rafmacalaba/data-use-mentions-extended huggingface dataset:
broadband penetration, employment rises from 0.2 to 0.3 percent, or about 293,000 jobs for an economy at less than full employment.

Strand & Toman (2010) discuss the short-run and long-run effects of green stimulus efforts. They find that most green stimulus programs that have large short-run employment and environmental effects are likely to have less significant positive effects for long-run growth, and vice versa. This is because programs that create more (less) job opportunities tend to employ lower- (higher-) skilled workers, and therefore yield smaller (larger) long-term growth effects. For example, most jobs in the wind or solar PV sector are highskilled; investment in these sectors can contribute substantially to future technical development and growth. Most jobs in solar-thermal and biomass production are by contrast lower-skilled, with less scope for technological enhancement and learning effects for the individuals employed. One-fifth of employees in clean-energy production and energy-efficient occupations have college degrees (Muro _et al._ , 2019).

By the same token, capital-intensive investments such as digitalization and 5G are more likely to show economic benefits over the longer term and therefore much less likely to create strong short-term stimulus effects. Strand and Toman’s (2010) analysis implies a tradeoff in many cases between skill levels and labor intensities, reinforcing the point that different instruments are needed for addressing different problems. A recent paper by Hepburn _et al._ (2020) based on expert surveys also shows that there are limited policy options that generate both large economic multipliers and climate impact.

Despite the recognition of limited availability of triple win-win-win opportunities, countries that invested the most in green stimulus after the Great Recession such as the United States, Germany, and the Republic of Korea boosted their economies and created jobs. The green stimulus supported 900,000 job-years in clean energy fields from 2009 to 2015 in the United States, and 156,000 new green jobs from 2009 to 2011 in Korea. Korea included green measures in almost 80 percent of the stimulus in
In this paper, we provide a comprehensive analysis of poverty and inequality trends in Viet Nam in the past two decades. We compile new, extensive panel data at the province level over the past two decades using the Vietnam Household Living Standards Surveys and other data sources. We find that although average incomes between provinces tended to converge, there was a significant rise in within-province inequality over time. Within-province inequality was three times larger than between-province inequality in 2020. While economic growth helped reduce poverty, greater inequality could negatively affect poverty gap and poverty severity as well as economic growth. Although poverty levels in the country declined significantly, the poor were increasingly segregated in certain provinces. In particular, provinces with a larger share of ethnic minority groups had greater poverty levels. Economic transitions toward wage and service economies could have beneficial impacts on growth and poverty reduction but could also increase poverty segregation.

SDG 1 calls for an end to poverty in all its forms. Certainly, the goal refers to a broader notion of poverty. Admittedly a limitation of our analysis is that we can focus only on monetary (i.e., consumption and income) poverty and do not measure changes in multi-dimensional poverty in Viet Nam. However, goal 1 also emphasizes the need to address poverty in specific underserved geographic areas within each country. To that effect, our analysis examines a new aspect of poverty in the country and reveals a rise in the segregation of its poor. This new finding lends further support to those in the existing studies that poorer population groups are both spatially and ethnically concentrated (Benjamin, Brandt, and McCaig, 2017; Lanjouw, Marra, and Nguyen, 2017; Fujii, 2018). As such, although Viet Nam has made substantial progress in reducing overall poverty, geographically targeted policies can be designed to reach out to the remaining poor.

