rafmacalaba/data-use-mentions-extended huggingface dataset:
measure of housing adequacy. This unidimensional measure allows to assess the relative positions of emerging economies with regards to housing adequacy, provide an assessment of the general state of housing within a given economy, anticipate future developments and trends, and raise awareness regarding the overall housing situation within a given country.

Second, the soundness of the _AHI_ is dependent on the _a priori_ selection of its constituent variables and how they relate to the underlying concept of interest (in our case: housing adequacy) (e.g., Booysen 2002; Diener and Suh 1997; OECD 2008). In order to ensure cross-country comparability, the components of the index should be comprehensively capturing the phenomenon of interest, should be of universal significance, and should have cross-cultural applicability (Booysen 2002; OECD 2008). In our case, the selection of variables for the _AHI_ was guided by theory as well as data availability and reliability. We selected seven adequacy dimensions that have consistently been found to be positively correlated with socio-economic development by a large body of research, that are outlined in the Agenda 2030 and are, therefore, of great political and practical relevance, and that are comparable across countries. The selection of dimensions was constrained by data availability in most recent HH surveys across a large set of countries and hence excludes variables on e.g., environmental security or location of the housing unit (more on this issue below).

Third, index creation is often critiqued for arbitrary weighting of its components (e.g., Cox et al. 1992; Foster et al. 2013; Nardo et al. 2005). One frequently cited issue associated with equal weighting schemes is “double counting” of conditions that are highly correlated. If two collinear dimensions are included into composite index, with a weight of _w1_ and _w2_ , the unique dimension that the two indicators measure would have a weight of ( _w1_ + _w2_ ) (cf. OECD 2008). We test the pairwise correlation of the seven adequacy dimensions, which although
of these provinces accounted for almost exactly half of the national GDP in 2007.<sup>5</sup> Each of the selected cities was allotted a sample size of either 100 or 150 firms. These firms were then randomly drawn from an electronic database of firms. Unlike the India survey, the China survey sampled firms from manufacturing and service industries.<sup>6</sup> The sample in China was restricted to businesses that had an employment size more than 20 workers for manufacturing and 15 employees for service industries.

To ensure comparability, we select only manufacturing firms drawn from the industries covered in both the India and the China surveys.<sup>7</sup> In addition, while Indian firms are largely all private firms, some Chinese firms in our sample are state owned. Many commentators suggest that

> 4 The industries are textiles, garments and leather goods, household electronics, electrical equipment and parts, auto and parts, food processing, chemicals and pharmaceuticals, and metallurgical products and tools. 5 National Bureau of Statistics, http://www.stats.gov.cn, accessed March 1, 2010.

> 6 The manufacturing industries include garments and leather goods, household electronics, electrical equipment and parts, auto and parts, food processing, chemicals and pharmaceuticals, metallurgical products and tools, and transport equipment.

> 7As a result, we exclude textiles producers from the India sample and establishments in services and transport equipment sectors from the China sample.

6
internal market with fiefdoms controlled by local officials whose economic and political ties to protected industry resemble those of the Latin American economies of past decades.‖ His explanation is that fiscal decentralization links local government revenue with local industry protection since local firms are stable sources of taxes and jobs. Based on five fairly aggregated sectors, he finds that industrial composition became more similar between 1980 and 1997 while prices of goods diverged. Moreover, resource allocation seemed to be deviated from the principle of comparative advantage.

Two studies reach similar conclusions. Zhou et al. (2000) find a general lack of integration among the indica rice market in the PRC based on monthly rice data from 12 cities between 1992 and 1996. They attribute this lack of integration to transport costs and government intervention in the grain markets. Similarly, Poncet (2003 and 2005) also suggest that trade barriers within the PRC 9
implying that growing up in the rural areas results in the penalty of a one year less schooling for a child.

