rafmacalaba/data-use-mentions-extended huggingface dataset:
Bradley and Corwyn 2002; Burchinal et al. 2000; Duncan and Brooks-Gunn 1997; Evans 2004; Hart and Risley 1995; Hoff 2003; Walker et al. 2011; Yeung et al. 2002). We focused on two subdomains from the HOME that were found to be correlated with math and literacy scores among children 7–10 years of age in the reference population: the domain relating to fostering growth and education and the domain relating to the provision of active stimulation (Bradley and Corwyn 2005).<sup>5</sup> The adapted HOME scale was included in the model as an ordinal scale from 0–10, with one point per item (see appendix S2 for complete list of questions).

# III. EMPIRICAL METHODS

The empirical methods described below for the analyses are parametric in nature, and, as such, are correlational. We are careful throughout the analysis in claiming neither causality nor inference about structural parameters in our results.

_Magnitude and Evolution of the Socioeconomic Gradients_ The primary objective of our analysis is to document the magnitude of the association between child development outcomes and disparities in SES and to document its evolution over time. We have a multidimensional set of domains (indexed by �� with data on cognition (visual spatial processing, working memory, and reasoning), language, and executive function spanning the preschool years when children were 3–6 years old to the school years when children are seven and older. For disparities in SES, household wealth quintiles from when children were 0–3 years are used<sup>6</sup> .

In a parametric framework, ��� gradients are estimated by OLS using the following specification (Todd and Wolpin 2007): where ��� is the adjusted z-score (internally standardized) obtained for child i on the � developmental domain j at time t. �� is the magnitude of the gradient for _k_ -th quintile ���, expressed as the size of the difference in mean z-scores obtained by wealth
are compounded when importing countries do not entertain visa extensions or do not grant multiple-entry visas.

Thus, while all 45 sample countries in the OECD STRI database except Russia allowed multiple-entry to business visitors in the year 2017, this was not found to be the case for aircrew, seamen and truck drivers (the latter are the most adversely affected with 9 of the 45 countries – Costa Rica, Greece, Iceland, Korea, Lithuania, Mexico, South Africa, the United Kingdom and the United States - not allowing them any multiple-entry in the year 2017). Likewise, only four countries (Canada, Denmark, Indonesia and Japan) provided visas on arrival or exempted visas for temporary entry/transit of crew across services sectors. Most of the other countries are found to be rather restrictive along this dimension, with six countries (China, Ireland, Russia, South Africa, Turkey and the United States) being completely restrictive across all sectors.

Moving towards multiple-entry visas for service providers in all sectors, providing opportunities for e-visas/visas on arrival and/or exempting visas for temporary entry/transit of service providers should all be a part of a viable MNP TFiS agenda. In fact, one notable development in facilitating visas for business professionals is the relatively recent good practice of enabling e-business visas (see Box 1 for details on the Indian e-business visa). Similarly, the Italian Embassy in Riyadh and the Italian consulate in Jeddah have introduced a new facilitation mechanism for Saudi businesspersons and investors to facilitate trade and investment between the two countries. Specifically, applicants who have a "declaration of invitation" from a firm operating in Italy are exempted from submitting certain supporting documents, including proof of the objectives of the journey and "the condition of economiccommercial operator" (Attström et al. 2013).

# **Box 1: India’s e-business visa program**

In November 2014, the Government of India introduced an online tourist Visa (e-Visa) for 43 countries. In 2015, India rolled out the e-Visa program
of the land. These results are consistent across the three databases we use in this work. Similarly, we find that the interaction term between the distance to the hypothetical road network and connection to submarine internet positively correlates with the interaction term between the distance to the real road network and connection to submarine internet, and we also find that lightning intensity negatively correlates with the latter variable.

For our three samples, a strong and significant correlation is demonstrated between the instruments used and the endogenous variables of our model. The Cragg-Donald Wald and Anderson-Rubin Wald statistical tests show that the chosen instruments are strong enough. Certainly, the results of these tests allow us to reject the null hypothesis of weak identification. Also, results of the KleibergenPaap test allow us to reject the null hypothesis of under-identification. Our first stage is, therefore, robust across the three samples used, with the theoretical model well fitted to the data.

# **4.2 Average Complementary Effect of Roads and Internet**

This subsection presents the average, complementary effect of access to submarine internet and road networks on employment and occupation types.

