rafmacalaba/data-use-mentions-extended huggingface dataset:
GDP. These averages induce a strong decline in trajectories of foreign debt.<sup>12</sup> These estimated long run values, however, do not seem reasonable. For example, the implied long run value for the debt shock is highly aected by the debt relief episode associated with the HIPC initiative. In addition, it is dicult to believe that the yearly rate of appreciation of the real exchange rate in Senegal will be, on average, 5 percentage points for the indenite future. Therefore, it is important to be particularly careful with the long run values of the determinants of debt implied by the proposed country-specic intercept. To make this point more clear, the lower panel of Figure 6 displays results analogous to those in the top panel, but setting the long run values of the endogenous variables in Senegal equal to the pooled averages across time and countries.<sup>13</sup> Results are quite dierent from those in the top panel. For example, the median debt trajectory now decreases to only 29 percentage points of GDP. Moreover, the probability that debt will increase to 60 percentage points of GDP by 2020 is now over 20 percent, more than 12 percentage points larger than the probability displayed in the top panel.

In sum, this exercise highlights the importance of having reasonable and accurate forecasts for the long run values of the main determinants of debt. Historical data is probably not the best way to do that, given the changing environment in low income countries. Here, the insight of experts knowledgeable of the country's idiosyncrasies could be extremely valuable. Alternatively, one could perform debt sustainability analysis under dierent long run scenarios to assess the robustness of the results.

# 4.1 Comparison with the IMFWorld Bank DSA

This subsection explores the consequences of ignoring the co-movements between the determinants of debt by comparing the predictions of the stress tests of a typical IMFWorld Bank DSA (IDA and IMF, 2011)
>11.2|14.2|9.7|
|Inequality (%)<br>39.7|40.3|38.1|
|Source: Authors’ calculation using forecasted HEIS 2018/2019.|||
|Note: Inequalityis based on welfare aggregate without spatial adjustment.|||

For illustrative purposes, we have also calculated how much it would cost just to maintain poverty at prereform levels (rather than decreasing it as the proposed GOI measure does). We do this by providing universal cash transfers. Results are shown in Table 12. The amount of daily per capita transfer will be about IRR 8,380– smaller than the GOI proposal. The overall costs of the cash transfers would be about IRR 300 trillion. This will be lower than the costs of the GOI program despite being universal. The overall net savings will be higher at about IRR 139 trillion.

Table 12. The impact on Government annual spending and revenues after increase in gasoline prices and <u>providing universal cash transfers, trillion IRR</u> 11
_By Income Level:_**|||
|Low Income LDCs|33%|65%|
|Lower-Middle Income LDCs|50%|54%|
|Upper-Middle Income LDCs|63%|67%|
|Developed|88%|55%|
|**_LDCs By Region:_**|||
|Sub-Saharan Africa|30%|75%|
|East Asia and Pacific|36%|19%|
|Europe & Central Asia|70%|59%|
|Latin America & Caribbean|73%|73%|
|Middle East & North Africa|7%|50%|
|South Asia|25%|75%|
|**Total**|**53%**|**60%**|

Source: Author compilation from various sources (ITU for telecoms and consultation with World Bank, IDB, ADB , EBRD and EEC economists for electricity) A second indicator is the extent to which corruption has declined with reforms in the sector. Here the story is not as positive. While there is no long term monitoring of corruption in this sector, there is partial information available from various recent surveys of perception by foreign investors summarized in the World Bank Governance database. The database shows that, between 1999 and 2002, for the richest developing countries, corruption has declined but extremely modestly (no change is rated at 4 on a scale of 1 to 7 and that country group was rated at 4.1). The other two country groups (low middle and low income LDCs) have seen corruption increase (the rating is 3.3 and 3.7 respectively). Of course, these are

> 14 See for example Stern and Holder (1999) and Stern, J. and J. Cubbin (2003)

> 15 See for instance  the recent book by Kessides (2004) for an overview of the debate.

11
employees). Second, we rerun the regression above with the variables captured in the baseline ES and include further variables captured in the follow-up survey rounds reflecting how firm circumstances changed during the pandemic (changed business practices, received any cash support from government). A noteworthy limitation of this analysis is that there is a nontrivial number of missing values for several of these survey questions. As such, as a robustness check we present in Appendix B the analysis excluding all respondents with missing values.

