rafmacalaba/data-use-mentions-extended huggingface dataset:
and living with their spouse. We focus on respondents with at least junior secondary school education as research has shown that women with this level of education have the most discretion over whether they work or not and so are more likely to be able to have their behavior influenced.<sup>8</sup> The sample was constructed such that 75% of both male and female respondents were high school educated and 25% tertiary educated (to roughly reflect the coverage of these groups in the Indonesian population). The survey collected demographic information (including age, gender, number of children, own and spousal work status) and information on respondents’ attitudes and perceptions of social norms.

Specifically, we collected information on personal attitudes in relation to the extent of support for a) female labor force participation: “Are you supportive of married women with children under 12 working for pay outside the home?”; and b) sharing of childcare between husband and wife: “How supportive are you of husbands sharing day-to-day childcare duties with their wives?”.

We also elicited injunctive norms on what proportion of married men and married women are supportive of the above behaviors. These estimates were incentivized by paying approximately USD17 to the respondents who made the most accurate estimate. The difference between the mean of the sample’s reported level of support and the individually reported perception of social norms in both domains generates a measure of the extent of misperceptions about the social norm.

We also collected information on which people’s opinions are important to respondents when making decisions about female household members working and sharing childcare. Respondents were asked to indicate the extent to which they were concerned about the opinions of husbands, parents, parents-in-law, extended family members, people in their social networks and people in their religious community. Finally, we enquired about the potential sanctions individuals would face if they decided to deviate from the social norm.

Our second online survey collected similar information from 4,478 similarly selected respondents. The information intervention was embedded
In this paper, we investigate the impact of job displacement on labor market outcomes of Mexican workers, with special attention to differential effects on gender and educational groups. We use retrospective information on labor history of a cross-section of workers collected from the extended questionnaires of the Mexican Labor Force Survey ( _Encuesta Nacional de Ocupación y Empleo, ENOE_ ). The dataset permits to identify six mutually exclusive categories of job displacement: 1) Plant closing; 2) Fired; 3) Quitting; 4) Closed own business; 5) Retired; and 6) Other reasons. We study wages, hours of work, formality status, and duration of unemployment that follow job displacement. After estimating the average impact of job displacement on these labor outcomes, we ask whether the impacts are transitory or permanent, and present results for educational groups and gender. We estimate a hazard model to look at the duration of unemployment spells of displaced workers.

The analyses in this paper focus on workers displaced due to plant closings. We should be cautious, as the relevant coefficients might be subject to endogeneity or selection bias to the extent that displaced workers are more likely to have unobservable characteristics that make them less skillful than the benchmark category of non-displaced workers. Unlike Kaplan et al. (2005), we include informal workers in our analysis. The inclusion of informal workers is crucial, as they account for more than half of Mexico’s total employment. To test the robustness of the cross-section estimates, we estimate a difference-in-difference (DID) specification using the _ENOE_ ’s longitudinal structure.

We find that displaced workers due to plant closings experience sizeable and long-lasting wage reductions, which average a reduction of -7.5 percent over a nine-year period. After an initial drop of 11 percent, the wage gap with respect to non-displaced workers narrows to 5 percent in the fourth year and equals 2 percent in the ninth year. We find no evidence of gender-differentiated 4
variation in the income and sales tax system in Costa Rica, and a ten-year panel of administrative tax records. We construct the Costa Rican tax register from the universe of registration and deregistration records since 2006. We match the register with income and sales tax records from the universe of rms, including both self-employed and corporations. We further match these data with over 20 million third-party and withholding records from rms' transaction partners, nancial institutions and state institutions.

Our analysis is divided into three parts. In the rst part, we leverage the data to conduct a detailled anatomy of compliance.<sup>5</sup> To our knowledge, this is the rst study to use populationwide third-party and withholding data from a developing country, and to analyze all compliance margins, including the extensive, intensive and payment margin. On the extensive margin, we nd that about 50% of tax-liable rms fail to le their income tax declaration. The vast majority of these non-lers are identied through third-party reports, and thus denitely economically active. However, non-lers are disproportionately small. We estimate that total income tax revenue could be increased by at most 10% if all non-lers were made to le. However, a companion paper by Brockmeyer et al. (2015) shows that the gains from a communication intervention to non-lers are actually orders of magnitude smaller. On the intensive margin, we nd that 14-17% of rms underreport their sales, while 30-50% of them under-utilize their costs compared to third-party reports. The potential revenue gains from perfectly enforcing tax liabilities on third-party reported sales are again approximately 10% of income tax revenue. However, evidence from the impact of desk audits shows that realized revenue gains are orders of magnitude smaller. On the payment margin, we nd that 5-25% of small rms pay their taxes with signicant delay. After accounting for theses delays, however, payment is above 95% of liabilities for both the income tax and the sales tax.
3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>.3<br>1<br>.8 .2<br>.6<br>.1<br>CCDF<br>.4 Difference<br>.2 0<br>0 -.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _B1. Change in the weights assigned to each dimension. Baseline scenario: Evenly distributed weights across all indicators (25% each). Alternative scenario: Skewed weighting scheme in favor of the Social Inclusion dimension (40% for Social Inclusion, and 20% for the rest of dimensions)._ South Africa B1.1. FOSD: Counting Vector B1.2. Dominance Analysis 41
elasticities, that determine the degree of mobility of primary factors across sectors; (iii) the flexibilities of regional investment allocation; (iv) consumer demand elasticities.

