rafmacalaba/datause-encoder-data huggingface dataset:
Since, as we explain below, most of our firm-level databases only cover firms with ten or more workers, the model-based counterpart of the average size should is adjusted accordingly. TFP is still defined as in equation 13.

# **5.2 Calibration**

We must choose parameter values for the elasticity of substitution _θ,_ the subjective discount factor of the household _β_ , and the set of parameters governing the process of firm dynamics, entry and exit: entry and fixed operation costs _fe_ and _fc,_ the size of the jump in the binomial process ∆, the exogenous exit rate _δ_ , and the parameters in the innovation cost function, _η_ and _φ_ . For calibration purposes, we consider the economy with no distortions and target moments of the firm size distribution and the life cycle of firms in the US. Table 1 summarizes the parameter values and the matching moments.

Table 1: Parameter Values and Calibration Targets

|Parameter|Value|Target|
|---|---|---|
|_ρ_|3|Hsieh and Klenow (2009), Broda and Weinstein (2006)|
|_β_|1<br>1_._05|Interest rate of 5%|
|_δ_|0.025|Employment-based exit rate of large frms of 2.5%|
|_η_|0.00056|Top 10% employment share = 0.77|
|_φ_|15|Employment age 21–25 relative to age 1|
|∆|0.25|Std dev. of employment growth of large frms|
|_G_(_ω_)|_ω_0 = 0|Size of entrants = 20% of average incumbent|
|_fc_<br>_fe_|0_._1|Exit rate of 5%|
|_fc_|3.165|US average frm size conditional on _L ≥_10 = 118|

The top 10 percent employment share, the average employment ratio between 21–25 and 1-year-old firms, the average employment ratio between entrants and incumbents, and the average firm size conditional on _L ≥_ 10 were computed from the Business Dynamics Statistics database for the year 2007. Numbers are for the manufacturing sector. Standard deviation of employment growth rates for large firms are reported in Atkeson and Burstein [2010].

While the calibration strategy is standard, it is important to highlight the calibration of the fixed cost of production, which is set to target the average firm size in the US manufacturing sector among firms with ten or more workers. As explained in the data section, our cross-country firm-level database restricts us to studying forms above the ten-worker threshold, so we adjust 33
# **Figure 13: Labor force participation (with cell's average at the PUMA level). Males.**

<!-- Start of picture text --> 0<br>-0.01<br>-0.02<br>-0.03<br>-0.04<br>-0.05<br>-0.06<br>-0.07<br>-0.08<br>Whites Blacks in Blacks Asians in Asians Hispanics Hispanics<br>out out out in out<br><!-- End of picture text -->

**Figure 14: Labor force participation (with cell's average at the PUMA level). Females.**

<!-- Start of picture text --> 0.06<br>0.04<br>0.02<br>0<br>-0.02<br>-0.04<br>-0.06<br>-0.08<br>-0.1<br>Whites Blacks in Blacks Asians in Asians Hispanics Hispanics<br>out out out in out<br><!-- End of picture text --> Data from the 2000 Census IPUMS data (5% sample), population aged 25-65. The figures display the marginal coefficients and their 95% confidence intervals on interaction terms for racial groups, foreign born status (in for natives, out for foreign born) with labor force participation as the dependent variable. “White natives” is the omitted dummy. The unit of observation is a cell defined at the level of the PUMA for each combination of age group, racial/immigration group and education group. The regression results are further detailed in table A5.

27
12 New Jersey program designers distinguished the displaced from other<sup>unemployed</sup> workers by applying five "screens during the fourth week of<sup>claiming benefits. Taken together, the</sup> cumulative effect of these screens is to define the displaced as<sup>Ul claimants 25 years of age and older</sup> who had at leas **t** hree years of tenure with their pre-layoff employer<sup>and who could not provide a specific</sup> date at which they expected to be recalled. In the evaluation<sup>carried out by Mathematica Policy</sup> Research, Corson and others (1989) conclude that the five screens<sup>do satisfactorily</sup> Identify claimants who, in the absence of additional employment services, would be likely to experience difficulty in finding new employment. Sizable proportions of the eligible population<sup>were older, previously employed In</sup> manufacturing, and displaced from their jobs by a plant closure or the elimination<sup>of a shift. Moreover,</sup> the eligible population includes a substantial proportion of black and Hispanic workers. Compared to the ineligible population, in addition, the eligible population experienced longer Ul durations and a higher Ul exhaustion rate.

