Europe,<br>incl. Turkey<br>5%<br>nonCU‐CIS<br>6% EU28_fuel<br>46%<br>China<br>8%<br>Rest of Asia<br>12%<br>EU28_nonfuel<br>9%<br><!-- End of picture text --> _Source_ : COMTRADE data.

_Note_ : Fuel exports refer to chapter 27 in the Harmonized System (HS).

24
0.80|0.69|0.40|
|... if yes, size (ha)|0.12|0.06|0.08|0.10|0.20|0.14|0.05|
|if yes, inherited|0.46|0.66|0.50|0.48|0.30|0.39|0.67|
|... if yes, size (ha)|0.07|0.08|0.08|0.07|0.05|0.05|0.11|
|Transacted out land|0.28|0.27|0.29|0.27|0.30|0.29|0.27|
|... if yes, subdivision|0.29|0.32|0.29|0.31|0.24|0.26|0.34|
|... ifyes,size|0.18|0.13|0.15|0.19|0.25|0.19|0.15|
|No. of households|3348|839|831|835|834|2259|1078|

Source: Own computation from 2015 round of Rwanda Land Tenure Regularization Survey
# Appendices

**Appendix A: Trade Share Data and Tariff Rates for Kenya’s Trade Partners**

**Appendix B: Documentation of the Calculation of Ownership Shares for Kenya**

**Appendix C : Estimates of the Dixit-Stiglitz Elasticities of Substitution  for Kenyan  Imperfectly Competitive Goods**

**Appendix D: Engineering Services in Kenya - Restrictiveness Index**

**Appendix E: Data on Research and Development Expenditures and Sales for the United States in 2004 and 2005.**

**Appendix F: Kenya Model with Multiple FDI and Trade Partners (Algebraic Strcuture)**

**Appendix G: A Note of the Relationship Between Sector Specific Capital and the Elasticity of Supply in Applied General Equilibrium Models of Imperfect Competition**

80
the personnel mix toward more preventive health services staff like nurses and midwives can generate further cost savings. To attract and retain more qualified staff in poorer provinces, the government should also provide such incentives as housing, bonuses, education for dependents, and better posting after service in rural areas. To minimize future imbalance in labor input mixes, another powerful policy instrument is to upgrade training and retraining for less qualified health personnel and then substitute them for more qualified staff where feasible. To implement these policies, the government should ease its currently rigid controls on remuneration to make it more amenable to local needs. However, whether the local or the central government should be given authority to choose the structure of such benefit package remains an open question.

_Targeting of_ **_resources:_** One implication of reduced government health financing is that the poor has suffered higher morbidity and mortality. Among the poorest quarter of the rural population, infant deaths are 3.5 times greater than among city dwellers (NHS 1991). Compounding this inequity is the wide disparity in the distribution of provincial government health expenditures particularly in the past few years. Further econometric analysis with 1993 data indicates that province-level public spending on health responds more strongly to changes in GDP in high-income provinces than in low-income provinces. Moreover, a one percent growth in provincial income is associated with much greater increase in government health expenditure in richer provinces than in poorer ones. These results are disturbing, since they imply that even with equitable growth in income across provinces--an unlikely prospect--the _distribution_ of provincial government expenditure on health will become _more unequal._ If economic growth in the province alone is less likely to reduce the imbalance in provincial health expenditures, a strong case can be
_Credibility Theory_

# <u>Description of step</u>

This step is similar to the credibility step in calculating probable yields. However, instead of smoothing average yields within a district, weighted average historical loss costs are smoothed within the state.

First, the Product Base Rate (PBR) is defined as the average capped loss cost for that district. For district GJ8, using the capped loss cost history of Table 12, this is calculated as . Product base rates are displayed for all insurance units in the Gujarat in column [3] of Table 13.

