rafmacalaba/data-use-mentions-extended huggingface dataset:
# 1. Introduction

Using geospatial data as auxiliary data for small area estimation is an old idea. Proof of concept was initially demonstrated thirty-five years ago by Battese, Harter, and Fuller (1988), who combined survey data with early imagery from the Landsat satellite to predict the area under corn and soybean production in 11 counties in Iowa. That paper is widely cited in the field of small area estimation statistics, with nearly 1,100 cites on Google Scholar as of May 2023. But the paper is better known for another seminal contribution, as it was the first to develop and apply the well-known nested-error unitlevel model, with a conditional random effect specified at the target area level, to estimate means for small areas. From 1988 to about 2015, economists and statisticians devoted considerable effort to refining this model in various ways, with a particularly important innovation introduced by Molina and Rao (2010) to estimate indicators other than means, such as poverty headcount rates, using simulation techniques. In the meantime, the publication of Elbers, Lanjouw, and Lanjouw (2003), which used a slightly different unit-level model, popularized the use of small area estimation at the World Bank. Nonetheless, until relatively recently virtually all applications during this time used census or other administrative data as auxiliary data, ignoring geospatial data as a potential source of auxiliary data from which surveys could “borrow strength” to improve the measurement of socioeconomic data.

Geospatial data was rediscovered as a potential source of auxiliary data in the mid 2010s, as advances in computing power and storage enabled geospatial data to become publicly available at a wide scale; as surveys began to be regularly implemented on tablets that collect geocoordinates; and as a new generation of data scientists, economists, and statisticians discovered the potential of geospatial data to improve socioeconomic measurement. This, in turn, sparked interest in combining survey and satellite indicators for the purposes of small area estimation. Using appropriate methods for this type of “data fusion” is important because small area poverty estimates have
price data (not CPI values per se). The problem is that many countries collect price information only in urban areas (see Table A2 in Appendix A). Nevertheless, if the geographic coverage of the data is adequate, using the same price data for spatial and temporal price adjustment can be an efficient approach in terms of the data collection costs, and it could also enhance consistency between temporal and spatial price patterns.<sup>11</sup> In addition, compared to market price surveys, a wide variety of food and non-food price information is collected in the CPI data, and the data collection is frequent (for example, monthly). A well-known issue is that the basket used for the calculation of the CPI is often skewed toward higher-income households. For

> 10 Some countries report regional CPIs. Such regional CPIs still do not indicate price differences across regions as they only indicate inflation in each region and do not utilize the data for determining spatial variations in cost of living across the regions.

> of living across the regions. 11 Inconsistency in temporal and spatial price movements is not uncommon. Let us consider a spatial price index at year 0 ( ) and year 1 ( ). By applying the regional CPIs between _t_ 0 and _t_ 1 to , one can obtain another spatial price index ( ∗ ). This index ∗ often deviates from . tt0 tt1 tt0 SSPPSS SSPPSS SSPPSS tt1 tt1 tt1 SSPPSS SSPPSS SSPPSS

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views on country’s economic situation, and attitudes towards democracy. I also perform a heterogeneity analysis based on the strength of municipal state capacity.

## _4.1 LAPOP Analysis (2004-2018)_

Table 3 presents the main results on the relationship between income and trust in the president and the municipal government. Regardless of whether I use income data (for a smaller sample of municipalities in columns 1 to 3) or NTL density as proxy for income in an extended sample (in column 4), individuals’ trust towards the president positively correlates with higher level of development at the municipality level. Specifically, the empirical association between income and trust in the president is virtually unaffected by the inclusion of individual levels controls (column

> 11I used 1992 population counts to normalize both the number of homicides and the amount of government transfers

> 12A municipality is defined as weak (strong) state capacity if the average share of household with access to electricity and water is below (above) 80 percent.