# **4 Findings**

## **4.1 Sales**

### **4.1.1 Survey findings**

According to the survey data, on average, formal firms in Zambia experienced a very large decline in sales in 2020 and a substantial recovery in the first half of 2021. Figure 3 shows the average decline in sales (compared to the same month in 2019) was 42.7 percent in June 2020, 33.9 percent in December 2020, and 16.4 percent in July 2021. The results dis-aggregated by firm size are striking. By July 2021, the average sales of large firms had fully recovered to 2019 levels; in contrast, small and medium-sized firms reported a larger initial decline and a slower recovery.

These average changes in sales somewhat mask variation between firms. Figure 4 shows that most firms reported a decline in sales (compared to the same month in 2019) in June and December 2020, but by July 2021 less than half of the firms reported a decline in sales. As was the case for the average change, large firms fared much better than small and mediumsized firms. In July 2021 only around one-third of large firms reported a decline in sales (compared to the same month in 2019), and almost half reported an increase in sales.

17
Unadjusted and adjusted changes in risk associated with simultaneous access to nutrition drivers for infant and early childhood stunting (0-23 months of age) <!-- Start of picture text --> Unadjusted Adjusted<br>Any 1 domains (Definition 3) — ——_—_<br>Any 2 domains (Definition 3) aoe —.—-<br>Any 3 domains (Definition 3)} 2s a<br>Any 4 or 5 domains (Definition 3) ose a<br>Female oe oe<br>Female, 0-6 mo ——8_ ___ ><br>Single birth] —-s+— —<br>Urban +7<br>Total HH size (N) +<br>Total children under five in HH (N) tit<br>Total HH income per month (1 = 1000 PHP) (log) —<br>In poorest quintile of total HH income per capita —e<br>Mother has high school or tertiary education o—<br>Mother is married ——-<br>Mom wasa teenager at time of birth —_——<br>Mother has a job or business ><br>0.5 1:0 15 05 10 #15 |<br>Relative Risk (95% Cl)<br><!-- End of picture text -->

Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: Only the relative risk estimates for statistically significant predictors are labeled.
variety of reforms have been introduced, including the

> 4  Figures on coverage are from China’s National Health Survey (NHS), undertaken by the Ministry of Health (MOH) in 1993, 1998 and 2003. The figures for 1993 and 1998 are published in Gao et al. (2002). The urban insurance figures for 2003 are taken from “Health Services Utilization and Urban Health Insurance Reform in China”, a presentation by Ling Xu of China’s MOH in December 2004 at an MOH seminar on the 2003 NHS. The rural figures for 2003 are from “Main Findings from the 3rd NHS Survey”, available online at www.moh.gov.cn, accessed on April 21st, 2005.
Non-zero<br>Zero Childbirth in January 2012-December 2014<br>Drug spending before pregn. Childbirth in October 2015-December 2017<br>Non-zero<br>Zero<br>0 .05 .1 .15 .2<br>Share with major wage increase<br>Childbirth in January 2012-December 2014<br>Childbirth in October 2015-December 2017<br><!-- End of picture text --> _Note:_ Figure shows heterogeneity in reported earnings and employment before giving birth by individual, firm, and job characteristics. Panel (a) shows variation in the probability of a pre-childbirth top-percentile wage increase by individual and firm characteristics (average probability and 95% confidence interval). Panel (b) shows changes in non-employment to employment transition probabilities before giving birth, compared to thirty to ten months before childbirth. The panel shows estimated _β_ parameters and 95% confidence intervals from a modified version of equation (1), with a binary indicator of pregnancy period (one to nine months before childbirth versus ten to thirty months before childbirth) replacing the event time dummies, and using an indicator for transitioning from non-employment to employment indicators as the dependent variable. Control variables include age dummies, county dummies, and calendar month date dummies. The sample is restricted to women who enter formal employment from non-employment, and the outcome is formal employment in a specific job category. In both panels, results for 2012-2014, before reporting incentives were strengthened, are shown by blue circles and results for 2015-2017, after reporting incentives were strengthened, are shown by red squares. We calculate the value added-based TFP. When doing so, we apply the estimation procedure of Wooldridge (2009) and use the `prodest` Stata package by Rovigatti and Mollisi (2020). TFP quartiles are calculated based on the year-specific distribution of TFP, using the entire linked employer-employee database. The heterogeneity indicators refer to twelve months before giving birth, except for the drug spending indicators, which refer to _−_ 24 to _−
|2.9|17.9|
|national AROPE rate|31.7|78.7|

Note: Percentage of individuals (using survey weights). See Table 1 and Table 2 for a detailed description of each AROPE dimension.