## **4.2.1 Employment**

Table (3) displays the coefficients resulting from the estimation of equation (2). In columns (1) and (2), using the DHS sample, we regress two measures of the odds that an individual is employed on the infrastructure variables and on all our controls including year, country and grid-cell fixed effects. In the third column, we use the LSMS data to compute the effect of infrastructure on the number of hours worked, using the same identification strategy as in the first two columns.<sup>14</sup> The last three columns display the estimations computed from the WBES, with the dependent variables being the number of hours of operation per week in the last fiscal year; the number of permanent, full-time employees at the end of the

> 14Information on the number of hours worked is not collected in the DHS
WHS|DHS, MICS, WHS,<br>LSMS, HBS, etc.|
|34 indicators – all<br>services or<br>outcomes|115 indicators –<br>all services or<br>outcomes|73 indicators –<br>incl. 4 FP<br>indicators|51 indicators –<br>incl. more NCD<br>and FP indicators|
|Focus on equity<br>in MDG<br>indicators|Focus on equity<br>in MDG<br>indicators|Not just MDG<br>indicators – some<br>NCD and FP<br>indicators|Levels of and<br>equity in MDG<br>and SDG<br>indicators, incl.<br>FP|

The 2018 edition of the HEFPI database continues this broadening-out. In addition to the ‘traditional’ MCH and communicable disease indicators from the DHS and MICS, it includes more data on adult NCD indicators, drawing on data from the WHS, the DHS, the Stepwise Approach to Surveillance (STEPS) surveys, and many other regional and national surveys. In addition, the database expands dramatically the number of financial protection datapoints – from 54 to 563. This expansion builds on World Bank research on monitoring progress towards UHC – initially in Latin
Table 3. Role of Fuel Products in the Economy

||Share in<br>total fuel<br>consumption|Fuel intensity<br>in<br>production/<br>consumption|Fuel import<br>intensity by<br>sector|
|---|---|---|---|
|Agriculture|12.4|19.6|49.8|
|Fuel products|0.5|7.0|184.6|
|Industry|29.9|11.6|34.7|
|Transport|40.0|30.8|—|
|Other services|7.1|2.3|—|
|Households|10.1|1.2|—|
|_Urban_|_37.1_|_1.1_|_—_|
|_Rural_|_62.9_|_1.2_|_—_|

Source: Based on HBS (2005/2006) and the Social Accounting Matrix of 2009.

In general, direct effects on households from subsidy reform relate to their consumption of fuel products, whereas indirect effects relate to the change in real household incomes because of higher production costs of goods and services. Consistent with evidence from other countries, the direct expenditure for fuel is modest at 1,805 Yemeni Rials (YER) per capita per year, or about 1.2 percent of household expenditure, on average (Table 4). The per capita amount is higher for urban households (2,659 YER) than for rural households (1,363 YER) in absolute terms. However, rural households spend a higher share of their income on fuel: 1.2 percent compared with 1.1 percent for their rural counterparts. Indirect consumption of fuel matters more.<sup>8</sup> To illustrate this point, consider the case of transportation: Household expenditure on transportation (which is fuel intensive, see Table 3
become. A huge effort, conducted by Luxembourg Income Study, has gone into standardization of national definitions. A similar project is underway at the World Bank using Living Standards Measurement Surveys (LSMS). Yet the standardization covers only a small portion of all countries and surveys. The main differences arise in the treatment of selfemployed income (what are business expenditures for the self-employed?), valuation of

> 16 Disposable household income retrieved from surveys in West European nations amounts to about 60 percent of GDI. A bulk of that difference is explained by health and education consumption. Publiclyfinanced health and education as share of GDI is much less in poor countries.

> 17  On the additional pitfalls caused by the use of averages from national accounts and distributions from household surveys, see Ravallion (2000), Deaton and Dreze (2002), and Deaton (2003).