# **The Role of Cognitive Ability**

A central concern in the literature on intergenerational mobility is whether the observed persistence across generations in economic status is primarily a result of mechanical transmission of ability from parents to children, with little influence of the economic forces such as returns to education and school quality discussed in the theoretical model in section (2). The IFLS 2014 is especially suited to make some progress on this question in the context of a developing country because it collected high quality data on cognitive ability of children.

We construct a measure of cognitive ability of children as follows. First, we calculate the 18
) that measure total online search volume of either nature‐related or cultural “brandtags”. Nature tourism brandtags include terms such as “beaches”, “diving”, “hiking”, “protected areas”; whereas examples of cultural and entertainment brandtags include “historical sites”, “museums”, “religious tourism”, “local gastronomy”, and “nightlife”. More than 3.8 million destination‐specific keywords correlated to tourist activities and attractions were analyzed across nine languages to build each indicator. Figure 5 shows that greater B2C internet use in the destination country is correlated with greater online search for tourism‐related terms in that destination.

In the second approach, we use _Google Trends_ search data. Choi and Varian (2012) and Varian (2014) advocate for the use of _Google Trends_ data to forecast values of economic variables, with illustrations of applications that include travel destination planning. In this paper, we focus on internet queries on Google for the names of five of the most common online tourism platforms: “TripAdvisor”, “Booking.com”, “Travelocity”, “Expedia”, and “Orbitz”. We downloaded search terms from 2004 to the present, with

> 10 That is, Poisson Maximum Likelihood estimation methods, as proposed by Santos Silva and Tenreyro (2010).

Page **14** of **34**
program in Central and East European countries by using relatively recent data from Lithuania.

(Without differentiating between types of private owners), I conclude that (Commercial) transfer of state property to private owners in Lithuania has brought about significant change in enterprise performance measured by increased revenues and improved export performance. The results suggest a negative bias in selection of enterprises for privatization. When controlled for this observed phenomena, the effect of private share ownership becomes larger and increases its significance. Studies based on data from early privatization programs from countries in Transition (predominantly Mass Privatization programs) usually report moderate changes in the magnitude of coefficient on private share ownership after selection bias is being controlled for. The fact that magnitude of coefficients on private ownership increased significantly (nine fold) after controlling for enterprise selection prior to privatization, emphasizes the vitality of controlling for selection bias when a Commercial case-by-case privatization is being studied. On the other hand, expected subsidies seem to contribute negatively and in some case significantly to performance indicators. However the paper finds no clear evidence of the effect of market competition (measured by Herfindahl index) on performance indicators in the short-run. The paper has a number of shortcomings. An apparent drawback of the above analysis is that it does not account for the fundamental differences between various types of private owners, e.g. insiders vs. outsiders, foreign vs. domestic, and institutional vs. non-institutional private owners. Second, it does not account for potential endogeneity in variables describing business environment which might somewhat bias the estimated results. These and related issues will be addressed in the follow-up paper. Even so, policy implication of these results, I believe, are still useful not only for Lithuania, but also for other FSU countries that lag behind in their effort to privatize state owned enterprises and achieve output and productivity gains.

32
Capital Goods.’ _Mimeograph, University of Chicago_

- Griliches, Zvi (1974) ‘Errors in Variables and Other Unobservables.’ _Econometrica_ 42(6), 971–998

- (1986) ‘Economic Data Issues.’ _Handbook of Econometrics_ 3, 1465–1514

- Griliches, Zvi, and Jerry A Hausman (1986) ‘Errors in Variables in Panel Data.’ _Journal of Econometrics_ 31(1), 93–118

- Hall, Vivian Bruce (1986) ‘Industrial Sector Fuel Price Elasticities Following the First and Second Major Oil Price Shocks.’ _Economics Letters_ 20(1), 79–82

- Haller, Stefanie A (2014) ‘Do Domestic Firms Benefit from Foreign Presence and Import Competition in Irish Services Sectors?’ _The World Economy_ 37(2), 219–243