Source: Own estimates based on 2021 Bulgaria SILC. We report the survey year, and not the income year. AROPE definition based on EU2030 target.

**Even after accounting for relevant individual and family characteristics, Roma individuals still exhibit a significantly higher likelihood of experiencing income poverty, living in households with low work intensity, and enduring severe material and social deprivation compared to the Bulgarian population (Figure 4 and Table A4.9).** Additionally, when examining conditional probabilities of being deprived in a particular dimension of multidimensional poverty, the AROPE rates for Roma households surpass those of all other vulnerable household types. Specifically, the probability of experiencing severe material and social deprivation among Roma households is notably higher by 12.8 percentage points, surpassing the estimated probability for other vulnerable households. Consequently, the overall likelihood of being classified as AROPE is markedly greater for Roma individuals, with a notable increase of 15.9 percentage points. This underscores the critical importance of properly identifying Roma as a vulnerable group using official household surveys and measures, targeting social policies to protect this group, and prioritizing fiscal support for the Roma community in national poverty reduction strategies aimed at alleviating poverty and social exclusion.

29
The lowest correlation coefficient in our sample is 0.53 for Greece, while the highest is 0.89 for Czechia.<sup>13</sup> These represent respectively the high and low mobility scenario, as the closer the coefficient is to one, the stickier incomes are. A further assumption then is that these imported coefficients are sufficiently extreme to form upper and lower bounds in the case of Malaysia. Given the variety of countries included, at various levels of economic development, we suggest that this assumption is not unreasonable.

# 2.5. Data

This study analyzes eight rounds of cross-sectional data from the Household Income Survey (HIS) collected by the Department of Statistics Malaysia. The data are for the years 2004, 2007, 2009, 2012, 2014, 2016, 2019 and 2022; we consider all pairs of adjacent survey rounds.<sup>14</sup> One requirement for analyzing welfare changes over time is that the measure of welfare, income in our case, is measured in a consistent and comparable manner. The Malaysian data are of high quality and satisfy this requirement. In addition, the sample sizes of the surveys are large, ranging from 36,000 to over 80,000 household-level observations per survey year.

The dependent variable in our analysis is the natural logarithm of monthly household pre-tax income per capita, which is evaluated against our poverty line to determine the poverty status of a household. We did not use post-tax income because few Malaysians are subject to personal income tax; it is thus unlikely to affect poverty or vulnerability status. Households report income over the twelve months preceding the interview. Notably, this means that households interviewed in 2022 also report over part of 2021.

> 13 In the case of Greece, this is the correlation of disposable household income between 2001-2004; for Czechia, it is the 2014-2015 correlation of disposable incomes.

> 14 Surveys may extend into the subsequent year. When we refer to ‘round 1’, this indicates the first round
May). Predicting individual wellbeing through test scores : evidence from a national assessment in Mexico. Policy Research Working Paper Series 8459, The World Bank.

- Duflo, E., P. Dupas, and M. Kremer (2011a, August). Peer effects, teacher incentives, and the impact of tracking: Evidence from a randomized evaluation in kenya. _American Economic Review 101_ (5), 1739–74.

- Duflo, E., P. Dupas, and M. Kremer (2011b). Peer effects, teacher incentives, and the impact of tracking: Evidence from a randomized evaluation in kenya. _American Economic Review 101_ (5), 1739–74.

- Edmonds, E. V. and C. B. Theoharides (2019, August). The short term impact of a productive asset transfer in families with child labor: Experimental evidence from the philippines. Working Paper 26190, National Bureau of Economic Research.

27
Nonetheless, the combination of these three outcomes— female homicides, female overnight hospitalizations for assault, and mandatory reports by health workers of physical and sexual violence against women—covers a range of types of violence, from the most severe (homicide) to less severe, and serves as an important complement to survey data.

_Contextualizing violence outcomes_ . The outcome variable on hospitalizations for assault is only associated with overnight stays. Aizer (2010) refers to hospitalizations for assault, but does not indicate if the variable also includes emergency room visits, which would not be included in the hospitalization outcome variable if they were of short duration. One-fifth of Californians were uninsured in 1995 (Bennefield 1996); the rate is likely higher among the low-skilled workers in Aizer’s sample. These victims would be more likely to seek emergency room care because of their more prevalent lack of insurance coverage. Brazil, on the other hand, offers universal health insurance; so, similar injuries could be treated at local clinics, suggesting that Aizer’s measure of violence is most similar to the report outcomes in this study.