19
combined-sample <mark>imputation following Rendall et al. (2013). Initially, this te</mark> chnique was developed to correct for missing values on items from survey data. The method was expanded to cases where variables are not present by design because the question was not asked at all. To still allow for the same adequacy definition across the sample of 64 countries (all seven adequacy dimensions aggregated into _AHI_ <mark>), we pool two nationally representative surveys (a) the countries</mark> HCES <mark>and (b) the respective De</mark> mographic and Health Survey (DHS) which both provide standardized information on a range of housing conditions to impute existing DHS data into HCES. We consider <mark>DHS as the “donor” survey and the H</mark> CES <mark>as the “recipient” survey. We take variables in common between the two data sets (i.e., location and wealth quintile of HH) to identify HHs with common characteristics in both samples to then impute on the</mark> _<mark>missing</mark>_ <mark>dimension.</mark> <mark>Two conditions must be satisfied for unbiased estimates (Rendall et al. 2013). First, the two pooled samples must be drawn from the same underlying population or “universe”</mark> (Carrig et al. 2015, Rendall et al. 2013; Rubin 1986). This assumption allows to treat the unobserved variable in the recipient survey as missing by design since the question has not been asked, which justifies the imputation across surveys (e.g., Gelman et al. 1998). <mark>Second, a number of variables must be “jointly observed” in both the donor and the recipient samples. Van Hook and colleagues (2015) demonstrate that the violation of the latter condition results in severe bias both in magnitude and direction.</mark
of shorter job tenures could be a potential reason behind tensions in Europe’s social contracts (Bussolo and others 2018).

The second set of arguments motivating a study on the evolution of job tenure points out that, beyond being an indicator of job stability, tenure also reflects the efficiency of the job search process (Pries and Rogerson 2022). When workers and employees are satisfied with the job match, only a strong shock can sever the employment relationship, but bad job matches are more prone to dissolve quickly. Changes in the average length of job tenure can then indicate changes in how good job matches are on average in the economy. Tenure is also important for human capital

> 1 According to data from the 2015 round of the International Social Survey Programme, 59 percent of European respondents considered job security a “very important” and 33 percent considered it an “important” attribute of a job (ISSP 2017).

> 2 Some papers have focused on documenting the heterogeneity in _levels_ of tenure, but fewer have analyzed the time _trends_ of tenure (see infra the literature review section for more details).

> 3 <u>Manning and Mazeine (2022)</u> find that in the US, UK and Germany, subjective perceptions of the probability of job loss have remained stable over the past 30 years. They find some differences in perceptions between subgroups of the population, but there are no substantial differences in trends over time. Note also that Molloy, Smith and Wozniak <u>(2021)</u> show that subjective perceptions of job stability can be more aligned with changes in short tenure jobs than in long tenure jobs.

2
the U.S. Moreover, our datasets do not present any obvious censoring or threshold for inclusion in the census. To the best of our knowledge, our contribution is the first that encompasses the entire manufacturing sectors—including informal establishments—of Sub-Saharan African countries in a comparative fashion.<sup>8</sup>

# **Summary of Firm-level Data from Selected Countries in Sub-Saharan Africa**

**Burkina Faso.** The data comes from the census conducted in 2016 by Institut National de la Statistique et de la Démographie (INSD). It covers establishments mainly in the non-agriculture sector and contains records such as an establishment’s industry classification, age, employment, accounting practices, state ownership, and foreign equity. Establishments are classified into formal and informal depending on whether they have a registration number (with mobile trade and mobile credit, tax identification number) and use SYSCOA as an accounting standard.<sup>9</sup>

**Cameroon.** The data is obtained from the first Recensement Général des Entreprises (RGE) conducted in 2009 by the Institut National de la Statistique (INS). The RGE covers all businesses of the modern sector (that is, excluding non-commercial agriculture) with a physical location, specifically excluding the street trading industry (hawkers, peddlers, and others), the (car and bike) taxi industry, and the callbox industry. It provides detailed information such as registration with administrative agencies, tax payment, accounting practices, age, employment size, state ownership, and foreign equity. In the census, formal establishments are considered formal if they prepare a Statistical and Fiscal Declaration (DSF) or those that do not prepare a DSF but which keep an operating account and a partial account of balance sheets.<sup>10</sup>

**Ghana.** The data comes from the 2014 Integrated Business Establishment Survey (IBES) conducted by the Ghana Statistical Service. The IBES is an economic census of all business
in the raw leader growth average is only associated with a 0.17 percentage point increase in the LS leader effect, suggesting high average growth rates are only weakly informative about an individual leader’s performance (Online Appendix Figure 1).<sup>32</sup>

