rafmacalaba/data-use-mentions-extended huggingface dataset:
in (1). However, since household surveys often use two stage sampling, it seems appropriate to consider a twofold nested error model. Marhuenda et al. (2017) extend the EB method from MR (2010) to a twofold nested error model, given by:<sup>6</sup>

> 4ELL (2002) provides the derivation of the estimate for var<sup>�</sup> _ση_<sup>2</sup> � in their appendix.

> 5The method is called CensusEB because it does not link survey and census households. The old method is called the clustered bootstrap EB (CBEB). For the updated Stata package, see https://github.com/pcorralrodas/SAEStata-Package.

> 6For simplicity, we omit the heteroskedasticity weights considered by Marhuenda et al. (2017).

5
65 weights. All averages, standard deviations, and counts we compute using HIES data are weighted using these weights.

We extract wage information from the 2019 HIES, which lists household members who “were paid employees during last four weeks/last calendar month”. We compute each district’s average wages as the (weighted) average of compensation in the main occupation across the district’s workers (excluding workers with zero compensation), with compensation defined as the sum of “wages/salaries” and “tips, commissions, overtime pay etc” and measured in LKR. We compute average wages separately for estate and non-estate (mobile) workers. Estate workers are workers who live and work in Sri Lanka’s tea plantations and for which changing jobs is nearly impossible in the short run, as their housing is tied to the tea estate they work in (Chandrabose, 2015). Average wages are multiplied by 12 so that they are expressed in annual terms.

We use information from the 2016 HIES to count the number of mobile workers, estate workers, and farmers in each district (making using of the statistical weights to obtain total counts). We define a “worker” as a person with a main occupation (in agriculture, manufacturing, or services) that paid her above-zero employment compensation in the previous month. We count the total number of such workers in each district and the fraction among these who are estate workers. We then subtract the number of estate workers from the total number of workers to obtain the number of mobile (i.e. non “tea-estate”) workers. To compute the number of farmers, we first count the number of households in each district with positive landholdings. We only count land owned by the household (as opposed to leased, rented, etc.) with no housing built on top of it. We then estimate the number of farmers per district by multiplying the number of farming households by the average number of economically active agents per household.
was comparable in the two regions or even slightly lower in East Asia. However, since then growth in East Asia has been much faster and, while regional inequality also increased, its growth appears less concentrated than in Africa.

# **II. Data Construction and Methodology**

The regional distribution analyzed in this paper is built from nationally representative household surveys from as many countries as possible. PovcalNet is the starting point of this data set. PovcalNet is the global database of national household surveys compiled by the research department of the World Bank, and it is the source of the World Bank’s global poverty estimates.<sup>5</sup> Where possible we supplement PovcalNet with additional data directly from household surveys. In total, we use 106 surveys, with three surveys directly from household survey data and the remainder from PovcalNet.<sup>6</sup> Because most countries do not conduct annual surveys, we have defined a number of benchmark years – 1993, 1998, 2003 and 2008 – to which we match household surveys. We begin our analysis

> 5 <u>http://iresearch.worldbank.org/PovcalNet/. Data downloaded 07 January 2015, which refers to data last updated on</u> 08 October 2014. While PovcalNet attempts to make the consumption measures as comparable as possible, it is important to recognize that changes in the survey methodology can have important effects (Beegle et al., 2012). However, the coverage of regional GDP and population is too low when using the set of surveys that satisfy a higher standard of comparability. For example, these surveys cover less than half of regional GDP in 2008.

> 6 We excluded one income survey from Namibia to ensure that welfare is measured as consumption expenditure throughout.

3
- Ginglinger, Edith and Quentin Moreau (2019). “Climate Risk and Capital Structure”. SSRN Scholarly Paper.

- Goldar, Bishwanath and Piyali Majumder (2022). “What factors drive industrial plants in emerging economies to adopt ISO 14000 environmental management standards: Evidence from the Indian organized manufacturing sector”. _Environmental Development_ 44, 100771.

- Gray, Harriet Brookes, Vis Taraz, and Simon D. Halliday (2023). “The impact of weather shocks on employment outcomes: evidence from South Africa”. _Environment and Development Economics_ 28. Publisher: Cambridge University Press, 285–305.