Table 13. Cotton in Gujarat: calculation of base pure rate

|**District**|**District Area**<br>**Sown 2007**<br>**(ha)**|**Product**<br>**Base**<br>**Rate**<br>**(PBR)**|**Sample**<br>**variance of**<br>**historical**<br>**capped loss**<br>**costs**|**Weighted**<br>**average PBR**<br>**across state**|**Ratemaking**<br>**Credibility**<br>**Factor,**|**Base Pure**<br>**Rate**<br> <br> <br>|
|---|---|---|---|---|---|---|
|[1]|[2]|[3]|[4]|[5]|[6]|[7]|
|GJ1|190,999|2.5%|0.0%|7.6%|67%|4.2%
First, although ours is the first attempt to examine the differences between the Doing Business estimates of compliance times with the actual distribution of experienced times from firm surveys, our findings are building on earlier literatures about the heterogeneity of regulatory compliance in specific countries and sectors. A classic early contribution was Stone, Levy and Paredes (1996) which showed that a comparison of the formal regulations of registering a business in Chile versus Brazil would lead one to conclude that Chile as a much better place to do business as its formal regulations were stream-lined while the regulatory path to registering a firm in Brazil was ―tortuous.‖ But, when they examined actual practice they found that in response to this difficulty of the formal regulations an industry of negotiating this on behalf of firms had emerged that made business registration ―a fairly affordable one-stop process.‖ Pritchett and Sethi (1994) had many of the same findings for the collection of import tariffs. They showed that: (a) the collected _ad valorem_ tariff rates were much lower than the legal _ad valorem_ tariff; (b) that collected tariff rates increased much less than one for one with legal tariffs; and (c) huge heterogeneity in collected tariff rates across items with the exact same legal tariff. Similarly, Gauthier and Gersovitz (1997) use a survey of firms in Cameroon to show how tax exemptions and evasion at the firm specific level cause massive deviations between the hypothetical _de jure_ tax revenues if the tax code were applied and the actual revenues collected. A recent application of randomization investigated the granting of driver‘s licenses in Delhi India (Bertrand, Djankov, Hanna, Mullainathan 2007) and found that individuals who hired a tout were effectively exempted from one element of regulatory compliance (the driving exam) while those who did not hire a tout did have to take the exam (and often failed). Our findings confirm at a cross-national scale and for a number of indicators the commonly observed, but hard to document, fact that policy implementation often deviates from the
Alesina and La Ferrara, 2005; Alesina and Giuliano, 2009; Cojocaru, 2014), policy preferences which also shape ones future opportunities.

The goal of this paper is to provide empirical answers to both these questions by taking advantage of the LiTS survey, which provides harmonized, comparable, and nationally representative attitudinal data for 35 countries in the Europe and Central Asia region. For the purpose of answering the above questions, the LiTS survey offers a number of distinct advantages. First, it offers an unambiguous assessment of the respondent’s own expectations of future mobility. Second, the LiTS questionnaire makes it possible to construct an analytically appealing measure of inequality of access to opportunities, based on expressed beliefs about the importance of personal connections for getting access to key opportunities such as a good job or university education; this measure of inequality of opportunity being in the spirit of the Second Principle of Justice postulated by John Rawls. Third, in addition to beliefs about the importance of connections, the survey also has responses on whether respondents have access to personal connections that can facilitate access to opportunities. Finally, it makes it possible to examine the link between inequality of access to opportunities and expectations of future mobility based on comparable data from a large set of countries; it also allows for comparisons between countries in Western and Eastern Europe.

By examining, empirically, the links between inequality of access to opportunity and expectations of future mobility, the paper aims to contribute to the literature that examines how expectations of future mobility are formed. While many factors can be at play, as shown both in the theoretical and empirical studies reviewed in Section 2, the focus of this paper is on the institutional dimensions of expectations / aspirations, namely on the extent to which the existence of an uneven playing field dampens such expectations, particularly for the disadvantaged groups. The subject of inquiry is related to the studies that show that inequality of opportunity may elicit behavioral responses such as resignation and reduced ambition, which can lead to 3