**Statistically significant leaders.** We define the best (and worst) leaders for growth as any leader for whom the estimated LS leader effect is positive (or negative) and significant at the 95% level. Figure 2 plots the combination of the LS leader estimate and the size of its standard deviation for autocracies and democracies (each leader with tenure ≥ 3yrs is a dot).<sup>33</sup> Leaders who are significantly good for growth are in the bottom right corner and their negative counterparts are in the bottom left corner. Both have leader effects (in absolute value) more than twice as large as their error. The significant leaders are listed in Table 3. Leaders with insignificant growth contributions are in the center region.

Our first main result is that the vast majority (93.5%, 602/646) of leaders with tenures ≥ 3yrs and complete growth data have a contribution insignificantly different from zero.<sup>34</sup> That is, these leaders have errors too large relative to their LS leader effect. This does not cast doubt on the existence of leader effects in general, since Table 2 shows sizable variation in underlying leader quality (as in Jones and Olken 2005). Rather, the low number of significant leaders instead μμ exposes the difficulty of detecting which leaders represent a draw of high or low quality amid the σσ

> 32 Online Appendix Figure 2 shows that most of this difference is due to the shrinkage factor . 33 We focus on leaders with tenures of 3 years or more because annual growth data introduce substantial rounding  We focus on leaders with tenures of 3 years or more because annual growth data introduce substantial rounding ψψ

33 We focus on leaders
(by life stage group/outcome). In the first step, each reviewer independently screened the list of articles based on the title and abstract. In the second step, one reviewer scanned the full text of the article and noted the presence or absence of each of the components required by the inclusion criteria. Disagreements on study selection were resolved by consensus and discussion with other reviewers.

# **Approach to data charting and synthesis of results**

A standard data charting table was jointly developed by two reviewers and data from eligible studies were charted by the lead reviewer, grouped by life stage. The table columns captured the relevant information on key study characteristics (e.g. author, publication year, study design, sample size), age and geographic location of study participants, type of diet exposure and metric used, type of nutrition/health outcome(s) and measure used, type of economic cost and measure used, and relevant key findings (e.g. strength of association observed, economic cost 6
882,150|
|Higher education|14%|35%|15,882,150|
|Previous formal sector attachment|39%|43%|15,964,933|
|Average months worked (cumulative)|5.9|4.6|15,021,274|
|Average real wages (cumulative)|1.7|12.4|8,368,115|

_Note:_ The worker-level statistics in panel A are based on the sample of workers in firms with a non-missing GFC firm shock measure in 2008 used in the regressions. The firm-level statistics in panel B are based on all firms with non-missing GFC shock measure in 2008 used in the main regressions. The worker-level statistics in panel C are based on all workers in the IESS database. Real wages are expressed in local currency in 2010 prices.

59
Table A.14: Robustness checks, using 2018 data. Estimating the effect of migrants’ legal status on wages after return, unconditional on return

||(1)<br>Migrate|(2)<br>Illegal|(3)<br>Return|(4)<br>Log of<br>hourly wage|
|---|---|---|---|---|
|Age|-0.001|-0.042|0.009|-0.002|
||(0.001)|(0.037)|(0.009)|(0.005)|
|Age squared|0.000**|0.000|-0.000|0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|
|No education|0.002|0.444***|0.155***|-0.332***|
||(0.002)|(0.127)|(0.035)|(0.026)|
|Primary or preparatory|0.001|0.243|0.133***|-0.325***|
||(0.003)|(0.151)|(0.039)|(0.027)|
|Secondary|0.001|0.258**|0.099***|-0.219***|
||(0.002)|(0.112)|(0.028)|(0.021)|
|Rural|0.003|-0.064|-0.036|0.003|
||(0.002)|(0.095)
e.g. Ogude 2017; Verwimp and Maystadt 2015; Mabiso et al. 2014). Some of the empirical papers we review offer an overview of the literature, such as Borjas and Monras (2017) and Clemens and Hunt (2017) who revisit several cases of large and sudden inflows in high-income countries, but none of these papers covers the range or scope of this work. The literature on the impacts of forced displacement on the displaced themselves was very limited until recently (see the reviews by Kondylis and Mueller 2014 and Ruiz and Vargas-Silva 2013) but has also started to grow in the last years (e.g. Gimenez-Nadal, Jose Ignacio, José Alberto Molina, and Edgar SilvaQuintero 2018; Fransen, Vargas-Silva, and Siegel 2018). The results of this literature are linked to our review as the impacts on the financial assets, human capital and psychological well-being of forced migrants will in return influence how they impact the consumer and labor markets of the host community.