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# **6. Conclusions**

There is no consensus on how to define the phenomenon of labor market informality in developing economies. In many cases researchers’ choice of definition is determined by the availability of data. There has been only cursory discussion in the literature on whether this matters. We conclude in this paper that it does. Using data from Brazilian household surveys we have constructed three alternative definitions of informality, which attempt to reflect the various alternatives used in the literature. Informality is defined according to employment contract status, according to social security protection and, in a manner which is often adopted in previous research, according the nature of the employment and the characteristics of the employer. These three definitions are far from observationally equivalent. Around 64% of all the economically active in Brazil are informal according to at least one of the three definitions, but only 40% are informal according to all three definitions. There is a closer correspondence between informality defined by not having a registered employment contract and not contributing to a social security scheme. However substantial numbers of workers are classified as informal if the definition is based on the nature of the employment and the employer, even though they may have registered contracts and/or may contribute towards social security.

Even though overall rates of informality appear constant in Brazil through the 1990s, steady compositional changes are taking place. More women are becoming formal, while men are becoming more informal. Rates of informality are falling among the least skilled (as they decline in importance as a group) while rates of informality are rising quickly among the most skilled (as they increase in importance as a group). There are also occupational changes at work.

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this leaves a substantial unexplained component, which seems to stem from other – potentially distortionary – factors. The rest of the paper is organized as follows. Section 2 describes our data sources and reports key moments used in the estimation. Sections 3 and 4 contain our main results. Section 5 concludes.

# **2 The Data**

We use data compiled from a number of different sources. First, we use the Bureau van Dijk Orbis database. Orbis contains firm-level data (including both public and private firms) for a number of countries (although the extent of the coverage varies widely).<sup>6</sup> We focus mainly

> 3The data to compute markup dispersion are only available  in four of the 11 countries, highlighting some of the data hurdles future work along these lines may have to overcome.

> 4‘Correlated’ distortions of this kind have been emphasized in, e.g., Restuccia and Rogerson (2008), Guner et al. (2008), Bartelsman et al. (2013), Buera, Moll, and Shin (2013), Buera and Fattal-Jaef (2016), Hsieh and Klenow (2014) and Bento and Restuccia (2016).

> 5Note that this refers to dispersion in markups within an industry, not the average level of markups or the dispersion across industries.

> 6For a comprehensive description of the Orbis database, see Kalemli-Ozcan et al. (2015). Gopinath et al. (2017) also use the Orbis data in a misallocation context, albeit for a developed country, Spain.

3
However, the picture differs with respect to innovative capacity, as the _Annual Report of Regional Innovation Capability of China_ (2001 and 2002) shows. Beijing lagged behind Shanghai in 2001, but generally surpassed Shanghai one year later (see Table 6). According to this report, Beijing has enhanced the innovation capability of enterprises as measured by the outputs of new products and technical innovations. This may be the result of success in attracting enterprises and research institutes of both domestic and multinational corporations to Beijing. Shanghai remained stable in most subentries.

**Table 6: Ranks of Beijing and Shanghai in** **_Annual Report of Regional Innovation Capability of China_**<sup>**_5_**</sup>

||Knowledge<br>creation|Knowledge<br>diffusion|Innovation<br>capability of<br>enterprises|Innovation<br>environment|Economic<br>performance<br>of<br>innovation|General|
|---|---|---|---|---|---|---|
||||2001||||
|Beijing|1|4|4|1|2|2|
|Shanghai|2|1|2|2|1|1|
||||2002||||
|Beijing|1|6|2|1|1|1|
|Shanghai|2|2|1|2|2|2|

_Source_ : based on Annual Report of Regional Innovation Capability of China 2001 and 2002.

**Table 7: Selected responses to on-line survey comparing investment environment in Beijing and Shanghai**<sup>**6**</sup>

|Which city is better? (% of responses)|Beijing|Shanghai|No<br>opinion|
|---|---|---|---|
|_General condition_||
recent patterns in use of antidumping and other TTBs raise the question of how earlier research on antidumping and conditions of market competition may apply to emerging economies, both as users and as targets.