The screening procedure, however, is far from perfect. Claimants determined<sup>to be eligible by</sup> the screens include individuals in the prime of their working lives<sup>and workers from industries, like the</sup> service industry, that were strong and groving in New Jersey. Moreover,<sup>35% of the ineligible population</sup> exhausted Ul benefits (as compared to 45% of eligibles), and 20% of the<sup>eligible population were recalled</sup> to their former employers.

# Basic Education Programs

The discussion in Section B leaves little doubt that the unemployed who bear the highest costs of displacement include those with little formal education and vocational skills. Such<sup>workers are</sup> likely to have a deficiency in basic written communication and mathematics skills severe enough to retard their employment prospects or even their chances for benefitting from job skills training. Thus, a lack of basic skills would seem to be a readily measurable screen to use In identifying displaced workers with substantial need for adjustment assistance services.
and 2015. Only two countries have three waves of the survey (Bulgaria and the Democratic Republic of Congo), approximately half of the countries have two waves (20% of them are African countries), and the rest has only one wave. Although this is not a panel, firms were asked information on sales and employment for the year of the survey and the three previous years, which allowed the construction of annualized growth rates. To assign pollution data to each firm, we use the year of the survey wave and assign the pollution level that corresponds to that same year. For example, the Republic of Yemen has two waves, 2010 and 2013. Then for firms surveyed during the wave 2010, we assign 2010 pollution mean values, and for firms surveyed in 2013, we assign 2013 pollution mean values. In the end, because firms that have GPS location were mostly surveyed after 2010, our sample is reduced to 80 countries with only one wave of the survey (with the exception of the aforementioned example of Yemen that keeps the two waves).

For our estimation, we only use PM 2.5 data. NO2 data are only available until 2011 and georeference information is available starting at 2010, which results in most of the geo-referenced firms with no available NO2 data. In the end, the sample for the second half of our analysis consists of 56,841 firms, distributed in 412 cities, in 79 countries. From these firms, 17.4% are in Africa, 6.2% in East Asia Pacific, 12.3% in Europe and Central Asia, 14.3% in Latin America, 12.6% in the Middle East and North Africa, 21% in South Asia, and 16.2% in high-income countries.

PM 2.5 concentration levels vary widely across our sample (Figure 1). Only 30% of the firms are located in cities where PM 2.5 levels are considered safe by the WHO, i.e., PM 2.5 annual mean below 10 μ . Half of the firms are still exposed to low levels, around 13 μ , and approximately 10% of the firms are in cities with PM 2.5 concentration levels above 50 μ . /mm<sup>3</sup> /mm<sup>3</sup> Moreover, the distribution is not homogenous across regions (Table 3). High-income countries /mm<sup>3</sup> show low levels of PM 2.5, while regions like South Asia or East Asia are among the most polluted ones, with an average around 45 and maximum levels of 111 and 94 respectively.

10
Thailand National Statistical Office. 2009. “Socio-Economic Survey 2008.” Bangkok.

- <mark>Van de Walle, D., and K. Nead. 1995.</mark> _<mark>Public Spending and the Poor: Theory and Evidence.</mark>_ <mark>Baltimore: Johns Hopkins University Press.</mark>

- Van der Gaer, D. 1993. “Equality of Opportunity and Investment in Human Capital.” PhD dissertation, Catholic University of Leuven, Leuven.

- World Bank. 2008. _Côte d’Ivoire Public Expenditure Management and Financial Accountability Review. (PEMFAR)._ Washington, DC.

- ———. 2010. _Liberia  Education Country Status Report: Out of the Ashes—Learning Lessons from the Past to Guide Education Recovery in Liberia_ . Washington, DC.

