rafmacalaba/data-use-mentions-extended huggingface dataset:
|Variable|N|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
|R,thd.hryvnas|40440|8001.91|16948.02|0.08|548158.70|
|L, workers|40440|171.57|265.52|1|6779|
|K,thd.hryvnas|40440|3111.47|6964.35|0.07|183732.00|
|M,thd.hryvnas|40440|6971.82|17583.37|0.1|706991.3|
|I,thd.hryvnas|30357|693.39|2112.28|0|89370.69|
|Serv. Lib.|40440|0.36|0.57|0|4.85|
|Serv. Lib. (FDI)|40440|0.34|0.64|0|29.31|
|Exporter, Yes=1|40440|0.34|0.47|0|1|

Table 2: Descriptive statistics services (Arnold et al., 2011; Fernandes and Paunov, 2011) liberalization on productivity. We follow a similar identification strategy. The strategy relies on the assumption that manufacturing firms that use services more intensively gain more from services liberalization. For each period _t_ , we construct a firm-specific index of services liberalization by interacting a subsector-specific index of services liberalization with firm- and sub-sector-specific intensity of services use. We further look at the _within_ firm variation in TFP and relate it to the changes in the firm-specific index of services liberalization. To recover the TFP measure, we estimate the production function for each manufacturing industry (1-digit NACE classification) by the Olley-Pakes procedure (Olley and Pakes, 1996), controlling for sub-industry-specific demand and price shocks as suggested by De Loecker (2011). We identify demand and
||(1.097)|(1.746)|(1.649)|
|Dummy_Mais Educação_* year 2009|–0.148|–5.373***|–2.572|
||(0.963)|(1.661)|(1.650)|
|_Mais Educação * l_og(city population)|0.382|2.410|7.768|
||(5.569)|(10.34)|(11.34)|
|_R_ <sup>2</sup>|0.736|0.755|0.741|
|Observations|2,595|2,595|2,595|
|Dummy year 2009|Yes|Yes|Yes|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP. _Note:_ Treatment group are schools that joined _Mais Educação_ in 2008 and remained in the program until 2011. Municipal population variable is in log. This table reports coefficients from the ordinary least squares estimation of equation (6) in the paper. The dependent variable is in column (1) is Portuguese grades in _Prova Brasil,_ in column (2) mathematics grades in _Prova Brasil,_ and in column (3) dropout rates. All variables are at school level. ***significance at the 1% level; **significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching weights. Panel A reports these estimates for the schools from the fifthgrade sample. Panel B reports these estimates for schools from the ninth-grade sample. City size is captured by
a larger share of mortality burden. At the same time, emerging evidence from advanced economies shows that within those countries, households with lower socio-economic status and those with more precarious jobs have been more affected, in terms of job and income losses, by the restrictions aimed at containing the COVID-19 pandemic (Adams-Prassl et al., 2020; Chetty et al., 2020; Crossley et al., 2021).

Even when pandemic restrictions are disproportionately affecting jobs and incomes in low-income households, the impact on income inequality, at least in the short term, will also depend on the size and the effectiveness of the government response measures. Clark et al. (2021), based on data from five European countries, find that disposable income inequality fell between January and September of 2020, accounting for the government mitigation measures, but would have increased had those measures not been put in place. Finally, even when government response programs can mitigate short-term inequalities related to income losses, the COVID-19 pandemic can still have long-term effects related to differential losses in learning and human capital, or long-term unemployment effects.

Against this background, this paper aims to contribute to our understanding of the inequality effects of the COVID-19 pandemic by (i) extending the evidence base on the inequality impacts of the pandemic to emerging markets and developing economies (EMDEs); (ii) providing a first account of the impact of the pandemic on inequality for a large set of EMDEs; and (iii) analyzing the distributional patterns of early recovery related to the relaxation of the policy stringency measures. To address these questions, the paper pieces together empirical evidence on impact of the pandemic on inequality in EMDEs from existing pieces of research – updated in some cases with more recent data as they become available – along with evidence from past crises. To estimate the distributional impacts of COVID-19, the paper primarily draws on novel

> 2 This is of similar magnitude to the Great Recession increase in disposable income inequality in EU countries such as Greece, Italy, Portugal and
22|

Sources: calculated from Tabulation on the 2000 Population Census of the P. R. China, Table 4-1b, page 989-1042.

Note: 1, agriculture; 2, construction; 3, manufacturing; 4, transportation, and telecommunication; 5, retail and hotel; 6,finance; 7, services; 8, health, education, and research and development; 9. government institution.

The living conditions are also much better in towns in Zhejiang than in other two provinces. Table 4-1 presents the availability of tapped water and composition of fuel resources for cooking in households in towns in three provinces. Data show that in Zhejiang and Sichuan, majority of households in towns were equipped with tapped water, whereas in Henan only about half of town residents had access to the tapped water, which was lower than the national average of 70 percent.

