rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 9 Examples of Countries With Inferior fit in the Distribution Tails**

<!-- Start of picture text --> A) Argentina 2019 B) Panama 2019<br>4 12<br>2 10<br>8<br>0<br>-2 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 6<br>4<br>-4<br>2<br>-6<br>0<br>-8 -2 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91<br>-10 -4<br>-12 -6<br>Lower bound Upper bound Upper bound Lower bound<br>Actual growth Old Model Actual growth Old Model<br>New - Rescaling New - No Rescaling New - Rescaling New - No Rescaling<br><!-- End of picture text --> Source: Own estimations based on SEDLAC.

_Scale and flexibility factors_ Table 3 also shows cases where the percentage of percentiles that fall inside the confidence interval is very small or even zero. The GICs for labor and total income are presented for Chile 2017 and Uruguay 2019 in Figure 10. In the case of Chile 2017, the GICs show a scale factor indicating that the estimated income growth falls short when compared to the actual growth at each distribution percentile. For Uruguay 2019, the GICs show that there are some parts of the income distribution where more flexibility is necessary, and they do not necessarily correspond to the tails of the distribution (percentiles 13-20 and 71-88). In both cases, the GICs for only labor income indicate that the _new model without rescaling_ estimates an income growth rate closer to the actual growth than the other models. This way, results suggest that the differences in levels are mainly due to the non-labor income, where more flexibility might contribute to better estimating the changes in the total income distribution. Some refinements of this methodology could help improve the estimation of non-labor income and the overall estimate, like modeling public transfers or applying different assumptions on remittances, capital, and pensions. In an exercise like this paper, Cojocaru and Olivieri (2014) find that accounting for social protection benefits in a micro-simulation model applied to Serbia 2009 reduced the bias in poverty estimates statistically indistinguishable from the actual headcount. An applied example of how refinements in non-labor income modeling, more specifically remittances, affect the estimation results is presented in **Box 1** for El Salvador.

25
## **3.3 Exit Rates**

Although firms can adjust their material costs and their payroll, some firms remains unprofitable following the lockdown, or more unprofitable than prior to the crisis. This applies to firms with high fixed costs and those receiving a large revenue shock, especially if their baseline profitability is also low. The profit loss would trigger an increase in firm exit rates. Exits are primarily thought of as firms closing their operations and filing for bankruptcy but could also refer to firms’ leaving the formal sector and thus disappearing from the corporate tax data.

We estimate the potential increase in exit rates by exploiting the panel structure of our data. We calculate the average exit rate among loss-making and profit-making firms prior to 2020, as well as the overall average (benchmark) exit rate, for each of the three impact sectors. At baseline, profit-making firms display a 1015% chance of exiting in a given year and loss-making firms a 20-30% exit rate. These patterns are stable across countries, as shown in Figure 2b, and across sectors and over time. We then apply the exit rates for loss-making and profit-making firms respectively, after shifting the profitability distribution for 2020 through the revenue shock and the material and labor cost adjustments, and hence obtain the overall share of firms exiting. Figure 2c shows that exit rates increase by 112% (147%) on average compared to the baseline for the three-month (five-month) lockdown. The increase in exit rates varies across countries, as the share of profitable firms also varies substantially and because the increases are calculated over a small (albeit stable) baseline exit rate. The increase in exit rates varies from 47% to 283% for the three-month

# **4 Implications for Government Policy and Public Finances**

We now study the implications of our simulations for government policy and public finances. First, we estimate to what extent wage subsidies—a widely discussed policy tool, common in many European countries (Giupponi and Landais, 2020)—can mitigate the loss in formal employment generated by the lockdown. Second, we estimate the overall budgetary impact of the lockdown, which reduces the tax revenue collected from the corporate sector and simultaneously increases private sector financing needs.

## **4.1 Can Wage Subsidies Mitigate Payroll Losses?**

To minimize the loss of employment, some governments offer wage subsidies to firms during economic downturns (also called “short-work” schemes in Western Europe). We use our simulations to examine how much of the economy’s formal aggregate payroll can be protected with wage subsidies and how this varies across countries.