Most of the papers covered are published in peer-reviewed international journals and most of these journals are top ranked journals in their respective disciplines. This set of papers is complemented by papers published as working papers in reputable series by known authors using standard modeling techniques. The oldest paper covered is dated 1990 and the newest 2018. No papers prior to 1990 were found. The episodes of FD included in this literature span from 1922 to 2015 and cover 17 of the major FD crises of this period, those that received the most attention from scholars. These are distributed between high, medium and lowincome host countries and include episodes of FD in the US and Europe, Middle East, North-Africa, SubSaharan Africa and Latin America. Only empirical papers in economics with original results are covered by the review.

Almost all studies are described as _natural experiments_ by the authors but face major measurement and identification challenges. Availability of micro data is one of the challenges and the first explanation of why these types of studies have emerged only very recently.<sup>
|289.830<br>(16.43)**|287.410<br>(11.47)**|258.563<br>(17.47)**|235.342<br>(19.94)**|
|Observations|424|207|234|246|
|R-squared|0.46|0.41|0.40|0.54|

a Huber-White robust t statistics in parentheses Robust t statistics in parentheses significant at 5%; ** significant at 1% 21
<br>5<br>New employees added per year<br>0<br><!-- End of picture text --> The revenue generation capacity of sampled firms is also limited, with an average of US$55,000 per year in gross sales. The median firm, however, generates only US$12,000 per year, and 50 percent of firms generate between US$5,800 and US$34,000 in sales per year. In general, firms appear to have a limited ability to generate sufficient income to cover the basic needs of firm owners and their families or, 3 Although it is not possible to compute exact survival rates with a cross-section of firms, looking at the age distribution provides a rough idea of the extent of “churning” among firms; if the age composition is biased towards young firms it is likely that survival rates are low, and vice-versa if the distribution is biased towards older firms. In the case of Ecuador, the fact that only about 20 percent of firms is less than 2 years old suggests that the survival rate is higher than in OECD countries, where it is estimated that between 20-40 percent of entering firms fail within the first two years, so that–assuming that there is no firm entry in a 2-year period—at most 80 percent of firms would be older than 2 years. Since it is not realistic to assume zero entry for two years, it must be the case that at any given time the age distribution shows less than 80 percent of firms being two years and older. In Ecuador more than 80 percent of firms are older than two, which means that the survival rate is probably much higher than the average for the OECD. In turn, this is consistent with low rates of firm exit, which typically occur when there are high exit costs. See López-García and Puente (2006).

> 4 When comparing this low employment generation with the Enterprise Survey data for Ecuador—which includes
100 scale) which ―is a composite measure of the absence of tariff and nontariff barriers that affect imports and exports of goods and services”; a higher score indicates less barriers (more freedom).

Both specifications explain about 80% of variation in the prevalence of informal dependent employment and slightly more than a half of variation in unemployment and discouragement; in the case of the prevalence of informal self-employment, specification {1} explains 64% of variation, whilst specification {2} explains 79% (these R-squared are from underlying OLS regression). There are no cases of significant effects having opposite signs in

> 13 Note that this measure is likely to capture the employment-depressing effect of minimum wage better than the alternative which uses economy-wide average wage.