Furthermore, most of households in towns in Henan (two-thirds) used coal for cooking, about twice as much as likely the national average (one-third). The percentage of using coal for cooking in Sichuan was slightly higher than the national average. In Zhejiang, gas became the major fuel source for cooking, which was about twice as much as likely the national average (41%). Nationwide, a little over 20% of households were using wood/grass as their cooking fuels in towns in China, and the level of using wood/grass as fuels for cooking were found to be lower in all three provinces examined in this paper.

> 3 Scholars have emphasized that at the local town level there was a “local state corporation” in parts of rural China, an entity which related local government closely with enterprises and people (Oi 1992, 1999).  This might explain why some of rural towns had higher share of population working in the local government institutions.

5
LMICs for which data are currently available. Since our focus is global, we have not included metrics specific to individual countries or regions. Data on metrics of nutritional and health status are well established and not reviewed here; UNICEF and WHO maintain global databases in this area (see Appendix Tables 4 and 5). Schooling outcomes and standardized test outcomes are available from UNESCO and key ones are included in the World Bank World Development Indicators database.

To help assess **energy sufficiency** , data on per capita food supply have been collated regularly at national level by FAO (and give some indication as to whether availability meets nutrient requirements). Measures compare food available and distributed and what is needed, according to the composition of the population by age, gender and physical activity levels. The Prevalence of Undernourishment (PoU, one of the suite of food security measures maintained by FAO) provides an estimate on how many people lack enough dietary energy for a healthy and active life. The prevalence of moderate or severe food insecurity based on the Food Insecurity Experience Scale (FIES, also maintained by FAO) provides an estimate on how many people do not have access to nutritious and sufficient food due lack of money or other resources. Energy sufficiency can also be assessed at the individual-level using quantitative dietary recall data.

To assess **nutrient adequacy** , quantitative data from dietary recall is considered to be the preferred source. However, given the difficulties in maintaining nationally-representative, up-to-date, quantitative data, metrics currently in widespread use employ recall to measure whether or not individuals consumed food from each of various food groups. One widespread summary population-level dietary indicator is that of **minimum diet diversity (MDD)** ; however, this has been developed only for young children and women. For children 6-23 months of age, MDD is defined as consumption of at least five out of eight food groups including breastmilk in the preceding 24 hours (WHO and UNICEF, 2021). For non-pregnant women of reproductive age (15-49
If this is right, it is important information for policymakers to have to be able to target compensation programs effectively.

We take the focus on occupations in a new direction. Building on earlier work (Artu ̧c, Chaudhuri and McLaren (2010)) (henceforth ACM) in which we estimated the costs to workers of switching industries in a dynamic model in order to measure the welfare effects of trade shocks on workers in different industries, we expand our framework to allow workers to change _both_ their industry _and_ their occupations, estimating the costs of doing so in an integrated dynamic structural econometric model. Our strategy is to specify a rational-expectations model in which industry and occupational switching is a forward-looking investment decision by long-lived workers; estimate the key structural parameters (particularly means and variances of moving costs) on worker data; and then simulate the effects of trade shocks using these estimates to analyze welfare and the time-path of the labor market’s adjustment.

This approach has a number of advantages. First, it allows us to incorporate a real dynamic analysis into the effect on different occupations. Workers can and do change occupation, but it is costly to do so, and the degree of cost will affect the wage effects of a trade shock as well as how those wage changes translate into welfare changes. Importantly, a dynamic analysis allows us to identify the role of option value, which has been shown to have a large effect on the welfare analysis of trade shocks (Artu ̧c, Chaudhuri and McLaren (2010)). If one’s wage in one’s own industry and occupation is reduced by a policy change, but wages in other occupations and industries to which one might consider switching are increased, then the positive option-value effect brought about by the latter may dominate the negative direct effect of the former. One needs a dynamic model with option value built in in order to out what the net is.

Second, we will argue that a full account of occupational choice can have a significant effect on
approximately 12,000-13,000 vatu higher, relative to a baseline of 65,000 vatu, and the effect on log expenditure is equivalent to approximately a 28 percent increase. Panel B of the table also shows positive and significant effects using the duration of time in the RSE as the dependent variable.

The fixed effects results also give large point estimates, although smaller in magnitude than the difference-in-difference estimates, and less significant. Nonetheless, the results on the PS-1 and PS-2 screened samples still show significant increases in both logs and levels of per capita income and consumption when using the duration in the RSE, and significant impacts on percapita income and log per capita consumption using the measure of ever in the RSE. One potential reason for the smaller coefficients with fixed effects is attenuation bias due to measurement error. The Vanuatu data is considerably noisier than the Tongan data. For example, the baseline coefficient of variation of per capita income for the RSE households is 0.90 in Tonga compared to 1.40 in Vanuatu, while the correlation in per capita income from one wave to the next for the non-RSE households varies from 0.43 to 0.77 in Tonga, compared to between 0.19 and 0.27 in Vanuatu. There is thus more signal relative to noise in the Tongan data than in the Vanuatu data.