———. 2012. Tajikistan BOOST data—special tabulations.

Zambia, Ministry of Education. 2009. “Educational Statistical Bulletin 2008.” Lusaka.

- Zambia, Ministry of Finance and National Planning. 2010. _Financial Report for the Year Ended 31st December 2009_ . Lusaka.

Zambia Central Statistical Office (2010) Living Conditions Monitoring Survey (LCMS) 2010, Republic of Zambia: Lusaka

# **Appendix 1. Computing the Human Opportunity Index from Household Survey Data**

According to Molinas et al. (2010); Paes de Barros et al. (2008), Narayan and Hoyos (2012) –from which this section draws--, in order to construct the HOI one needs to estimate the conditional probabilities of access to opportunities for each child based on his or her circumstances. A simple way to do so is to fit a logistic model, linear in the parameters β, where event _I_ corresponds to accessing the opportunity (for example, access to clean water), and _x_ the set of circumstances (for example, gender of the child, education, gender of the head of the household, and the like). One can fit the logistic regression using survey data: where _xk_ denotes the row vector of variables representing the _k_ dimension of circumstances, hence,  _x_  ( _x_ 1 ,..., _xm_ ) and  (1 ,..., _m_ ) , a corresponding column vector of parameters. From the estimation ˆ of this logistic regression, one obtains estimates of the parameters  _k_  to be denoted by  _k_ , _n_  , where _n_ denotes the sample size.

Given the estimated coefficients, one can obtain for each individual in the sample his or her predicted probability of access to the opportunity in consideration: 36
R., R. Drigo, <u>A. Ghilardi & O. Masera, 2015. ‘The carbon footprint of traditional</u> woodfuels’, _Nature Climate Change_ 5266–272.

- Bailis, R, R. Drigo, A. Ghilardi, O. Masera. 2015. “The Carbon Footprint of Traditional Woodfuels,” _Nature Climate Change_ , Vol. 5: 266- 272.

- Bailis, R, V. Berruetam C. Chengappa, K. Dutta, R. Edwards, O. Masera, D. Still, K. Smith. 2007. “Performance Testing for Monitoring Improved Biomass Stove Interventions: Experiences of the Household Energy and Health Project,” _Energy for Sustainable Development_ 11 (2): 57 – 70.

- Barnes, D, K. Openshaw, K. Smith and R. van der Plas. 1993. “The Design and Diffusion of Improved Cooking Stoves.” _World Bank Research Observer_ , Vol. 8: 119-141.

- Bensch, G. and J. Peters. 2013. “Alleviating Deforestation Pressures? Impacts of Improved Stove Dissemination on Charcoal Consumption in Urban Senegal.” _Land Economics_ , Vol. 89: 676-698.

- Bensch, G. and J. Peters. 2012. “A Recipe for Success? Randomized Free Distribution of Improved Cooking Stoves in Senegal.” RHUR Economic Papers #325, RhurUniversität Bochum, Bochum, Germany.

- Bess, M., and J. Kenna. 1994. Cooking efficiency improvement and new fuels marketing project. Project final report, 1994-95 status report and 1995-96 work plan. Ethiopian Energy Studies and Research Centre Report B4-03-140. Wiltshire: Energy Sustainable Development Ltd.

- Beyene, Abebe, Randy Bluffstone, Zenebe Gebreegziabher, Peter Martinsson, Alemu Mekonnen, Ferdinand Vieider, 2015. “The Improved Biomass Stove Saves Wood, But How Often Do People Use It?”  Evidence from a Randomized Treatment Trial in Ethiopia, Portland State University Institute for Economics and the Environment Working Paper available at http://www.pdx.edu/econ/iee .

- Beyene, A, R. Bluffstone and A. Mekonnen.  “Community Forests, Carbon Sequestration and REDD+: Evidence from Ethiopia,” Forthcoming _Environment and Development Economics_

- Bizzarri, M. 2010. _Safe Access to Firewood and Alternative Energy in Ethiopia: An Appraisal Report_ . Prepared for the World Food Program. http://www.genderconsult.org/uploads/publications/doc/SAFE_Ethiopia_Appraisa l_Report_Final_Draft_2.pdf Accessed May 12, 2015.