# **4.2 Fragmentation of production and multinational activity**

Fragmentation of the value chain in the production of many traded goods has been increasing (Hummels, Ishii, and Yi 2001), and the share of trade mediated by affiliates (and/or) parents of the same multinational firm rather than through arm’s-length transactions is large. For example, nearly half of total U.S. imports in 2000 were intra-firm transactions.<sup>22</sup> Increased fragmentation of production and trade between related parties complicate both the political economy of antidumping and its potential impact on conditions of market competition.

Antidumping measures affecting intermediate inputs raise the costs of firms further downstream in the supply chain, thereby hurting overall competitiveness relative to firms and production locations unencumbered by antidumping.<sup>23</sup> Thus, to the extent that international transactions occur between affiliates of the same multinational firm, exporters and importers have a shared incentive to keep markets free from new trade barriers.<sup>24</sup> Global supply chains and a significant 22 In their study of the determinants of intra-firm trade, Bernard, Jensen, Redding, and Schott (2010) report that 46 percent of total U.S. imports in 2000 occurred between related parties. The importance of intra-firm transactions varied significantly by trading partner, ranging from close to zero to nearly 100 percent, and also by product.

23 Krupp and Skeath (2002) provide empirical evidence of a negative impact on production by downstream users of intermediate products subject to antidumping duties. The authors use a panel of 13 upstream/downstream product pairs, e.g., methanol and formaldehyde, for 1977-1992 to identify the effects of antidumping duties in the upstream industry on production and sales in the downstream industry. Antidumping duties upstream are confirmed
0.40<br>Comm. & Industrial<br>0.20<br>Parts & Components<br>0.00<br>74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 06<br>-0.20<br>-0.40<br>-0.60<br>Source: Korea Electronic Association<br>Note: TSI=(export-import) / (export + import) for each<br>industry<br><!-- End of picture text -->

Source: based on COMTRADE data, SITC Rev1, 2 digits, <u>extracted from WITS</u> Note: RCA=the share of an industry in the economy‘s <u>exports /its share in global exports</u> In terms of the inter-industrial dimension, Korea‘s maintained high revealed comparative advantage (RCA) in clothing exports until the end of the 1960s, followed by the footwear until the 1980s. In the 1990s, it rapidly developed a high RCA in electronics exports which was replaced by transport equipment exports more recently.

Korea‘s ̳Flying Geese catch-up‖ model also had an international dimension which involves the relocation of an industry from one country to another. For example, it gained a sharp increase in RCA in footwear in the mid of the 1960s partly as a result of manufacturing alliances and

> 17 For the debate on whether the HCI drive in the 1970s is ―comparative advantage following‖ or ―comparative advantage defying‖, see Lin & Chang (2009).

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use of a data set explored by Autor, Levy, and Murnane (2003) that breaks down the ‘task’ composition of a wide range of occupations in US labor data. Authors who exploit these distinctions to look at the differential effects of trade shocks on workers with different types of occupations include Peri and Sparber (2009), Ebenstein, Harrison, McMillan, and Phillips (2009), and Liu and Trefler (2011). Some of the results in Ebenstein et. al. (2009), in particular, suggest that occupational distinctions may be more important than industry in identifying who loses from globalization, that it is workers in vulnerable occupations (namely, those that are the most offshorable) in affected industries who lose.

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sup> According to this definition, and after hand collecting information on all 256 senators elected in 2000 and 2006, we divide senators into three types, increasing in political importance. The type I includes all senators that did not chair any commission; the second type includes all senators who have only chaired non-influential commissions; while the third type includes all the remaining senators that have presided an influential committee. As shown in Table A3, 44 percent of senators are type I, 42 percent of senators are type II, and the remaining 14 percent are type III. In this

> 14 Recent studies show evidence that leakage of important macro-news and policies affects the distribution of private information in the financial market. (e.g. Bernile, Hu and Tang, 2015; Agarwal, et al.,2015b). 15 Senators can only preside one commission per legislature.