A final point to note on Tables 2 and 3 is that the estimates are reasonably robust to the choice of sample, with the estimated effects not changing dramatically as one moves from the full sample to the PS-1 and PS-2 screened samples. Given this robustness to different control samples and to controlling for individual fixed effects, and given the large magnitudes of the effects estimated, we believe these estimates of the causal effect of the RSE are convincing and unlikely to be driven by unobserved self-selection.

The median after-tax income earned in New Zealand reported by the seasonal migrants is approximately NZ$12,000.<sup>13</sup> This is several multiples of the mean annual household
relatively worse projects relatively more likely to receive a higher priority. I present empirical evidence for these properties using firm level data from emerging and developed economies. I find that in emerging economies, productive firms are relatively more sensitive to fluctuations in the aggregate supply of funds, and that the opposite is true for relatively less productive firms. I show that this type of mismatch can arise from many common sources of distortions, such as incomplete information, inefficient government intervention, collateral constraints and search frictions.

5
## _Power Limitations_

Our analysis of forest cover loss is unfortunately limited by low statistical power. The minimum detectable effect sizes (shown in the row above the number of observations in **Table 5** ) are very large relative to the variation in the data. For example, the MDE in the Hansen data for the percent deforested (column 1) is 0.304. The mean value of percent forest cover loss for the non-beneficiaries is 0.619, with a standard deviation of 3.38. The MDE of 0.30 suggests that the impact of the program would have to be larger than 49% in order to be detected (0.304/0.619). In order to detect impacts in the 25-point window sample with the 2011-2012 cohort, the impact would have had to have exceeded 67% (0.644/0.956), and the corresponding minimum detectable effectiveness for the 2013-2014 cohort would have had to exceed 50% (0.151/0.319).

Results from previous studies suggest that power limitations, rather than null effects, could explain the lack of statistically significant results. The estimated changes as a percentage of expected loss are similar to those from previous research, which suggest that the 2003-2010 cohorts lowered loss of forest cover by 25-50 percent depending on methodology and unit of analysis (Alix-Garcia et al. 2013, Alix-Garcia et al. 2015, Sims and Alix-Garcia 2017). This previous work, which finds statistically significant effects, used longer time frames and/or measures that included the Normalized Difference Vegetation Index (NDVI), the latter making the environmental analysis more sensitive to degradation. These differences helped to improve power of prior studies.

# **5.2 LAND-COVER MANAGEMENT ACTIVITIES**

In this section, we describe results for the land-cover management impacts, which provide an alternate measure of the environmental impacts of the program.<sup>15</sup> **Table 7** presents results for landcover management activities
9 the inverse distance squared weighting technique.<sup>19</sup> In particular, rainfall was considered excessive in a particular year if it exceeded 300 millimeters, 450 millimeters, or 600 millimeters in any rolling five‐day period throughout that year. By using 5‐day periods as opposed to monthly data, a more refined picture of localized flooding could be constructed.

The annual occurrence of excess rain across the grid during 2001‐2006 is presented in Figure 4. Figure 5 presents the proportion of years the 0.1 degree grid cells experienced at least one episode with more than 300 mm rain in a consecutive five‐day period. The Northcentral and Southcentral Coastal regions appear especially prone to excess rain. Localized flooding occurred also in the Red River Delta Region in 2003. More generally, localized flooding appears especially concentrated along the coast. Excess rainfall is however rather rare in the northern uplands and the south (Southeast and Mekong River Delta regions), suggesting that excess rain is closely related to tropical cyclones, and less to riverine floods. Nonetheless, there are also small inland patches with excess rain, spread across the country.

# _Riverine floods_

Given the historical importance of riverine floods in Vietnam and the limited correlation between the geographical distribution of localized floods and the location of the rivers, the localized flood maps were complemented with riverine (and coastal) flood maps constructed from the Dartmouth Flood Dataset (Dartmouth Flood Observatory, 2008). The latter is an historical worldwide flood database spanning 1985‐2007 and showing locations of floods and their major causes (monsoonal rain, heavy rain, tropical cyclones). The information is derived from a wide variety of sources including news and governmental reports based on locally gauged and remote sensing observations.<sup>20</sup> Unfortunately, the maps are not very disaggregated (ten by ten degrees in latitude and longitude). In principle, the flooding caused by any of the types of rainfall might have been picked up already by the localized flooding dataset
# **Table of Contents for the Appendices**