Figure 3a shows the predicted loss in the aggregate annual payroll as a function of the generosity of the wage subsidy, on average across countries. The simulated subsidy ranges from 0% (no subsidy) to 11
**TABLE 8: Impacts on Child Assessments - 36-Month Follow-Up**

|||**Pea**|**bod**|**Depend**|**ent variable**|**: Assessme**<br>**Kau**|**nt Score**<br>**fman**|||
|---|---|---|---|---|---|---|---|---|---|
|||<br>**Pic**<br>**Voca**<br>**T**|**y**<br>**ture**<br>**bulary**<br>**est**|**Le**<br>**Sust**<br>**Atte**|**iter**<br>**ained**<br>**ntion**|<br>**Asses**<br>**Batt**<br>**Chil**|<br>**sment**<br>**ery for**<br>**dren**|**Early**<br>**M**<br>**Asse**|**Grade**<br>**ath**<br>**ssment**|
|**Variables**||**(1)**|**(2)**|**(1)**|**(2)**|**(5)**|**(6)**|**(7)**|**(8)**|
|T2 i tii||0.077|0.057|-0.036|-0.038|0.015|0.034|-0.101|-0.083|
|(caregver ranng)||(0.095)|(0.092)|(0.079)|(0.076)|(0.085)|(0.085)|(0.075)|(0.065)|
|T3 T2 + rir innti||0.161|0.146|0.044|0.023|0.051|0.046|-0.028|-0.031|
|( caegve ceves)||(0.109)|(0.104)|(0.075)|(0.068)|(0.085)|(0.086)|(0.075)|(0.066)|
|T4 (T2 + arentin trainin)||0.113|0.108|-0.005|-0.020|0.046|0.081|-0.040|-0.021|
|pg g||(0.103)|(0.099)|(0.092)|(0.084)|(0.091)|(0.089)|(0.074)|(0.065)|
|Lagged Dependent Variable (Baseline)|||0.203***<br>(0.023)||0.363***<br>(0.032)|||||
|Malawi Developmental Assessment Tool: Fine|||||||0.373***||0.384***|
|Motor/Perception Skills(Baseline)|||||||(0.038)||(0.038)|
||T2=T3|0.377|0.333|0.276|0.383|0.648|0.887|0.337|0.443|
|F-test for Equality of Parameters - p-value|T2=T4|0.686|0.556|0.692|0.815|0.690|0.553|0.405|0.366|
||T3=T4|0.635|0.697|0.521|0.536|0.956|0.653|0.868|0.883|
|District-bin Fixed Effects?||Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Baseline Control Variable and Age Dummies?||No|Yes|No|Yes|No|Yes|No|Yes|
|Number of observations||2,009|2,009|2,029|2,029|2,027|2,027|2,027|2,027|

Notes: .01 - ***; .05 - **; .1 - *; OLS regressions at child level using baseline and 36-month data on scores. Standard errors in parentheses are clustered at the CBCC level. We use all children for whom the relevant variables are available. We weight observations using sampling weights. For each round, scores are stardardized using the (weighted) mean and standard deviation for the control group in that round. For Kaufman Assessment Battery for Children and Early Grade Math Assessment, total scores were obtained by constructing a weighted average of the subscale scores of each assessment (for Kaufman Assessment Battery for Children: Hand Movements, Triangles, and Number Recall / for Early Grade Math Assessment: Number Recognition, Quantity Discrimination, and Addition), where the weights were obtained using Inverse Covariance Weighting, as in Casey et al. (2012) and described in detail in the Appendix.

51
is about 25% of biomass at the start of the fishing season (Kroetz et al., 2016).

During fishing seasons in which adult biomass exceeds 4 million tons at the start of the fishing season, harvest of cohort _c_ is Harvest of cohort _c_ on day _t_ + 1 depends on three terms, which may vary by scenario _S_ and whether individuals in cohort _c_ are juveniles ( _lc,t_ +1 _<_ 12) or adults ( _lc,t_ +1 _≥_ 12). The first term is total biomass at the start of the season divided by 4 times 91 (25% of initial biomass divided by the number of days of the fishing season). The second term is the fraction of harvest by weight that juveniles or adults comprise. Between 2017 and 2019, juveniles represent 8.78% of tons caught. Therefore, in the status quo scenario juveniles comprise 8.78% of tons caught and adults comprise 91.22% of tons caught. I estimate that the closures policy increases tons of juveniles caught by 41% (Figure A6). In the counterfactual scenario without the closures policy, juveniles therefore comprise 6.23% of tons caught and adults comprise 93.77% of tons caught (6.23% = 8.78% / 1.41).