> 16 This negotiation takes place through the _Junta de Coordinacion Politica_ , a group within the Senate formed by the senators leading each parliamentary group, as well as three additional senators: two from the largest parliamentary group and one from the smallest one. By law, the votes of the members of the _Junta_ are weighted by the number of senators that each parliamentary group has (Articulo 81, Ley Organica del Congreso General De los Estados Unidos Mexicanos).

17 With 60 permanent committees, the Mexican senate is the upper chamber with more committees worldwide (Power, 2012; Morales, 2015).

18 For each committee, Table A1 displays a brief description of its main areas of work as well as the state of its chairman per legislature. These committees can be further divided into ones that are related to specific economic sectors (e.g. Energy) and those that purview a more global agenda (e.g. Treasury). In the last column of Table A1 we display the economic sectors that are directly related with the purview of a committee using the NAICS 2007 classification.

> 19 Detailed data on all the bills received in the Senate
|Congo, Dem. Rep.|43|Chile|31|Mozambique|50|Argentina|22|South Africa|38|Indonesia|18|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Australia<br>|8|United States|10|Tanzania|12|United States|17|Australia|18|Australia|10|
|Indonesia<br>|7|Peru|8|Kazakhstan|11|Bolivia|16|Gabon|11|Canada|10|
|Canada<br>|6|Australia|6|Canada|7|Chile|11|United<br>States|8|Russian<br>Federation|9|
|Nauru<br>|5|Congo, Dem.<br>Rep.|4|Malawi|6|Australia|7|Kazakhstan|4|Philippines|8|
|Tonga<br>|5|Russian<br>Federation|4|United<br>States|3|Canada|7|Canada|4|New Caledonia|8|
|Cuba<br>|3|Canada|4|Australia|3|Germany|5|Ukraine|3|Brazil|5|
|Philippines<br>|3|Mexico|4|Madagascar|2|China|4|Georgia|3|Cuba|4|
|Zambia<br>|2|China|4|Sweden|2|Congo, Dem.<br>Rep.|3|Ghana|2|South Africa|3|
|New Caledonia<br>|2|Indonesia|3|Greenland|1|Mexico|1|India|2|Nauru|3|

_Source:_ Authors based on World Bank’s AFE Energy Transition Material (
|Electricite<br>du Laos|Lao PDR|Energy||2100|

Source: China Global Investment Tracker.

# 3.2 Transportation Cost

To measure transportation costs, we construct detailed transportation cost data by the mode of transportation. First, we measure air distance using the population-weighted great-circle distance from the CEPII GeoDist data set. Second, we measure sea distance using data from Feyrer (2009).<sup>10</sup> Lastly, we construct road distance and driving time data using Google Maps Distance Matrix API.<sup>11</sup>

> 10 We thank Jim Feyrer for kindly sharing the data.

> 11 In a related project, Baniya et al. (2018) examine the role of BRI in international trade by estimating the effects of driving time reductions. We thank Michele Ruta for the valuable suggestion of looking at driving time as a measure of transportation cost.

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j + β 4 M j + β 5 Beneficiary ij *ML j + β 6 Beneficiary ij *L j + β 7 Beneficiary ij *M j + ε ij_ , where _i_ indexes the individual (household) and _j_ indexes the village. _Beneficiary ij_ is a dummy that takes value of 1 if the individual _i_ in village _j_ is a beneficiary of the NAIVS voucher program in 2010/11. Variables _ML j_ , _L j_ and _M j_ are dummies that take value of 1 if village _j_ is under both interventions, only second intervention and only first intervention, respectively. The models presented here represent an intention to treat analysis and are not adjusted for any baseline covariates. Dependent variables are constructed based on questions asked on follow-up household survey. The dependent variable in Column 1 is a dummy that indicates whether respondent is aware of the presence of the NAIVS program in his or her village. Column 2 reports the results using a dummy that codes whether the respondent was asked about his or her ability to pay for the voucher top-up in 2010/11. In Columns 3 and 5, the dependent variables are dummies that indicates if respondent reports receiving vouchers in 2010/11 and 2011/12, respectively. For those regressions, respondents self-report receiving or not voucher and the answers may differ from administrative data used to assign beneficiary status. In Column 4, the dependent variable in Column 4 is a dummy variable that indicates whether the respondent reports being very satisfied with the selection of voucher beneficiaries in 2010/11. P-values are computed using the t-asymptotic wild cluster bootstrap procedure described in Cameron et al. (2008) and reported below the coefficients. Clustering was done at the village level. ***,**,* denote statistical significance at the 1, 5 and 10 percent level, respectively. See Appendix Table 1 for definition of variables.
right after program completion. However, treated women invest 97% more in other businesses at endline and our index of savings and investment outcomes shows positive, significant effects. Together, results on savings and investments indi-