**Appendix A:  Trade and tariff data**

**Appendix B:  Ownership shares for key services sectors by region**

**Appendix C:  Estimates of the Dixit-Stiglitz elasticities in goods**

**Appendix D:  Estimation of the reduction in trade or border costs**

**Appendix E:  Estimate of the reduction in standards costs**

# **Appendix F:  Estimate of the ad valorem equivalents of barriers in services in Armenia.**

**Appendix F is available as Modebadze and Eroyants (2010) at** https://docs.google.com/leaf?id=0B0V-t-Bs4hXYjA3MTQ5MDctNTAxYy00M2I4LWI3NDAtZGRiNDI3NmYwOGU2&hl=en

# **Appendix G:  Construction of the input-output table for Armenia**

**Appendix H: Technology Diffusion and Trade**

**Appendix I: Comparison of the CASE study of the impact on Armenia of the Deep and Comprehensive Agreement between Armenia and the European Union with the present study**

71
in Dustmann et al. (2005), Basso and Peri (2015) and Edo et al. (2019), a way to minimize the correlation between past immigration and current outcomes is to use a sufficiently long time lag to predict the actual number of immigrants. To mitigate this concern, we derive initial migration shares using data from 2002 – the first year for which data are available in the EU Labour Force Survey, and benchmark time that is nine years before the beginning of our period of analysis. In addition, we control for region fixed effects that take care of unobservable regional characteristics in the IV specification, to ensure that the exclusion restriction is met.

Another possible concern with the shift-share instrument is that using an earlier distribution pattern of immigrants as an instrument can violate the exclusion restriction in the presence of general equilibrium adjustments and strong serial correlation of immigrant flows (Jaeger et al., 2018). If it takes time for markets to adjust to shocks, the error term can also include other influences that reflect ongoing general equilibrium adjustment effects of past migration shocks. This is a greater concern in contexts where the mix and share of immigrants does not vary much over time, like in the US, and where, as a result, the first stage of the IV estimation is stronger due to more stable migration inflows. As noted by Jaeger et al. (2018), the serial correlation in the number of immigrants is lower in Europe compared to the United States as immigration pattern to Europe have changed substantially over time.<sup>17</sup> In particular, the period between our initial measure of immigrant share in 2002 and our period of study for labor market outcomes, which starts in 2011, has been marked by the EU enlargement which triggered important changes in migration flows to the EU (Elsner, 2013).

A final concern is that the total inflows of immigrants in the EU (the shift) could be correlated with local demand-pull factors, given that the inflows include
finn by its corresponding industry median market-to-sales ratio. The imputed value of the firm is obtained by summing the multiples across all of its segments.

The financial and segment data of the Worldscope database allow the computation of the market performance measure. Similar to the ownership data, historical segment data for many of the firms are, however, missing from Worldscope. In such cases, we collected the missing segment data from various issues of the Asian Company Handbook and the Japan Company Handbook. All financial data are converted to US dollars using end-year exchange rates. We exclude a small number of firms from the sample because they do not report segment sales. For the remaining firms with complete segment data, we determine the industrial sector(s) to which they belong and group a firm's reported segments according to the two-digit Standard Industry Classification (SIC) system.<sup>4</sup> Table 3 provides mean and median statistics of EXV for the whole sample and subgroups by types of ownership. By construction the median of EXV is (about) 1. We find that a higher degree of cash-flow rights is associated with somewhat higher mean and median EXV for all type of owners combined (Panel A, column 2). The relationship does appear to taper off, however, as the mean and median EXV for the fourth quartile are only marginally higher than those for the third quartile (1.237 and 1.021 versus 1.231 and 1.012). Splitting the sample by the type of owners, we find that for family ownership the relationship between cash-flow rights and 4 This procedure involves two steps. First, we assign the four-digit SIC codes reported by Worldscope to appropriate segments. In the majority of cases we are able to obtain one-to-one matches between SIC codes and segments. For some companies, the number of reported SIC codes is not the same as the number of reported segments. If a segment can not be associated with a reported SIC code, we determine the segment's
.7%|25.9%|36.4%|23.3%|27.4%|40.6%|35.9%|
|Education|Years of education of<br>HH head|8.5|8.7|8.6|6.8|6.2|8.8|8.9|
||Number of hh members|3.0|3.1|6.0|3.5|4.1|3.6|3.6|
|Demographics|Age household head|48|49|51|50|50|50|53|
||Male household head|52.8%|63.1%|71.3%|62.9%|65.8%|71.3%|68.8%|

Source: authors' calculations based on the SEDLAC database.

For hazard variables ( ), we exploit two types of data, the number of occurrences of natural disasters in the region in the past and the potential damage expected from them. For the first one, we use the number of natural disasters that occurred in 2000-2020 at the state level from HH the EM-DAT of the Université Catholique de Louvain, Belgium. This database offers a 8