- Bluffstone, R. 1995. “The Effect of Labor Market Performance on Deforestation in Developing Countries under Open Access: An Example from Rural Nepal, _Journal of Environmental Economics and Management_ . Vol. 29: 42-63.

- Bluffstone, R. 1989. “Improved Cookstove Promotion and the Proper Public Role: A Case Study of Salyan, Nepal.” A Report for the National Cooperative Business Association and US Agency for International Development, Kathmandu, Nepal.

- Bluffstone, R., E. Robinson and P. Guthiga. 2013. “REDD+ and community-controlled forests in low-income countries: any hope for a Linkage, _Ecological Economics_ . Vol. 87: 43-52.

- Borthwick, R. and L. Howard. 1988. _Malla Rani Improved Chulo: Step-by-Step Guide to Constructing Chulos Entirely with Local Materials and All you need to Know to Conduct Trainings_ , self-published booklet.

22 | P a g e
<!-- Start of picture text --> =<br>ts<br>2D> ‘ !<br>Se =. 'i 5 :oei '<br>e® 1 - .<br>Te) "Vy. eae if ° i .<br>t ° + Fe -e | th:<br>oO be : Py<br>aa .en! .a . bd y . : i e 4iz ° 2 |i.<br>= . . : j “sits ‘ii<br>: . j +5 : «ff : . ) = |<br>r=) 2° . i 7 275 ts .<br>=F te“ .: ! aie: ° . ;<br>;<br>° °<br>?<br>2019m1 2020m1 “ateim1 2022m1 2023m1<br>* CH4Anomaly =——— Monthly Mean Anomaly<br><!-- End of picture text -->
# **Figure 9. Trade Openness and Control Variables of Constitutional Changes Vs. Hiring Rates/Size of the Formal Sector A. Trade Openness**

<!-- Start of picture text --> 20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>-5 -5<br>-10 -10<br>-15 -15<br>-20 -20<br>-25 -25<br>0 50 100 150 0 5 10 15<br>Effective Tariffs (Average 1983-1987) Imports Penetration (Average 1983-1987)<br>B. Control Variables of Constitutional Changes<br>Overtime by formal workers Tenure of fired formal workers<br>0 20<br>-2<br>10<br>-4<br>-6 0<br>-8<br>-10<br>-10<br>-20<br>-12<br>-14 -30<br>-16<br>-40<br>20 40 60 80 100 120<br>1 2 3 4 5 6<br>% of overtime formal workers (weighted by average excess<br>hours). Average 1983-1987 Years of Tenure before firing (Average 1983-1987)<br>Union enrollment<br>20<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>-25<br>10 20 30 40 50 60<br>% workers enrolled to an Union (1986)<br>(difference of average before and after 1988) (difference of average before and after 1988)<br>Change in the Hiring Rates of the Formal Sector Change in the Hiring Rates of the Formal Sector<br>Change in the Size of the Formal Sector (difference of average before and after 1988) (difference of average before and after 1988)<br>Change in the Hiring Rates of the Formal Sector<br>(difference of average before and after 1988)<br>Change in the Hiring Rates of the Formal Sector<br><!-- End of picture text --> Note: Bubbles’ sizes reflect the relative size of the industrial sector. The regression lines are obtained from WLS univariate regressions where weights are determined by the size of the industrial sector.

53
driving factor is that in rural areas, many households had reached their limits to growth in agricultural incomes owing to productive land shortages, as 58% of Ugandan farms were less than one acre in 2005/6 (World Bank, 2011b). By 2009/10, about 40 percent of rural households operated a non-farm household enterprise (HE) compared to 24 percent in 1992/93.