The GTAP data base, version 6, includes 87 regions and 57 commodities. For our analysis, the regions are aggregated from 87 regions to 16 regions. The regional aggregation has been selected primarily based on importance in the world production, consumption, international trade, economic development and geographic location. Thus, the regional aggregation includes five donor countries and eleven recipient countries (Table 1).

# **Table 1. Regional aggregation**

|**Acronym **|**Region **|**Type**|
|---|---|---|
|**USA**|**United States**|_Donor_|
|**CAN**|**Canada**|_Donor_|
|**WEU**|**Western Europe**(Austria, Belgium, Denmark, Finland,<br>France,<br>Germany,<br>Greece,<br>Iceland,<br>Ireland,<br>Italy,|_Donor_|
||Liechtenstein,<br>Luxembourg,<br>the<br>Netherlands,<br>Norway,<br>Portugal, Spain, Sweden, Switzerland, the United Kingdom)||
|**JPK**|**Japan and South Korea**|_Donor_|
|**ANZ**|**Australia, New Zealand and Oceania**|_Donor_|
|**EEU**|**Eastern Europe**(Bulgaria, Cyprus, Czech Republic, Estonia,<br>Hungary, Latvia, Lithunia, Malta, Poland, Romania, Slovakia,<br>Slovenia)|_Recipient_|
|**FSU**|**Former Soviet Union**|_Recipient_|
|**MDE**|*
=28.

For comparison purposes, I used the actual transfer figures in 1994 to run the same regression. The resulting a, is statistically insignificant and the R-square is only 0.0002, showing not even a slight correlation between per capita transfers and per capita GDP levels. This suggests that the current transfer system has not effectively achieved any redistributive goal. The regression results are as follows: Regression V: PCTi = 206.46 - 0.97 PCGDPi (1.10) (-0.075) R-square = 0.0002, No. of observations = 30, Degrees of freedom =28.

The same regression is also conducted using the per capita transfer figures generated by the "partial equalization" transfer schemes proposed in the previous section. Table 2 compares the regression results of four systems: actual transfers in 1994, 20% equalization, 50% equalization, and full equalization. The results show that the full equalization and _50%_ equalization schemes have statistically significant equalization effect (the slope coefficients are significantly negative). The 20% equalization schemes do 57
The share of retained earnings is assumed to fall, and dividend payouts to rise to reflect the diminishing role of state enterprises in the economy. The dividend rate, i.e. the share not used for retained earnings, was 38.9% in 2010 and we project it to rise to 58% by 2020. It should be pointed out that national savings and investment in the Chinese data includes capital such as roads and other public infrastructure, items that are excluded from the “gross fixed private investment” item in most other countries National Accounts.

In the labor supply expression eqn. (A47) we have the product of the working-age population, annual average hours and quality. In Cao and Ho (2014) we discussed various population growth scenarios including the different two-child policies. Projections by age groups are taken from projections made by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat<sup>23</sup> and in the central population scenario we adjust them to incorporate a looser child policy. The results are plotted in Figure A5.

The composition of the work force changes over time with a bigger portion of educated workers, bigger or smaller portion of more experienced workers, and an older average age. This quality of labor _L_ input index,<sup>_q_</sup> _t_<sup>, in estimated in Cao et al. (2009) to have grown at 0.9% per year for the period 1983-2000.</sup> Given the expectation of continued higher educational attainment in the future we assume that China's aggregate labor quality continue to rise, but at a diminishing rate. By 2040 the quality index is assumed to grow at only 0.2% per year. For comparison, the U.S. labor quality growth peaked at 0.5% during the 1960s, and fell to 0.3% per year during 1995-2000 (Jorgenson, Ho and Stiroh, 2005, Table 6.5)
apply the LTGM to evaluate the impact of school closures on future income levels _for each country in our sample_ due to lower human capital. The future income losses generated by the LTGM are then used to calculate the _FPY_ . We summarize the methodology below.