I obtain initial values for the population at each length interval by digitizing IMARPE’s estimate of the population size distribution in March 2017, one month before the beginning of my study period (IMARPE, 2017). I divide the population into cohorts of .01 cm length intervals. I then simulate the population forward 20 years under the two scenarios. A new cohort of 7 cm-long individuals enters the fishery each day according to Equation 8. I record harvest each season and biomass at the start and end of each season.

Figure E1 displays the simulation results. Though juveniles comprise a greater fraction of harvest in the status quo scenario, total harvest and biomass are equivalent through the 47
17 to affect household welfare in Ruvuma, even after controlling for households’ use of coping strategies.

Welfare loss from illness or death shocks comes about through 1) increased (medical and funeral) expenditures and 2) foregone opportunities through a loss in labor supply (and thus earnings) and/or a decrease in the return to labor (Gertler and Gruber, 2002). While there is no information on the (opportunity) cost related to changes in labor supply and returns to labor, expenditures related to illness and death shocks are substantially larger in Kilimanjaro than in Ruvuma, suggesting a larger shift of resources from other household consumption to medical and/or funeral expenditures in Kilimanjaro than in Ruvuma (Table 4, panel 1). These results are consistent with the much lower reported use of health services in case of illness/injury in Ruvuma.<sup>14</sup> The larger average distance to a dispensary or health center in rural Ruvuma (4.5 km) compared with rural Kilimanjaro (2 km) suggests that lower accessibility of health care underpins this difference in health spending.<sup>15</sup> Moreover, the directly _reported_ health expenditures in case of an illness or death<sup>16</sup> in Kilimanjaro appear at least as large as the _estimated_ average welfare loss<sup>17</sup> suggesting that the welfare loss is largely due to medical and other related expenses and less due to

> 14 While about the same proportion of households reported an illness/injury over the past 4 weeks in Kilimanjaro and Ruvuma (23 and 24 percent respectively) during the 2000/01 Household Budget Survey, 74 percent of all households (rural and urban) in Kilimanjaro consulted a health provider, compared with 47 percent of all households in Ruvuma (National Bureau of Statistics, 2002, Table C16). 15 National Bureau of Statistics, 2002, Table C17.

> 16 These have been calculated from the survey in two ways.  First, the average difference (=6,900 TSH) in per adult equivalent health expenditures over the past 30 days between those with a health (illness or death) shock during the past year and those without (panel 2, table 4) was multiplied by 12, yielding 82,800 TSH as the average annual health expenditures in case of a health shock.   Alternatively, the average expenses incurred per household as a result of an illness or death shock (140,000 TSH from Table 4) divided by the average adult equivalent per household (4.4) yields 31,818 TSH.

> 17 The estimated 16 percent welfare loss associated with a health shock in Kilimanjaro yields an average reduction of consumption per adult equivalent of 30,688 TSH given 191,800 TSH average per adult equivalent consumption in Kilimanjaro 2003.
_Economics and Politics_ , 16(2):189–212.

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22
there are complaints of shortages of skilled (diploma level) nurses. Despite better access to healthcare service, the number of physicians per population in Thailand is lower than Vietnam and Laos. In Vietnam, due to limited number of medical facilities, many medical graduates cannot find work and take on the role of nurses.<sup>6</sup>

**Table 3: Number of registered health personnel**

||**Malaysia**<br>(2000)<br>(d)|**Singapore**<br>(2001)<br>(m)|**Thailand**<br>(2000)<br>(c)|**Indonesia**<br>(2003)<br>(e)|**Philippines**<br>(2003)|**Vietnam**<br>(2002)|**Cambodia**<br>(2001)<br>(f)|**Laos**<br>(1996)<br>(m)|
|---|---|---|---|---|---|---|---|---|
|Total no. of<br>physicians*|16,468|5747|22,435<br>(a)|48531<br>(g)|101,758|45,702|2,055<br>(h)|2812|
|Physician<br>per10,000 pop|7.1|14.0|3.6|2.2|12.7|5.6|1.6|5.9|
|No. of dentists|2,001|1087|6,966|9,177|45,321|NA|NA|196|
|Dentist per<br>10,000 pop|0.9|2.6|1.1|0.4|5.7|NA|NA|4.1|
|No. of<br>pharmacists|2,801|1141|10,354|NA|49,117|6,148|NA|NA|
|Pharmacist per<br>10,000 pop|1.2|2.8|1.7|NA|6.1|0.8|NA|NA|
|No. of nurses|39,890|17,398|119,651<br>(k)|81,190<br>(j)|354,544<br>(b)|47,006|11,105<br>(i)|4931|
|Nurse per<br>10,000 pop|19.3|42.4|19.3|3.8|44.2|5.8|8.5|10.3|

<u>Notes:</u> The population estimates for Indonesia, Cambodia, the Philippines and Vietnam are calculated from UN’s “World Population Prospects: The 2004 Revision Population Database”.