- Haller, Stefanie A., and Marie Hyland (2014) ‘Capital-Energy Substitution: Evidence from a Panel of Irish Manufacturing Firms.’ _Energy Economics_ 45, 501–510

- Jacoby, Henry D, and Ian Sue Wing (1999) ‘Adjustment Time, Capital Malleability and Policy Cost.’ _The Energy Journal_ pp. 73–92

20
|High income (8)|114|0|0|0|13|88|0|
|**Developing countries only (excluding h**|**igh-income**|**countrie**|**s)**|||||
|Net oil importer in 2009 (45)|90|2|20|13|29|33|2|
|Net oil exporter in 2009 (20)|18|30|30|25|5|10|0|
|East Asia & Pacific (9)|89|0|22|22|33|22|0|
|Europe and Central Asia (4)|88|0|25|25|0|50|0|
|Latin America & the Caribbean (17)|101|6|6|12|24|47|6|
|Middle East & North Africa (8)|-3|63|13|25|0|0|0|
|South Asia (5)|30|0|80|0|20|0|0|
|Sub-Saharan Africa(22)|70|5|27|18|27|23|0|

_Sources_ : Tables A2.1 and A2.3, and author’s calculations. _Note_ : The median is that for pass-through coefficients.
each year, increasing the atmospheric CO2 concentration by about 2 ppm. The city concentration anomalies in Figure 2 have the same order of magnitude, thus highlighting the global significance of inter-city variation.

Methods for direct conversion of city residuals to CO2 emissions are still in the research phase. Several recent studies (e.g. Ye et al. 2020; Wu et al. 2020) compare OCO-2-based city concentration anomalies (∆CO2OCO2) with anomalies (∆CO2E) estimated by combining atmospheric transport models with city-level data from global emissions inventories (principally ODIAC (Oda, Maksyutov and Andres 2018)). For the scaling factor [R=∆CO2OCO2/∆CO2E], Ye et al. (2020) find values of 1.6-1.9 for Riyadh, 2.4-2.9 for Cairo, and 2.9-3.2 for Los Angeles. In all three cases, city concentration anomalies calculated from standard emissions inventories significantly underestimate the anomalies in OCO-2 observations. These discrepancies may incorporate errors in sector-level activity data or emissions parameters employed by emissions inventories, as well as exclusion of some sectors from the inventories. For each city studied, a mid-range R-factor could be used to adjust its inventory-based emissions estimate.

Over time, accurate estimation of R-factors for more cities may permit larger-scale adjustment of urban CO2 emissions inventory estimates. The research reported in this paper contributes by quantifying the incremental contributions of multiple sectors to city-level OCO-2 concentration anomalies. Follow-on research could construct sector-level R-factors for adjusting emissions inventories at the sector level. Longer-term, R-factor research may succeed in dropping its current dependence on emissions inventories and produce methods for direct estimation of CO2 emissions 10
to have them in 2004 constant prices. In addition, GSS rebased Ghana’s national accounts series from the 1993 base year to 2006. In our analysis, both 2004 and 2013 I-O tables use the rebased figures. For robustness, we compare the value-added shares from our I-O tables against the ones available from the ETD. The values closely match for “other industry” and WRT services but appear somewhat different for the rest of the sectors. This is mainly because several adjustments have been made to ensure consistency over time of sectoral value-added and employment shares in the ETD.

Appendix B discusses how we constructed national I-O tables from the supply and use tables for Ghana. To construct I-O tables for the north and the south of Ghana for 2004

> 8 Since independence in 1960, Ghana’s district boundaries have changed multiple times. NIC 2003 follows a classification of 138 districts, and IBES 2014 follows a classification of 216 districts.

> 9 In our five-sector classification, “other industry” encompasses manufacturing, construction, and utilities, and “other services” include transport, communications, finance, commerce, government services, and private services.