Cross-country data from UNESCO records the length and intensity of school closures in 2020-2021.<sup>14</sup> We then convert the school closure durations into Learning-Adjusted Years of Schooling (LAYS) lost - a measure that takes into account the national average quality of schooling (Kraay 2018 and Filmer et al. 2020). Next, we calculate the impact of school closures on the average human capital of the workforce from 2020 to 2100 in the LTGM-HC by tracking the human capital of successive population cohorts.<sup>15</sup> We then estimate future GDP per capita growth under the pre-pandemic baseline trend and the scenario with school closures from the pandemic. As a neoclassical growth model, GDP in the LTGM is calculated 12 To compare with Jedwab et al (2023), our estimates of future GDP losses (results below) would be equivalent to a one-off loss of 28, 45, 54 and 46 percent of the 2020 GDP in high, upper-middle, lower-middle, and low-income countries, respectively. The main reason for the different pattern is that Jedwab et al (2023) include losses in the return to experience, which is more important for high-income countries.

13 For more details about the LTGM, see Loayza and Pennings (2022) or visit https://www.worldbank.org/LTGM. 14 The UNESCO data records for each day of 2020 and 2021 whether schools are fully closed, partially closed, or open.

15 For example, consider a one-year loss of formal schooling for 15–19-year-olds in 2020-2021. Adjusting for the (median) quality of education, the cohort losses are 2/3 of one LAYS, leading to an 8% fall in the cohort’s future productivity – based on international empirical evidence
|0.0|0.2|0.3|
||1994|25.2|67.2|--|27.7|0.0|0.4|0.4|
|Uruguay|1979-81|22.7|14.6|52.2|--|0.4|0.6|0.7|
||1986-8|8<br>36.0|33.1|22.2||0.2|1.7|0.8|
||1994|44.7|43.7|36.8||0.2|2.1|0.4|

Source: Trade intensity indices computed from UN COMTRADE statistics.
For all demand-driven activities (local consumption goods, hired agricultural labor, tractor services), market equilibrium is established by allocating demand to suppliers based on the initial market shares recorded in the SAM.

Finally, all surveyed households are net sellers of agricultural production, _i.e._ they begin in regime 3 of Figure 1. The simulations may result in a regime change for households, possibly turning some household groups into net buyers. Lacking observations from the survey we use an estimate of transaction costs for rice from Park et al. (2002) to set the width of the price-band at 25 percent of the selling price. Thus households will become net buyers if their shadow price rises 25 percent above the initial selling price.

Summarizing, the village equilibrium model resembles commonly used macro level general equilibrium models in the way in which consumption and production are modeled. A major difference with macro and existing village level models is household-specific production, which is affected by household consumption decisions through endogenous household shadow prices. Lack of data resulted in most village markets to be modeled as fixed price, demand-driven equilibria. The only exception is the village market for animal traction, which is balanced through an endogenous village price. Household production and consumption decisions are calibrated on the household survey data, resulting in householdspecific demand and supply functions.

# **3. Full Liberalization Impacts**

There are two major pathways through which trade liberalization affects households: changes in prices of consumed goods, agricultural inputs and outputs and changes in off-farm employment and wages. In this section we first analyze each of these two pathways separately before looking at the combined impact of the full liberalization scenario.

18
Data available in 2000. Regarding the situation in 2000, we estimate the total stock of adult immigrants in the OECD area at 58.496 million. The only country for which data on age, country of birth and educational attainment are missing is Mexico (0.493 million of immigrants). Data on immigrants’ age and educational attainment are not available for Japan, Iceland, Korea and Greece (2.030 million immigrants). However, the combination of European Council data and the European Labor Force Survey provides a good approximation of the Greek immigration structure (0.116 million adult immigrants). In the other 25 countries (55.974 million adult immigrants), Census and register data are (or will be) available. Adding Censuses and the Greek survey, we capture 56.090 million immigrants, i.e. 95.9% of the total. Finally, since about 1.8 million of those immigrants did not report their country of origin, we have accurate information on educational attainment and origin for 54.246 immigrant, i.e. 92.7% of the OECD adult immigration stock (to be compared with 44.1% in the US Census).

In the 26 countries (25 Census/Registers and Greece) where data on educational attainment will be available, we are usually able to work on the population aged 25 and over. However, this age limit is slightly di¤erent in some countries (18 in Switzerland, 15 in New Zealand, 13 in Poland). This should not distort our measure of the skilled immigration stock signi...cantly. Contrary to common belief, data on country of birth are available in a large majority of countries, even in European countries. The concept of citizenship must only be used for Germany, Italy, as well as for Greece (European Council data are based on the concept of citizenship), Japan and Korea.

Data available in 1990. The methodology is similar for 1990. To
, China’s TANSAT and the Japan Space Exploration Agency’s GOSAT and GOSAT-2. Detailed technical assessments of measures from these platforms have verified that they provide useful and comprehensive information for global carbon emissions analysis (Sha et al. 2021; Weir et al. 2021; Nassar et al. 2021; Pan et al. 2021; Wu et al. 2020; Hakkarainen et al. 2019; Labzovskii et al. 2019).