**Figure 4: Comparison of rural household livelihood portfolios in Uganda, 1992/93 -** **<u>2005/06, shows major increase in sources of income</u>**

<!-- Start of picture text --> HE & Ag wage Ag wage<br>non ag Non ag Other only2% Ag wage Non ag wage Ag wage only family and<br>HE wage wage 5% & Family 3% 2% farm<br>0% 2%<br>2% farm HE & 13%<br>7%<br>Family non ag Other<br>Farm, HE wage 14%<br>& ag 1%<br>wage HE<br>2% Family 3%<br>Farm &<br>HE<br>18%<br>Family<br>farm<br>Family Family<br>30%<br>Farm farm &<br>53% Family HE<br>Family farm, HE 21%<br>Farm & & ag<br>Family<br>non ag wage farm &<br>5%<br>wage<br>non ag<br>9%<br>wage<br>Rural areas, 1992/93 Rural areas, 2005/06 8%<br><!-- End of picture text -->

**Source** : Authors‘ calculations based on the IHS 1992/93 and UNHS 2005/06

This diversification was an _addition_ to household livelihoods, not a switch, which is why it does not show up in the primary employment data. Figure 4 shows how households combine types of incomes into livelihoods. As a result of the growth in other sources on income, the proportion of households that solely rely only on farm income (both wage and non-wage) declined by a third, from 54 percent in 1992/93 to 36 percent in 2005/06. In rural areas, the proportion of households relying on agriculture income only (usually subsistence) declined by 43 percent during this 18
All five programs studied accept voluntary savings. The volume of BKK's voluntary savings was equal to about 25 percent of the program's asset volume in 1995.8 Voluntary savings increased steadily over the three years since the program began to accept these deposits. The volume of voluntary savings were equal to 15 percent of asset volume for the LKP program in 1995, down from 22 percent in 1993. Voluntary savings are equal to about 3 percent of BKD's assets. Information on the volume of voluntary savings was not available for PHBK and P4K (Table

**Table 10**

# Outstanding **Voluntary** Savings **Volume** as a **Percent of Adjusted** Assetsa

|||**1992**|**1993**|**1994**|**1995**|
|---|---|---|---|---|
|**BKK**(for units that|0|12|21|25|
|accept deposits)||||-|
|<br>**LKP (Dompu**District)|14|<br>22|16|<br>15|
|<br>**PHBK**|<br>NA|<br> <br>NA|<br>NA|<br><br>NA|
|<br>**P4K**|<br>NAN<br>-<br>2|<br>A|<br>NA<br>-|<br> NA|

a! Programs' outstanding assets are corrected to simulate their writing off bad debt. Required savings are also subtracted from assets because they function as a reduction in loan size.

10).

_Outreach Depth._ Of the five programs reviewed, only P4K explicitly targets low-income households. The program claims that all beneficiary households have incomes below the poverty line. Limited data from PHBK indicate that about 20 percent of its client households have incomes below the poverty line. Income data on the other programs' clients were not available. One proxy for income commonly used to assess depth of microfinance outreach is

**Table 11** Depth of **Program Outreach**

||Average<br>Loan Size<br>(US$)|**Loan Size**<br>Percent of**GDP**<br>Per Caita|**Women**<br>**Percent of**<br>Borrowersa|
|---|---|---|---|
|**BKK**|<br>99|p<br>10|40|
|<br>**LKP (Dompu**<br>District)<br>|<br>75|<br>7|62<br>|
|**PLIBK**|71-130|7<sup>-</sup>1 3<sup>b</sup>|50|
|**P4K**|67|7|50|
|**BKD**|71|7|50|
|Range for 9<br>programs<br>worldwide|NA|16-136|20 - 90|

a] Women's share of loans was estimated by program managers or was based on a sample of program loans.

b/ Program managers' estimate of the average loan size per group membe **r** esults in loans per person equal to 13 percent of GDP per capita. Estimates of the number of members in each borrower group results in loans per person equal to 7 percent of per capital GDP.

loan-size- to-GDP-per-capita. For the five programs, average loan-size-to-GDP-per-capita in 1995 ranged from up to 13 percent for the PHBK program to 7 percent for LKP, P4K, and BKD. These figures are lower than those of nine of the most respected microfinance programs

> **8** The BKK program did not report voluntary savings separately from required savings. The value of required savings is estimated by multiplying the average required savings rate by the outstanding balance of BKK loans. The program's estimated voluntary savings is total savings minus this estimated required savings.