- Gray, Wayne, Joshua Linn, and Richard Morgenstern (2019). “The Impacts of Lower Natural Gas Prices on Jobs in the U.S. Manufacturing Sector”. _The Energy Journal_ 40, 169–194.

- Greatrex, Helen et al. (2015). _Scaling up index insurance for smallholder farmers: Recent evidence and insights_ . Report. Accepted: 2015-01-23T10:34:21Z ISSN: 1904-9005. CGIAR Research Program on Climate Change, Agriculture and Food Security.

- Greenstone, Michael (2002). “The Impacts of Environmental Regulations on Industrial Activity: Evidence from the 1970 and 1977 Clean Air Act Amendments and the Census of Manufactures”. _Journal of Political Economy_ 110, 1175–1219.

- Greenstone, Michael, John A. List, and Chad Syverson (2012). “The Effects of Environmental Regulation on the Competitiveness of U.S. Manufacturing”.

- Greenstone, Michael, Rohini Pande, et al. (2022). “The Benefits and Costs of Emissions Trading: Experimental Evidence from a New Market for Industrial Particulate Emissions”.

- Grover, Arti and Valerie J. Karplus (2020). _The Energy-Management Nexus in Firms : Which Practices Matter, how Much and for Whom ?_ World Bank, Washington, DC.

- Guo, Mengmeng, Yicheng Kuai, and Xiaoyan Liu (2020). “Stock market response to environmental policies:
5,172 (~100%)|20 (<1%)|5 (<1%)|
|**Total**|**79,756(100%)**|**57,003** (**71%)**|**15,201(19%)**|**7,552(9%)**|

Notes: Mangroves in WMP 2 are potentially capable of migration; those in WMP 3 & 4 are not able to migrate. Totals may not sum to 100% due to rounding Source: Authors’ estimates as described in the text.

Once again, the vulnerability of mangroves varies a great deal by region and by country. In South Asia and Sub‐Saharan Africa, more than 90 percent of mangroves respectively are in

> 14 For this computation, the following adjustments were made: (i) The coastline in DIVA is not the same as the SRTM coastline. Therefore, the DIVA database was spatially joined to watersheds delineated from HydroSHEDS (Lehner et al 2008) to allow the connection between the DIVA coastline and SRTM coastline; (ii) For more complete coverage, the HydroSHEDS adapted DIVA data are extended via the closest HydroSHEDS grid cell (via the ESRI ArcGIS Expand command) in areas with mangroves and areas where the 450 sq. m grid of HydroSHEDS and the 90m coastline do not overlap; (iii) In some areas due to a data constraint, the mangroves are well outside the HydroSHEDS coastline and given a WMP value of NA; (iv) Elevation of mangroves is considered 0m above sea level.

> 15 Due to the spatial differences in the datasets, approximately 4% could not be reliably mapped into WMP categories (2, 3 or 4) directly, so the remaining mangroves are allocated to the country level by proportions)

15
levels in the survey years 2010, 2012 and 2014 for the _Pooled_ model and by the change for 2010‐12 and 2012‐14 for the _Panel_ model. _X_ is a set of household‐specific controls that vary over time, such the levels of education, labor and land endowments in survey years for the _Pooled_ model and the difference between years for the _Panel_ model. _X_ also includes time‐ invariant demographic characteristics, such ethnicity and gender.<sup>9</sup> _T_ measures time‐fixed effects to neutralize common trends over time. _U_ is a random, idiosyncratic error term. The descriptive statistics of these variables are provided in Table A.1.

A main concern when fitting models to estimate weather impacts on economic outcomes is endogeneity bias. Problems of reverse causation (e.g. high consumption growth in one place leading to environmental depletion) are minimized by taking the historic risk profile and not measuring actual environmental conditions. Yet the model is likely to suffer from omitted variable bias caused by the potential correlation of risk variables with other commune characteristics that determine living standards. Such omitted variable bias could be minimized by fitting a fixed‐effects linear model using a within‐regression estimator based on the Panel data set as in Narloch (2016). Yet such a model does not allow to disentangle the impact of time‐invariant risk factors so it cannot be applied for the purpose of this study. To minimize omitted variable bias in this study, a set of

> 9 The data sets allow to control for land and labor inputs of some activities. These are, however, not included in the regression analyses due to potential endogeneity biases, as higher risks also determines land and labor inputs.