10
*4**|2,70|**4**|3,25|
|**South West**_(Gafsa/ Tozeur/_<br>_Kebili)_|**6**|2,65|**6**|1,80|**6**|2,35|**7**|1,96|
|**_Average Index_**||**3,34**||**2,12**||**2,85**||**3,18**|

_Source : IACE reports on regional attractiveness, 2016, 2017, 2018, 2019_

# **4. Variables and Methodology**

The objective of this section is to identify the factors that influence the regional attractiveness of FDI inflows in Tunisia. We will attempt to provide a comprehensive analytical approach that looks at a set of economic, institutional and geographical determinants. The goal of this analysis is two-fold. First, we will ask how each governorate’s own economic and business environment contributes to the attractiveness of this type of export-oriented FDI. Second, we will empirically measure to what extent the inflows of FDI to the country tend to follow an agglomerate approach within certain regions. It is not the purpose nor scope of this paper to unpack the underlying reasons of this approach either from home or host country perspectives. Instead, we focus on investigating the determining factors that have influenced the longstanding geographical distribution of FDI primarily directed toward coastal regions. Using a spatial analysis, we aim to demonstrate empirically how the geographical location of certain regions and their
switching plants.

> 11In principle, we could run the plant level analysis starting in 2007. However, representative data by regency and industry, which are discussed in Section 4.3, are only available from 2008. Therefore, to be consistent across levels of analysis, we start from 2008. Plant level results for the period 2007-2015 are very similar, which is not surprising given that robots’ adoption started to take off in 2009. This is discussed in Section **??** .

11
impacts on the economy and people, however there was wide variation in the scale of the response, with higher-income economies spending significantly more than lower-income economies on support to households and firms. A synthesis of available microsimulation studies shows that increases in poverty were only partly mitigated in poorer countries World Bank (2022b). While the phone surveys are an imperfect source of data to assess receipt of assistance, they point to benefits expanding in coverage over the course of the crisis, but not reaching many affected households, especially in LICs. Many income losses went unmitigated, as reflected in the strong correlation between income losses and high levels of food insecurity. Households adopted various coping strategies, including the use of savings and sales of assets, which could hurt their future productive capacity and ability to recover from the crisis, leading to higher future poverty and inequality, an economic version of “long COVID”.

2
wind direction in a 10-year analysis for the region.

In the second part of the paper, we explore the distributional implications of our findings. Using our methodology for analysis of spatial pollution displacement, we quantify NO2 pollution exposure for residents in the displacement path. To index relative vulnerability, we use WorldPop digital maps to estimate the number of children (0-5 years) and elderly adults (65+) in poor households in each grid cell. We develop separate indicators for three measures of poverty incidence based on the global multi-dimensional poverty index (MPI); a poverty line of $US 1.25/day; and a poverty line of $US 2.00/day.

We combine our pollution exposure and pollution vulnerability measures to produce a composite pollution impact measure for each grid cell. We aggregate our measures for level-3 administrative divisions and map the results to assess the implications. We find three elements of primary significance. First, areas of the city have highly-varied “environmental endowments” driven by strongly-seasonal weather effects on the spatial distribution and intensity of NO2 pollution. Pollution exposure is worst for areas that are in the wind path from high-traffic areas in seasons when pollution is maximized by weather factors. The second critical element is the spatial juxtaposition of environmental endowments and vulnerability. Some highly-vulnerable areas have poor environmental endowments while others do not, with potentially-critical significance for pollution impact analysis. The third element relates to the definition of poverty. Vulnerability 20
Individual _AL_ are thus weighted averages of activity factors where the weights vary across individuals based on their time-use.

The most detailed activity data for India comes from the Time-Use Survey (TUS) conducted in 1998-1999 by the National Sample Survey Organization for six states (Gujarat, Haryana, Madhya Pradesh, Meghalaya, Orissa, and

> 4Validation studies by (Shetty et al. 1986) and (Ferro-Luzzi et al. 1997) find that these types of prediction equations, which are typically estimated on European descended populations, have a reasonably good fit for subjects in India.