10
*|
||(-3.752)|(-4.896)|
|**Effects after first 6 months**|||
|Policy Implement.|-0.888|-0.311|
||(-0.784)|(-0.314)|
|**Policy Implement.*Palm**|**11.68****|**13.10*****|
||(2.048)|(2.846)|
|**Policy*Palm*World Price**|**-0.107**|**-0.0944**|
||(-1.494)|(-1.616)|
|World Price*palm|0.0279|0.107|
||(0.377)|(1.313)|
|World Price|-0.369***|-0.464***|
||(-5.451)|(-6.237)|
|Diesel Price|25.33***|25.32***|
||(6.303)|(7.073)|
|Dummy if World Price was rising (DW)|-0.0635|0.0210|
||(-1.432)|(0.379)|
|DW*palm|-0.214|-0.480|
||(-0.155)|(-0.350)|
|Palm|<br>-1.389|<br>-0.691|
||(-0.656)|(-0.377)|
|Relative world prices of palm-soy/wheat-rice|<br>-2.382|<br>-2.919*|
||(-1.543
communities. Overall, energy poverty observatories serve as indispensable tools in the fight against energy poverty, aiding in informed decision-making and the design of effective interventions.

# References

Amaral, S., Câmara, G., Monteiro, A. M. V., Quintanilha, J. A., & Elvidge, C. D. (2005). Estimating population and energy consumption in Brazilian Amazonia using DMSP night-time satellite data. Computers, Environment and Urban Systems, 29, 179–195. <u>http://dx.doi.org/10.1016/j.compenvurbsys.2003.09.004.</u> <mark>Asghar, N., Amjad, M. A., ur Rehman, H., Munir, M., & Alhajj, R. (2022). Achieving sustainable development resilience: Poverty reduction through affordable access to electricity in developing economies. Journal of Cleaner Production, 376, 134040.</mark> <mark>Bhatia, M. and N. Angelou (2015). Beyond Connections Energy Access Redefined. Tech. Rep. Washington, DC: Energy Sector Management Assistance Program, World Bank Group, pp. 1–244.</mark> <mark>Berger, M., & Worlitschek, J. (2018). A novel approach for estimating residential space heating demand. Energy, 159, 294-301.</mark> <mark>Boardman, B. (1991). Fuel poverty: from cold homes to affordable warmth. Pinter Pub Limited.</mark> <mark>Bollino, C. A., & Botti, F. (2017). Energy poverty in Europe: A multidimensional approach. PSL Quarterly Review, 70(283).</mark> <mark>Bouzarovski, S, Petrova, S, (2015), A global perspective on domestic energy deprivation: Overcoming the energy poverty–fuel poverty binary, Energy Research & Social Science,
analysis of over 100 studies, Estache and Garsous (2011) find that the effect of infrastructure on growth depends on three main factors: (i) the specific indicators used to approximate infrastructure;<sup>14</sup> (ii) the time period analyzed;<sup>15</sup> and (iii) the level of development of the country in question.<sup>16</sup> Since there are no specific growth elasticities with respect to infrastructure for the MENA region or countries, it is necessary to rely on a survey of international experience in developing countries to obtain an estimate of the output elasticity with respect to infrastructure. Although there is quite a large literature on the topic, relatively few studies cover developing countries. The basic average elasticity used for the MENA sample, and an associated confidence interval, draw on the estimation results from ten studies focusing on developing countries presented in Table 10. These are relatively high elasticities when compared to those used for developed countries – a fact consistent with one of the lessons in Estache and Garsous (2011). The variance is however large enough to be concerned about the reliability of the average elasticity derived from this sample.

**Table 10 Output elasticities with respect to infrastructure for developing countries**

|**Authors**|**Estimated Elasticity**|
|---|---|
|Dessus & Herrera (2000)|0.13|
|Gwartney, Helcombe & Lawson (2006)|0.17|
|Khan & Kumar (1997) (Africa)|0.32|
|Khan & Kumar (1997) (Asia)|0.26|
|Nazmi & Ramirez (1997)|0.129|
|Odedokun(1997)|0.033|
|Ram(1986)|0.372|
|Ram(1996)|0.299|
|Ramirez (1998)|0.58|
|Sanchez-Robles (1998)|0.
the World Bank’s World Development Indicators (WDI). The income and population data for all countries come from the 2018 version of the Maddison Project Database, which provides historical income statistics for 169 countries through 2016 (Bolt, Robert Inklaar & van Zanden 2018). The income figures are at constant 2011 US dollars. Our main analysis sample is an unbalanced panel of 41 sub-Saharan African countries over the period 1960 through 2016.