13
and the percentage of workers female. Model 2b, shown in Table 4, includes additional firm-related characteristics only available in the ELMPS data sets, such as the size of the firm’s capital (in categories), net revenue per worker, the presence of non-household members among the workers, separately for relatives and non-relatives, and the type of workplace. Model 3, also shown in Table 4, adds to the firmrelated characteristics in Model 2b some characteristics related to the firm owner, specifically the owner’s sex, educational attainment, broad occupational group, and age group.

We present in Table 2 odds ratios from a logit regression for the probability of being formal using the common set of variables across all four data sets (Model 1). In the first column we show results for the pooled data, with dummy variables indicating the data source. Separate results by data set are shown in the four subsequent columns. As expected, firm size is a strong predictor of formality in all four data sets. According to the pooled model, compared to one-person firms, the odds of being formal are 66% higher for two-worker firms, more than twice as high for 3-4 worker firms, and more than five times as high for 5-24 worker firms.

Older firms are significantly more likely to be formal. Compared to firms aged one year old, those zero are less likely to be formal, usually significantly so. The surveys vary in terms of which ages start to predict significantly higher formality, with the ELMPS 2018 showing a later and weaker relationship. In the pooled model, formality largely rises steadily with age, and the “don’t know” firms tend to follow the pattern of older firms (likely don’t know means a firm is old enough that the owner cannot remember when, exactly, it started).

All industry groups except “various professional activities” have lower odds of formality compared to wholesale and retail trade, usually significantly so in the pooled model. The industry that is
and solar energy, with contributions from biomass, biogas, geothermal and solid wastes (Shem et al., 2019). However, their market competitiveness is strongly challenged by the low electricity and coal prices.

In correspondence to the diffusion of electricity, also the telecommunication sector had a fast and wide development. Since 1995, the year of introduction in the country of the global system for mobile communication (GSM) and code division multiple access (CDMA), the telecom market has grown about 79% per year untill 2008 (Hwang et al., 2009). Initially, the Vietnamese telecom market was a monopoly with only one firm, the Vietnam Post and Telecommunications Corporation, and a weak competition between its two subsidiaries Vinaphone and Mobiphone (Hwang et al., 2009). Important reforms started at the beginning of the 2000s, when the government promoted competition by opening to foreign companies. The first Korean mobile telephone services started in 2003 and since then the competition continued growing, although the government set up a pricing control regulation (Hwang et al., 2009). A second policy stepstone was the 2005 “Program on the provision of public telecommunications services till 2010”. The objective of the programme was to improve telecom access to all households living in areas with a tele-density below 2.5 sets per 100 inhabitants by subsidizing: the development of telecom infrastructures in all districts; public telephone and internet centers; fixed telephone and internet services to rural users. In five years the program achieved many of its objectives. At the end of 2010 the tele-density raised to 16 sets per 100 inhabitants, the penetration of the internet services doubled, the public telephone and internet centers were operating in 97% of communes across the country (Thai and Falch, 2018).

# **3. Literature review**

In Vietnam the General Statistics Office (GSO) regularly collects household consumption data as part of the longitudinal household living survey. The first Vietnam Living Standards Survey (VLSS) was conducted in 1992 and
adults were more positive about their future living standards than the older adults: While 59 percent of young adults believed that the standard of living is getting better, 47 percent of older adults agreed.

In the post-ISIS period, more Sunnis and adults living in Baghdad reported that their living standards were improving. By contrast, Kurds and Shias report no improvement since the end of ISIS-control.

**Figure 3: Standard of living is getting better (% of adults)**

<!-- Start of picture text --> 59%<br>53%<br>51%<br>47%<br>43% 44%<br>41% 41%<br>Baghdad Sunni Shia Kurds<br>2014-2016 2018-2019<br><!-- End of picture text --> Source: Gallup World Poll.