This paper focuses on satellite measurement to support implementation of the Global Methane Pledge. The first requirement is an easily-updated template for tracking atmospheric CH4 (methane) concentrations at local and regional scales. Using observations from the Sentinel-5P (S5P) platform of the European Space Agency (ESA), we develop the template from data filtering techniques pioneered by Hakkarainen et al. (2019) and applied to satellite-based CO2 measures by Dasgupta, Lall and Wheeler (2022a,b).

The second requirement is support for prioritizing actions to reduce methane emissions from energy, agricultural and waste subsectors whose spatial distributions are highly non-uniform across and within countries. At present, priority-setting is guided by spatially-formatted “bottom-up” estimates from national or global emissions inventories such as the Emissions Database for Global Atmospheric Research (EDGAR). These inventories combine sectoral activity

> 1 https://www.globalmethanepledge.org/#about

1
# **Annex B: Methodology**

## **Labor income regression**

For the analysis of correlates with individual labor income, a Heckman’s selection model is estimated. The baseline OLS model is set as follows: ln( , where is the log monthly wage or labor income of individual yyii) = αα+ ββXXii + εεii _i_ , is a vector of covariates including various demographic characteristics (for example, age, age squared, gender, education ii ii attainment), characteristics of jobs (for example, economic sectors, public/private, and yy XX formal/informal), and geographic characteristics of an enumeration area (for example, distance to the CBD). is an iid error term. In order to take account of the selection of individuals reporting labor income, Heckman’s selection model is estimated by adding a first-stage selection model ii based on individual’s demographic characteristics. The results are presented in Table A.1. εε We also employed a regression analysis to identify factors that determine unemployment and informality of jobs. The outcome variable is a binary variable coded 1 if unemployed or working in the informal sector. An OLS (linear probability) model was used. The same set of demographic characteristics and geographic characteristics, as described above, was adopted for these regressions as well, although we also introduced interaction terms between education levels and sex to explore if there are any differential effects of education on employment conditions by sex. The results are presented in Table A.2.

## **Job accessibility index**

The index of job accessibility that we used in this report measures the share of job opportunities that can be reached within a certain travel time (Nakamura and Avner 2018). This index can be formalized as follows: where _i_ indexes each location (or origin), _Oj_ represents the number of job opportunities in location _j_ , tij is the travel time between location _i_ and location _j_ , and is a time threshold. 1[.
> rate.  Let _Wi_ /<sup>_W_</sup> _i EGS_ be the relative wage in state _I_ , where _Wi_ is the wage rate in the (non-PW) casual labor market and _Wi EGS_ is the MGNREGS wage rate. The correlation coefficient between ln( _Wi_ /<sup>_W_</sup> _i EGS_ ) and un-met demand ( _EDi_ ) is –0.558 which is significant at the 1% level.<sup>24</sup>

However, there are two reasons to question whether this really reflects greater tightening of the casual labor market in states where there is less un-met demand for work on the scheme. First, the implied relative wage rate at zero rationing is too high to be believed. We postulate that the relative wage depends on the excess demand as follows:<sup>25</sup> The expected value of the log relative wage rate when all demand for work on MGNEGS has been satisfied is then given by  . Using the data in Table 1 and 5, the estimated value of  is 0.398 (st. error=0.105) and the estimate of  is –1.777 (st. error=0.519; R<sup>2</sup> =0.311.) This implies that, when all demand for work is satisfied, the market wage rate would be 50% higher than the MGNREGS wage rate.<sup>26</sup> Yet the work is very similar, and there is no obvious reason why such a differential would exist in equilibrium.

Second, the excess demand variable could well be picking up some other factor correlated with it. Following our discussion in section 2, poverty is a plausible candidate. Adding the poverty rate to equation (5), we find that the effect of excess demand becomes insignificant (prob.=0.19) while the poverty rate is significant (a coefficient of 0.828, with st
business model on which global manufacturing is based. Due to limited country coverage of value-added trade data we are however unable to use such data in our analysis.

3 See for example Österholm et al. (2007) and Utlaut et al. (2010) for studies of spillover from AEs to Latin America and to Emerging Asia respectively. See also Erten (2012) for a study of spillover emanating from the Eurozone to emerging markets.

(continued)
and

> 44 Of course as capital‘s share decreases, labor‘s share must increase, but reconciliation of data with the household data, also resulted in an increase in the relative share of skilled labor to unskilled labor. This is because, lacking more specific data at the household level, we defined all labor income as skilled labor income if the worker had any post-secondary education. Russia has a highly educated labor force, so this reconciliation resulted in an increase in the share of labor income going to skilled labor.

68