> 5Half of the sampled women have an active business at baseline, and merely 11% are employed outside of self-employment. Many existing businesses are in perishable goods, livestock, and energy, but we also observe service-based businesses like salons as well as restaurants, retail, and construction.

5
For Indonesia, the Philippines and Thailand high resolution settlement layers from Facebook Connectivity Lab and Center for International Earth Science Information Network ‐ CIESIN ‐ Columbia University was used. These layers are produced at a spatial resolution of 1 arcsecond (~30 meters). The underlying data are based on 2015 and combines census data with satellite images from DigitalGlobe. The population is allocated according to subdivision censuses once settlements have been identified from the satellite images.

In addition to identifying economic activity, the settlement layers will be used to identify rural and urban areas based upon population density.

# **II. General Empirical Strategy**

The goal is to analyze the effect that natural hazards might have on the economic activity in an area. To try to tease out this effect, a data set was constructed for each natural hazard type. These sets contain localized nightlight values and damage indices for each hazard type, the total population in each light cell and administrative boundary data linking each nightlight cell to different administrative levels.

The new data sets are used in two different sets of analysis. First, a simple case study on a singular large event is performed. The case studies are done on the Aceh earthquake in 2016, the Haiyan Typhoon and finally for the 2017 floods in Thailand. All of these events had severe damages in the affected areas and one would expect that the nightlight values would change significantly following these events. As for the actual analysis, it is a graph of nightlight means of cells in affected areas, split between cells that experienced damage versus those that did not.

The second analysis is a fixed effects regression for each country and natural hazard type to see if any national effects exist. The regressions are lagged for the 12 months following the hazards to allow for any short‐ and mid‐term effects to materialize. The general methodology can be seen in Figure 4.

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It is assumed that the wage bill shares from a given aggregated sector from the data sets explained before, hold good across the GTAP sectors mapped to this aggregated sector. For example, if male wage bill share were 50 percent of the total wage bill in ISIC sector 1, which is crop and animal production, the male wage bill share in GTAP sectors 1 to 13 is assumed as 50 percent of their sectoral wage bills, based on the concordances shown in the boxes 1 and 2 above. Further, Bhutan dataset does not exist in GTAP, therefore it was split away from GTAP’s composite region named Rest of South Asia, using macroeconomic and trade data from the World Bank in SplitReg tool, to compute shares to separate Bhutan from this composite region. It is also assumed that the gender data in Bhutan may be similar to that in Nepal.

# **4. Description of the Modeling Framework**

The model developed in this paper is an extension of the standard GTAP model (Hertel, 1997). The sets SECT of sectors is defined (indexed by _k_ ) and REG of regions (indexed by _r_ in most cases and if the region is the source of exports/imports but by _s_ if the region is destination of exports/imports).

## **_International Trade_**

The change in imports of each region from each of the others is determined by three factors: (i) substitution among different sources, based on the differential between import prices from specific sources and the sum of import-augmented technical change and aggregate import prices _pimk_ **_k,s_** , multiplied by the elasticity of substitution of imports between the sources **_σM,k_** , using the Armington elasticity for the sector as in the GTAP Data Base, (ii) import-augmenting technical change, _amsk_ **_k,r,s_** , that lowers the effective price of a good in the destination market, and (iii) the import penetration as captured by the change