While the relationship may be highly endogenous, data show that changes in perceived standards of living mirror fluctuations in oil prices in Iraq (figure 4). By 2016, both followed an upward trend and 54 percent of Iraqi adults reported that their standard of living was improving, at par with other oil exporting economies. This also reflected an expansionary fiscal policy fueled by the rise in public spending. However, with the price of Iraqi oil exports dropping to US $38 per barrel during 2020, confidence in the economy may have dropped further. As of February 2021, oil export prices rebounded and surpassed US $60 per barrel for the first time in a year, but the impact of this on confidence is yet to be assessed.

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|59.291|4.420|
|Control S.D. in 2013|51.328|0.864||51.950|51.950|51.190|51.190|51.950|0.890|
|Notes:<br>Fixed effects regressions with firm and tim<br>*, **, *** denote significance at the 10, 5, a<br>Level denotes monthly level of employme<br>Firm data are taken from firm records, PILA<br>Conditional is for the group of surviving fir|e fixed ef<br>nd 1 perc<br>nt; I.H.S. is<br>data are f<br>ms, uncon|fects. Stan<br>ent levels<br>inverse h<br>ormal emp<br>ditional co|dard error<br>respective<br>yperbolic s<br>loyment d<br>des emplo|s clustered<br>ly.<br>ine transf<br>ata from<br>yment as|at the fir<br>ormation.<br>administra<br>zero once|m level are<br>tive record<br>firm dies.|in parent<br>s.|heses.||

39
an equilibrium exchange rate based on the m odel describ ed<br>iChdtl2017|n ouare e a. ()<br>Credit / G D P<br>D om estic credit to private sector as a p ercentage of G D P<br>Tim e to clear im p orts (log)<br>Logarithm of the numb er of days required to im p ort based on the D oing Business 06-15 m ethodology<br>Logistics p erform ance index<br>O verall score of the logistics p erform ance index obtained as a weighted average of the country scores on six<br>key dim ensions: e¢ ciency of the clearance process (i.e., sp eed, sim plicity and predictability of form alities)<br>by b order control agencies, including custom s; quality of trade and transp ort related infrastructure (e.g.,<br>p orts, railroads, roads, inform ation technology); ease of arranging com p etitively priced shipm ents;<br>com p etence and quality of logistics services (e.g., transp ort op erators, custom s brokers); ability to track<br>and trace consignm ents; tim eliness of shipm ents in reaching destination within the scheduled or exp ected<br>|delivery tim e.<br>Internet use<br>Percentage of the p opulation that uses the internet<br>English as second language (% )<br>Percentage of the p opulation that sp eaks english as a second language<br>Fem ale lab or force partic. (% )<br>Share of fem ales in total lab or force<br>Im p ort elasticity<br>Average for each country of the im p ort dem and elasticities estim ated by K ee, Nicita, and O larreaga (2008)<br>for all its m anufacturing products de...ned as those with HS 6-digit
* significant at 85% level of confidence.

** significant at the 80% level of confidence.

Decentralizing administration results in a reduction of resource costs initially, followed by an increase (but at a slower rate) as the coefficient for the second order effect is negative and significant while that of the third order effect is positive and significant but smaller than the second order effect. So the subsequent increase in resource costs does not totally offset the initial gains, and the overall effect of decentralization is to reduce resource costs. Tests on the shape of the function (convex and then concave) are accepted at a 99% level of significance. The optimal degree of decentralization of administration functions, defined as the point achieving the least unit costs of administration, is found to be between more than 50 and less than 90 percent local government contribution to financing administration functions (see figure 7).

The opposite is true with respect to maintenance where decentralization was found to result in a slight initial increase in unit costs of maintenance. The subsequent decrease in maintenance costs with increasing levels of decentralization (second order effect) was found to be larger but significant only at the 85% level of confidence. It may be that decentralized units of government undertake more expensive maintenance technologies to provide better quality of roads. This conclusion cannot be made from the model specification in table 5a and we need to compare the findings of other model specifications as shown in figure 8.

Using the results of the cross-country panel and the assumption that resource costs and preference costs are independently determined (i.e., decisions to maintain a road or constuct a new link are mode independently of the decision to allocate a specific expenditure budget for that activity), we find decentralization to be neutral with respect to unit costs of construction.