Figures for Lao PDR are not available.

- (a)  75% are medical specialists.

- (b)  A substantial share of registered nurses in the Philippines work abroad (see Section III).

- (c)  From Thammarangsi (2003a)

- (d)  From 8<sup>th</sup> Malaysian Plan (2000) p.489

- (e)  From Indonesia’s Ministry of Health (2001 figures for nurses).

- (f)  Ministry of Health’s employees only

- (g)  23% are specialists

- (h)  Physicians are supplemented by 1,425 medical assistants.

- (i)  In Cambodia, 44.8% are primary nurses and 55.2 are secondary nurses.

- (j)  Only 13% of nurses in Cambodia carry the 3-Year Diploma degree, the rest carry high school diplomas.

- (k)  The figure comprises nurses of various skill levels (from diploma graduates to college graduates).

- (l)  Global Health Atlas of infectious diseases. World Health Organization. Data updated with recent information from Regional Office websites and publications.

- (m)  Data from WHO Global Atlas of the Health Workforce.

6 In the 1980s, Vietnam had cooperation pacts with countries in Africa, e.g. Mozambique, Angola, Lybia, Algeria, Madagascar etc., whereby surplus medical doctors from Vietnam were sent abroad under the pact.

6
<u>Table 3c: HRS on Support Structures</u>

||<br>_Agree/Strongly_<br>_Agree_<br>No Controls<br>(1)|<br>_Agree/Strongly_<br>_Agree_<br>Including Control<br>(2)|_Strongly_<br>_Agree_<br>Including<br>Control<br>(3)|_Including_<br>_Neutral_<br>Including<br>Control<br>(4)|
|---|---|---|---|---|
|Allowed to work<br>independently|-0.106**|-0.10**|-0.06**|-0.07|
||(0.045)|(0.04)|(0.03)|(0.05)|
|Held accountable|-0.014|-0.02|0.01|0.03|
|by Head Teacher|||||
||(0.058)|(0.06)|(0.05)|(0.03)|
|Like more feedback|-0.031|-0.03|-0.10**|-0.00|
|from Head Teacher|(0.029)|(0.03)|(0.04)|(0.01)|
|Relationship with|0.013|0.02|-0.05|-0.01|
|Head Teacher|(0.017)|(0.02)|(0.05)|(0.01)|
|Relationship with<br>colleagues|-0.028*|-0.03*|-0.08|0.00|
||(0.014)|(0.01)|(0.06)|(0.00)|
|Respect the Head|-0.010|-0.01|-0.03|0.00|
|Teacher|||||
||(0.017)|(0.02)|(0.05)|(0.01)|
|Workload is<br>manageable|-0.013|-0.01|-0.04|0.01|
||(0.041)|(0.04)|(0.06)|(0.03)|
|Working hours are<br>too long|-0.053|-0.04|-0.01|-0.04|
||(0.058)|(0.06)|(0.02)|(0.06)|
|Domain 3 Index|-0.140|-0.12|-0.29*|-0.03|
||(0.113)|(0.12)|(0.16)|(0.14)|
|Control|No|Yes|Yes|Yes|

* _p_ <0.1; ** _p_ <0.05; *** _p_ <0.01

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**Table 10: Weight-for-age Z-score (Uganda)**

|||**Model 1**<br>|**- Without C**<br>|**ontrols**<br>||
|---|---|---|---|---|---|
|VARIABLES|OLS|Unc<br>|onditionalqu<br>|antile regres<br>|sion<br>|
|||10th|25th|50th|75th|
|1 - SSEXR|0.073**|0.114*|0.084**|0.106***|0.060|
||(0.031)|(0.065)|(0.041)|(0.035)|(0.037)|
|FCS|0.127***|0.227***|0.138***|0.130***|0.120***|
||(0.028)|(0.058)|(0.039)|(0.034)|(0.035)|
|HDDS|0.036|0.105*|0.067*|0.056|0.005|
||(0.031)|(0.063)|(0.040)|(0.035)|(0.036)|
|Per capita expenditure|0.104***|0.161***|0.118***|0.103***|0.066*|
||(0.033)|(0.062)|(0.038)|(0.035)|(0.038)|
|Food share|-0.023|-0.036|-0.036|-0.026|0.014|
||(0.029)|(0.058)|(0.037)|(0.033)|(0.033)|