19
This paper investigates and compares the evidence of the drivers of job loss from two countries. It provides an evidence base for quantifying further risk of job loss and econometric results to inform the construction of a risk index. Our methodology holds the promise to be applied in a broader set of countries as well. Our comparative findings suggest some degree of generalizability of risk factors – and in particular the importance of closures, customer infection risk (especially when cases and transmission are high), supply chain disruptions, and other sector-specific shocks to consumer demand. Despite Jordan and Georgia’s having different macroeconomic contexts and labor market structures, we find that they shared similar main drivers of job loss at the firm level, and the magnitudes of impact of those drivers was very similar for each.

Feasible approaches for broadening the application of our methodology and findings are likely to vary according to countries’ data availability. Where sufficient firm-level and labor force data exist, analysts can follow the same steps to derive and utilize country-specific coefficients, considering all possible risk factors observed at the firm or occupation level. Alternatively, analysts can assign a job loss risk value based on a rescaled index using our coefficient estimates. Although it is never perfectly safe to assume external validity, in the absence of other country-specific evidence or data, where our coefficient estimates are similar for the two countries, it may be justified to use them for other middle-income countries. Where firm or other data on initial shocks to sales (or its drivers, such as closures) exist, but data on other variables do not, the estimated effect of sales shocks can be linked to jobs in the latest labor force survey.

We propose improvements to future firm surveys that would facilitate the ex-ante assessment and empirical understanding of jobs impacts, not only of future crises, but also of key labor market dynamics in non-crisis times. The rapid evolution of COVID-19 has highlighted the need for more detailed firm-level data (from formal and informal firms, ideally, with more than one employee) on their occupational structures and the contractual status of their employees. More granular data on the demographic composition of firms’ workforces would complement labor force surveys and permit a more comprehensive understanding of evolving labor market conditions and occupational structures, as well as the identification of the most vulnerable categories of jobs or workers from future crises and shifting demands for labor and skill.

44
|**Table 7: Estimates of pr**|**ogram effects on**<br>**Ou**|**agricultural pr**<br>**tput**|**oductivity and in**<br>**Value of fer**|**put use**<br>**tilizer used**|**Used te**|**chnology**|
|---|---|---|---|---|---|---|
||**Total **|**Per acre**|**Organic**|**Chemical **|**Machinery**|**Irrigation **|
||||**Panel A: Ov**<br>|**erall impact**<br>|||
|MNREGS|-0.044|-0.044|0.117***|-0.001|0.055**|0.079**|
|* post|(0.054)|(0.054)|(0.031)|(0.017)|(0.027)|(0.037)|
|Observations|6,468|6,468|6,468|6,468|6,468|6,468|
|R-squared|0.892|0.787|0.640|0.652|0.641|0.655|
|||**Pan**|**el B: Heterogene**|**ity by farm size**|**class**||
|MNREGS *|0.124|0.124|0.097*|0.057**|0.085**|0.102|
|post * small|(0.091)|(0.091)|(0.055)|(0.028)|(0.042)|(0.064)|
|MNREGS *|-0.078|-0.078|0.052|-0.023|0.093**|0.063|
|post* medium|(0.088)|(0.088)|(0.050)|(0.029)|(0.047)|(0.060)|
|MNREGS *|-0.100|-0.100|0.165***|-0.045|0.007|0.019|
|post * large|(0.102)|(0.102)|(0.050)|(0.031)|(0.052)|(0.064)|
|Observations|6,468|6,468|6,468|6,468|6,468|6,468|
|R-squared|0.894|0.792|0.645|0.654|0.644|0.658|

_Notes:_ Household and year fixed effects as well as controls included but not reported. Standard errors, clustered at household level, in parentheses; *** p<0.01, ** p<0.05, * p<0.1 21