From Table 5b we see that the country-specific fixed effects are jointly and independently (for most countries) significant. These effects are relative to the USA which is used as a normalizing country for comparison purposes. Korea is the
of what we know about job losses in other segments of the labor market. Section 6 concludes with a discussion of the methodology, policy implications and directions for future research.

# **2. Related Literature**

Our work contributes to the rapidly expanding literature aiming to understand the impacts of COVID-19 on labor markets. Early in the crisis, most of the empirical studies focused on high-income countries given that real time data is more available for them. Many studies highlighted the heterogeneous effects of the pandemic on different segments of workers and traced these in part to the differential ability to work remotely (Adams-Prassl et al., 2020), but also to issues such as infection risk, labor market attachment or tenure, and policy support measures (Lee et al., 2021; Chetty et al., 2020; Montenovo et al., 2020; Dang, Huynh & Nguyen, 2020). Prassl et al. (2020), for example, find that job losses differed substantially even across rich countries. They were much lower in the initial phase of the crisis in Germany, at 5 percent, than in the U.S. (18 %) and the U.K (15 %). They also find that the pandemic has exacerbated existing inequalities across workers within countries. Chetty et al. (2020) use real-time data from private companies for the United States and show that low-wage workers are more likely to experience job losses that lasted several months compared to high-wage workers. Lee et al. (2021) also find that the negative impact on employment was larger for women, minorities, the less educated, and young people in the United States, and Albanesi & Kim (2021) find similar gendered effects on the US labor <mark>market. In addition, del Rio-</mark> Chanona et al. (2020) estimate supply and demand shocks in the U.S. in the earlier stage of the pandemic using pre- and postCOVID datasets. In Australia,
**Table S19. Relationship between the UN Human Development Index and ODIN, Open Data Barometer, and Global Data Barometer scores, 2013-2022**

||ODIN -|ODIN -|ODIN -|ODB -|ODB -|ODB -|GDB -|
|---|---|---|---|---|---|---|---|
||Model 1|Model 2|Model 3|Model 1|Model 2|Model 3|Model 1|
|ODIN Score|0.006***<br>(0.00)|0.000<br>(0.00)|0.000<br>(0.00)|||||
|Open Data Barometer<br>Score||||0.005***|0.000|0.000||
|||||(0.00)|(0.00)|(0.00)||
|Global Data Barometer|||||||***|
|Score|||||||0.006|
||||||||(0.00)|
|Trade (% of GDP)|||0.000|||0.000*||
||||(0.00)|||(0.00)||
|Agriculture, forestry,||||||||
|<br>fishing value added (%<br>of GDP)|||0.000|||-0.001||
||||(0.00)|||(0.00)||
|Manufacturing value<br>added (% of GDP)|||0.000|||0.001||
||||(0.
The environment for job creation is particularly challenging in Jordan. Before the impact of the COVID-19 crisis, Jordan experienced only modest level and low productivity growth. With the impact of COVID-19, the unemployment rate increased to nearly 25% and women and youth, whose unemployment rates were already the highest, were hardest hit by the crisis. Creating meaningful economic opportunities for refugees and Jordanians alike will require tackling underlying economic constraints that lead to persistently high levels of unemployment and informality. According to the World Bank’s Job Diagnostic for Jordan (Winkler, Hernan, & Alvarez, 2019), the low level and quality of job creation in the private sector is mostly explained by firm dynamics: limited entry and growth of firms not driven by the most productive firms, while firms exiting are not always the least productive, all resulting in a productive structure dominated by small/micro, low-productivity firms employing half of private sector workers, including two-thirds of informal workers. The Government of Jordan launched a National Employment Plan in Response to the Crisis, but structural reforms are also needed to help the private sector create more jobs (World Bank, 2021a).

However, providing access to economic opportunities is not a guarantee that gender gaps in access to economic opportunities for either refugees or Jordanians will be reduced if men have full control of gains in access to economic opportunities, as determined by patriarchal norms. Economic empowerment programs for refugee women, in particular, should build in guidelines for their protection and should engage refugees in promoting more gender-equitable relationships (Heilman & Barker, 2018).

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