We also contrast our main results with the analysis of the evolution of manufacturing in non-oil GDP series for a smaller subset of countries. For 38 countries in our main sample (excluding Equitorial Guinea, Sudan, and Swaziland), we collect data on real growth rates of non-oil GDP, oil revenues as a percent of GDP, and real effective exchange rates from the International Monetary Fund (IMF)’s Africa Regional Economic Outlook (AFRREO) Fall 2018 database for the period 2004 through 2016. The AFRREO Fall 2018 database is in turn based on the statistical appendix of the Regional Economic Outlook (Sub-Saharan Africa) of the Fall 2018 World Economic Outlook (WEO).<sup>4</sup> We utilized the real growth rates of non-oil GDP data series to recover estimates of the manufacturing output shares in non-oil GDP. Finally, for the purpose of testing the implications of the Dutch Disease for countries with significant oil-exporting and agricultural sectors, we collect data on the monthly minimum wages from the International Labor Organization’s ILOSTAT Database for Cameroon, Ghana, Nigeria, and South Africa.

Three measures of industrialization are commonly employed in the literature, namely the share of manufacturing valued added in GDP at constant prices (RMVA), the share of manufacturing valued added in GDP at current prices (CMVA), and the share of manufacturing employment in economy-wide employment (EMP). We focus on RMVA and EMP for our substantive statistical analysis since the trend in the share of manufacturing valued added in GDP at current prices derives from movements in both price and quantity that are indistinguishable to the
# **Material and social deprivation**

<!-- Start of picture text --> 25.0<br>15.0<br>5.0<br>-5.0<br>-15.0<br>-25.0<br>-35.0<br>location vulnerable households household structure housinghealth head of the household individual characteristics<br>AROPE<br>35.0<br>25.0<br>15.0<br>5.0<br>-5.0<br>-15.0<br>-25.0<br>-35.0<br>-45.0<br>location vulnerable households household structure housinghealth head of the household individual characteristics<br>rural areas recipient Roma student inactive retired unemployed age male<br>towns and suburbs hh with social aid hh with pensioner hh with disability hh with +5 members hh with children single-elderly hh hh with unemployed female headed hh number of >64 number of 18-64 number of <18 crowded house unmet medical needs tertiary educ. secondary educ. home ownership<br>rural areas recipient hh with +5 members hh with unemployed Roma needs student inactive retired unemployed age male<br>towns and suburbs hh with social aid hh with pensioner hh with disability hh with children single-elderly hh female headed hh number of >64 number of 18-64 number of <18 crowded house unmet medical tertiary educ. secondary educ. home ownership<br><!-- End of picture text --> Note: Average marginal effects from the Probit regressions are expressed as percentage points. See Annex A4 for further details on the regression analysis. See Table A4.9 for regressions results. Source: Own estimates based on 2021 Bulgaria SILC. We report the survey year and not the income year. AROPE definition based on EU2030 target.

31
For cyclones, storm surges, earthquakes, and tsunamis, we model events with return periods between 2 and 1,500 years. The assumption that each event can only happen alone and once every year means that we underestimate the real impact.

All events but droughts are modeled as a shock on income for affected households, during the year of the event.

It is first necessary to select affected households. In order to avoid exposure bias, all households in the country are duplicated, i.e. affected households are “created” similar to the existing ones while the weight of existing households is reduced. The weights of affected and unaffected households are adjusted to reflect the share of people affected and to keep the total number of people in the country unchanged. This method avoids a random selection of households, which would require running the scenarios many times. The income of affected households is then changed using the vulnerabilities calculated for the resilience indicator. Those differ for the poorest 20% and the rest of the population.

The impact of droughts is modeled through two channels: a shock on the income of farmers and a shock on food prices for consumers. The vulnerability of poverty reduction to food price hikes has already been demonstrated, for instance in (Ivanic and Martin 2008; Ivanic, Martin, and Zaman 3
- Duranton, G. (2015). Growing through cities in developing countries. _The World Bank Research Observer_ , _30_ (1), 39–73.

- Duranton, G. (2021). Classifying locations and delineating space: An introduction. _Journal of Urban Economics_ , _125_ .

- Duranton, G., & Puga, D. (2004). Micro-foundations of urban agglomeration economies. In J. V. Henderson & J.-F. Thisse (Eds.), _Handbook of regional and urban economics_ (pp. 2063– 2117, Vol. 4). Amsterdam: Elsevier.