> 14 Alternative specifications (available on request) which use union density lagged two years back (either from the previous ESS round, or from OECD database) give similar results, but with larger estimated mean squared errors.

> 15 Labor market regulations are not included in this index, as they are covered by another Heritage index.

12
has enabled a sustained reduction of poverty, a large percentage of its population is still battling poverty and extremely vulnerable to natural disasters. This means that gains are threatened and could be wiped out in the aftermath of a major disaster. A better understanding of the impacts and potential coping mechanisms is valuable input to inform poverty reduction strategies and policies taking into consideration disaster and climate risk management considerations.

Overall, the methodology of this study improves our understanding on how we model impacts and economic outcomes in Central America, providing a systematic approach to quantifying the effects of disasters on poverty and other well‐being indicators in the region. The comparative approach, in terms of hurricane damage indexes, provides a better understanding of how windstorm could impact economic and social outcomes.

One of the key innovations of this study has to do with the use of an accurate and comprehensive model that generates high‐resolution, surface‐level, sustained wind speed data of historic hurricanes and tropical storms in Central America proposed by Pita el al. (2015). The windstorm model was calibrated and validated for Central America, enabling a more accurate spatial specification and precise inland measures of wind speed. The three hurricane damage indexes (MSWS – unweighted wind speed, IHD – population‐ weighted index, and WEI – area‐weighted index) showcase different aspects of hurricane damage, however all three indexes provide similar conclusions in terms of the negative impacts in GDP per capita, income, and poverty. Results vary slightly across each index but are not statistically different.

The GDP per capita and household analysis complement each other. While data for GDP per capita have been of better quality and statistical significance, they underestimate the effects of hurricane windstorm on the most vulnerable population. GDP per capita does not begin to capture the impact on well‐being resulting from these natural hazard events. It requires the combined insight from both sets of analyses to contribute to improve the design of programs and policies, which could then be aimed more effectively 20
2.6|1.6|-1.1|
|Hong Kong|3.9|4.9|5.3|7.0|6.6|5.2|-0.3|2.2|3.8|
|Singapore|8.7|7.0|7.6|0.9|2.0|2.0|2.7|2.8|1.8|
|Taiwan|6.0|5.7|6.9|4.6|2.5|-0.5|0.4|0.2|0.2|
|Japan|1.5|3.9|0.9|-0.1|0.1|1.7|-3.6|-1.1|-0.2|
|USA|2.0|2.8|3.8|2.8|2.9|2.3|-1.9|-1.1|-0.2|

_The Size of the Government's Implicit Liabilities_ We now discuss the impact of the banking crises on the governments' potential liabilities. We proceed in two steps. First, we provide evidence based on estimates of loan default rates prior to the crises. Second, we discuss the size of government outlays in Korea, Thailand, Indonesia and Malaysia associated with efforts to reorganize their financial sectors.

Corsetti, Pesenti and Roubini (1998a) provide estimates on loan default rates in Asia prior to the crises. These are summarized in the first columns of Tables 6 and 7. Note the sharp difference between default rates in the crisis and non-crisis countries. In the former they range from a low of 14 percent in the Philippines to a high of 19 percent in Thailand. In all of the non-crisis countries the ratio of non-performing loans to total loans is roughly 4 percent. Using these
.15|-4.85|-5.37|-5.46|I(1)|
||_||_Critical_|_values:_|||||
||1%|-2.58|-3.46|-4.00|-2.58|-3.46|-4.00||
||5%|-1.94|-2.87|-3.43|-1.94|-2.87|-3.43||
||10%|-1.62|-2.57|-3.14|-1.62|-2.57|-3.14||

# Figure A.1: Trade Shares

<!-- Start of picture text --> Trade Shares of Argentina's Major Trade Partners (%)<br>30<br>Brazil United States China Germany Chile<br>25<br>20<br>15<br>10<br>5<br>0<br>Source: IMF DOTS<br>1980 1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text --> 35