## **Model 2 - With Controls**

|VARIABLES|OLS|Unc<br>|onditionalq <br>|uantile regres<br>|sion<br>|
|---|---|---|---|---|---|
|||10th|25th|50th|75th|
|1 – SSEXR|0.044|0.053|0.056|0.089**|0.052|
||(0.034)|(0.070)|(0.044)|(0.038)|(0.040)|
|FCS|0.084***|0.157**|0.103**|0.100***|0.089**|
||(0.031)|(0.062)|(0.044)|(0.039)|(0.040)|
|HDDS|-0.017|0.003|0.011|0.019|-0.029|
||(0.036)|(0.071)|(0.046)|(0.041)|(0.043)|
|Per capita expenditure|0.075*|0.128|0.107**|0.084**|0.052|
||(0.040)|(0.079)|(0.044)|(0.040)|(0.048)|
|Food share|-0.035|-0.057|-0.039|-0.033|0.008|
||(0.030)|(0.059)|(0.039)|(0.034)|(0.036)|

# **5 SYNTHESIS OF RESULTS**

The data, the underlying surveys, and in fact the set of food security indicators we were able to analyze for each country were quite different. Yet there was enough overlap between these indicators for us to draw some general conclusions on the robustness of our main conclusion in various socio-economic, agro-climatic, and cultural settings. Overall, we find strong support for two broad conclusions.

The first is the resounding support for the idea that food and food security-related dimensions remain a critical piece in the malnutrition puzzle. In our results, the various measures of household food security do appear to carry significant signals about the nutritional status of children that reside within the household. This result holds even after we control for a wide array of other socio-economic characteristics of the households that are generally also thought to be associated with the quality of child nutrition.

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# **<u>Table 10. Migration and language: migration intensity measures</u>**

||||||**D**|**estination C**|**ountries/R**|**egions**|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Countries/Regions of**<br>**Origin**|**Arabic**<br>**North**|**Arabic**<br>**South**|**Chinese**<br>**North**|**Chinese**<br>**South**|**English**<br>**North**|**English**<br>**South**|**French**<br>**North**|**French**<br>**South**|**Portuguese**<br>**North**|**Portuguese**<br>**South**|**Spanish**<br>**North**|**Spanish**<br>**South**|
|**Arabic North**|**1.19**|8.95|0.17|0.52|0.36|0.29|0.13|0.05|0.02|0.11|0.05|0.08|
|**Arabic South**|2.83|**3.47**|0.09|0.29|0.27|0.29|4.21|0.58|0.03|0.29|1.67|0.08|
|**Chinese North**|0.05|0.07|**2.12**|6.30|2.09|0.14|0.11|0.07|0.38|0.36|0.00|0.20|
|**Chinese South**|0.06|0.08|16.42|**0**|0.87|0.73|0.16|0.09|0.08|0.22|0.31|0.17|
|**English North**|0.15|0.21|0.10|1.15|**1.91**|0.40|0.38|0.12|0.42|0.23|0.74|1.18|
|**English South**|1.89|0.84|0.28|1.37|0.75|**2.05**|0.11|0.55|0.12|1.09|0.13|0.05|
|**French North**|0.26|0.35|0.04|0.46|0.74|0.29|**4.03**|2.22|9.74|0.78|5.98|0.50|
|**French South**|0.05|0.96|0.01|0.05|0.20|1.41|1.15|**7.04**|0.09|0.41|0.16|0.31|
|**Portuguese North**|0.06|0.08|0.05|0.17|0.66|0.21|7.61|0.13|**0**|17.23|2.19|0.95|
|**Portuguese South**|0.04|0.08|0.03|7.85|0.30|2.72|0.43|0.92|23.83|**2.01**|0.83|1.52|
|**Spanish North**|0.07|0.10|0.04|0.24|0.51|0.28|6.45|0.12|2.12|3.80|**1.81**|6.03|
|**Spanish South**|0.09|0.08|0.05|0.40|1.84|0.12|0.08|0.15|0.21|0.69|2.19|**3.17**|

Note: Calculations based on the sub-sample of 163 countries.

26