- Ellis, P., & Roberts, M. (2016). _Leveraging urbanization in South Asia: Managing spatial transformation for prosperity and livability_ . Washington, D.C.: World Bank.

- Fay, M., & Opal, C. (2000). Urbanization without growth: A not so uncommon phenomenon, World Bank Policy Research Working Paper No. 2412.

- Ferré, C., Ferreira, F. H., & Lanjouw, P. (2012). Is there a metropolitan bias? the relationship between poverty and city size in a selection of developing countries. _The World Bank Economic Review_ , _26_ (3), 351–382.

- Ferreira, F. H., Chen, S., Dabalen, A., Dikhanov, Y., Hamadeh, N., Jolliffe, D., Narayan, A., Prydz, E. B., Revenga, A., Sangraula, P., & Serajuddin, U. (2016). A global count of the extreme poor in 2012: Data issues, methodology, and initial results. _The Journal of Economic Inequality_ , _14_ , 141–172.

- Florczyk, A. J., Corbane, C., Ehrlich, D., Freire, S., Kemper, T., Maffenini, L., Melchiorri, M., Pesaresi, M
provinces. These declines were offset by larger relative increases in the service sector. In section 2.3 we show that public spending on defense, health and education (which are included in the service GVA) increased significantly more in the more remote provinces during the 2018/2019 recession. This may have offset the larger relative declines in construction and manufacturing in these provinces.

These findings paint a picture of a volatile economy, characterized by large booms and busts in overall GDP, and also by large changes in growth patterns across sectors. Consistent with the argument by Lucas (1988), quoted above, it is evident that there is a compelling need to identify the drivers of these short-run changes, rather than analyzing long-run average growth rates. However, this will necessarily involve a deeper analysis of the economic (e.g., Gabaix (2011), Baqaee and Farhi (2019)) and political (e.g. Alesina (1987)) drivers, both of which are outside the scope of this paper. Instead, this paper employs a spatial lens to document changes at subnational levels — which perhaps constitutes an incremental step towards gaining a better understanding of the fundamental drivers. In the next sub-section, this paper uses the official province-level data along with two measures of remoteness to document how spatial and sectoral factors interacted with each other during each phase of the business cycle.

# **2.2 Convergence in Economic Activity across Sectors and Periods**

As Figure 1 shows, Istanbul province, which is the wealthiest and best-connected province in the country, grew (in real per capita terms) by around 50 percent between 2004 and 2019. In contrast, Aardahan and Mardin, both of which are among the poorest, grew by almost 100 percent. This subsection examines absolute convergence separately by sectors and periods to document more disaggregate patterns of convergence over time and sectors. Our motivation is to understand how convergence varied across the different periods in order to shed light on how business cycles may influence spatial inequality
true elasticities vary with income. While constant elasticity estimates are often statistically very robust, their usefulness in forecasting depends on whether they are at least as robust as variable elasticity estimates. This paper reports estimates of variable elasticities and compares them with constant elasticities.

Our previous paper suggested differences between the cross-section and time series relations in the panel data, particularly in the urban data. Cross-section variation often results from long-run behavior, and time series variation from short-run behavior. Specifically, long-run income elasticities capture the change in demand when consumers are able to make an unconstrained behavioral adjustment in response to a change in income, and short-run income elasticities measure the demand change in a constrained situation. Empirically, long-run income elasticities can be estimated using cross-section data that cover a wide range of income levels, while short run elasticities require time series data. The empirical results presented in Ingram and Liu (1997) are essentially longrun estimates because they are obtained from cross-section analyses. While these long-run estimates provide a broad picture of growth patterns of motorization and road provision, in this paper we also compare them with short-run income elasticities.

I Based on additional data sources, we made minor corrections on the national data set and added two new observations to the urban data set. See Annex A for details and Appendix Tables 10 to 12 for the national data.
|
||**1991**|**1992**|<br>**1993**<br>**1994**|<br>**1995**|
|Annual cash cost (US$)|1.118,709|642.641|576,804 2,036,908|2,503.066|
|Cost percent of annual loan volume|<br>117|<br>28|<br>9<br>17|<br>23|
|Cost per group credit (US$)|463|127|<br>54<br>107|<br>163|
|Cost per new group credit (US$)|540|171|<br>70<br>145|<br>292|

a/ Program costs were provided for fiscal years. Costs by calendar year are estimates based on fiscal year data.

69