6
products offered by a manufacturing firm is a services activity.ss<sup>17</sup> At the intensive margin, servitization is measured as the share of revenue from services sales in total sales of the firm. T is _firm productivity_ for firm _i_ at time _t._<sup>18</sup> Firm level information on value added, capital and labor is required, at the very least, ii,tt to estimate a production function, Since firms in Prowess do not report the count of employees, we estimate TT it by dividing the total wage bill of the firm with the median wages by industry, state and year from the Annual survey of industries data. For estimating the production function, we use the Levinshon and Petrin (2003) method, with expenses on power, fuel and water as the proxy variable. And, e e is _exposure to import competition_ for firm _i_ at time _t._ An industry’s exposure to import competition is measured as the ii,tt share of imports relative to output in the industry. To compute firm level exposure, we weigh the industry-sse ee Where level exposure with the share of manufactured products. = <u>j, ,</u> is the revenue from sales of manufactured product EEe eesse eeii,tt = �ρρjj jj,ii,tt ∗IIjj,tt _j_ by firm _i_ , , as a share of total manufacturing activity of the firm ρρjj,ii,tt RRRRii,iitttt _i_ at time _t_ , and = IO I <u>j,</u> is the share of imports in industry RRjj,ii,tt _j_ at IItt tt time _t_ in total output of the industry at time _t._ Imports at the industry level are obtained from WITS database, RRii,tt IIjj,tt ttIIOOttjj,tt

> 14 Prowess is not well suited for understanding firm entry and exit because firms are under no legal obligation to report to the data collecting agency. However, since
as a cosmopolitan social welfare function (Atkinson and Brandolini, 2010).<sup>3</sup> One might also have an instrumental concern for regional interpersonal inequality if extreme regional inequality contributes to conflict or migration.

The analysis in this paper is based on a database of national household surveys from Sub-Saharan Africa maintained by the World Bank, over the period 1993 to 2008. Our welfare measure is consumption expenditure per capita as measured in household surveys, expressed in 2011 PPP USD, although the results are robust to using 2005 purchasing power parity (PPP) exchange rates. While our database of household surveys has a good coverage of Africa as a whole (representing three-quarters of the regional population), the coverage of fragile countries remains low. Hence our results are probably a lower bound on African inequality. Another reason for why our estimates are likely to be a lower bound is that we use consumption expenditure surveys which most likely underestimate consumption at the top of the distribution.<sup>4</sup>

> 2 Throughout this paper, we use “Africa” and “Sub-Saharan Africa” interchangeably, as in Beegle et al. (2016). 3 A large literature in philosophy discusses the cosmopolitan view of the world and issues of global justice more broadly (e.g. Pogge, 2002; Singer, 2002; Nagel, 2005).

> 4 Furthermore, inequality in consumption expenditure tends to be lower than inequality in income, e.g. Li et al. (1998) find a difference of 6.6pp for the Gini index. Considering the regional distribution of _wealth_ (as opposed to consumption expenditure), it can be shown that the ten wealthiest individuals have as much wealth as the bottom half of the African continent (in this case including both Sub-Saharan and North Africa) (Lakner, 2015). However, the imputations used in the wealth data might underestimate wealth holdings at the bottom (see footnote 5 of Lakner, 2015), thus suffering from the opposite problem
intervals around current corruption indicators. The statistical methods to derive such confidence intervals need to assume some random process that relates actual corruption to the observation expressed by the respondents. If there are other unidentified observations in the data that are not about corruption, it is not clear how one would adjust for this. It might thus be better to drop the pretense of creating “scientific” confidence intervals.

# **Speed Money**

In the last decade a new data set has been created that annually measures a set of regulatory arrangements relevant for business, currently across 183 countries – the Doing Business (DB) report. By now there are eleven sub-indicator sets that measure inter alia official procedural requirements for registering a business, obtaining a construction license, an import license and getting an electricity connection. Starting with the pioneering first data set on regulation of entry (Djankov, 2002), the data have been used to assess whether more complex procedural 6