# **Gender Wage Ratio**

The analysis relies on administrative individual-level microdata from Brazil’s Ministry of Labor and Employment on the formal sector labor force in 2011–16 to measure the gender wage ratio in each Brazilian municipality.<sup>8</sup> In addition to the raw wage ratio, following Aizer (2010), a predicted wage ratio was also constructed for the analysis that isolates the variation caused by shifts in labor demand that affect labor market opportunities among women relative to men. This technique helps predict the average wage for each sex in a municipality based on the distribution of workers across sectors in the municipality at the start of the sampling process, as well as changes in the industry average wage among workers outside the municipality. This measure is thus not affected by the movements of women into and out of employment and across sectors that may be associated with violence. This measure is referred
, the map shows that Barahona and Puerto del Plata have districts with a higher vulnerability rate than the rest. In Ecuador, the vulnerable households are concentrated in the east, the region with more effective poverty rates. But where the TH index of landslides suggests more potential damage is in the central region, where some cantons also show a high vulnerability. El Salvador's vulnerability rate for droughts, except for San Salvador, is very similar among provinces. But some differences are found between districts. In particular, some districts of Morazan and Ahuachapan show a high rate in comparison to districts of other provinces. Honduras shows that the administrative unit at level 1 with the highest percentage of vulnerable households when earthquakes are considered is Lempira (78%). The map shows that many other districts present a sizeable rate. But, in Lempira's case, all their districts present a significant rate. Lempira is located in the west, corresponding to a region with significant potential earthquake damage.

15
the bottom quintile of technology readiness (i.e. firms that were rarely adopting any digital technology) were significantly less likely to adjust and were harder hit by the shock (controlling for observable characteristics such as size, sector, region, and management quality).

Our results suggest that an important additional benefit from upgrading technology might be an increase in the resilience of businesses to shocks. Given the increasing interest by government agencies in implementing technology upgrading programs to support digitization, it is important to emphasize this additional benefit in order to increase the incentives to upgrade and maximize program take up.

# **References**

1. M. C. Apedo-Amah, _et al._ , Businesses through the covid-19 shock: Firm-level evidence from around the world, _Policy Research Working Paper 9434_ , World Bank (2020).

2. A. W. Bartik, _et al._ , The impact of covid-19 on small business outcomes and expectations, _Proceedings of the National Academy of Sciences_ **117** , 17656 (2020).

3. R. Dai, J. Hu, X. Z. Zhang, The impact of coronavirus on china’s smes: Findings from the enterprise survey for innovation and entrepreneurship in china, _Working paper_ , Center for Global Development (2020).

4. J. E. Humphries, C. Neilson, G. Ulyssea, The evolving impacts of covid-19 on small businesses since the cares act, _Cowles Foundation Discussion Paper No. 2230_ (2020).

5. A. Adams-Prassl, T. Boneva, M. Golin, C. Rauh, Inequality in the impact of the coronavirus shock: Evidence from real time surveys, _Journal of Public Economics_ **189** , 104245 (2020).

6. R. W. Fairlie, The impact of covid-19 on small business owners: Evidence of early-stage losses from the april 2020 current population survey, _Working Paper 27309_ , National Bureau of Economic Research (2020).

20
the point estimate increases slightly, though it is not very sensitive. Figure 4b shows the similar results for the India data set. Like the Mekong region, productivity falls by 5.5‐6.6 percent when EC exceeds this threshold. This threshold is exceeded by approximately 5 percent of observations in both India and the Mekong River basin. Results using the GEMStat data are shown in figure 4c. Globally, it is found that when EC exceeds 100 mS/m, yields decline by 11.0 to 13.5 percent.

Next, we estimate a more flexible model. Figure 5 shows results from creating six bins of EC, each 40mS/m wide, with the 0‐40mS/m bin as the comparison group. The line graph displays the change in yields (left axis) due to a corresponding EC value shown on the x‐axis. Results show a nearly linear decline in yields as EC rises. Even when EC is in the lowest bin, at 40‐80 mS/m, the decline in yields is statistically detectable, and significant in magnitude at between 5.2 and 7.4 percent, relative to the omitted bin. Further, the regression is not sensitive to changing the cropland land use restriction, as all point estimates are within a tight range and all 95 percent confidence intervals overlap. The bar graphs display the percentage of observations falling into each of the buckets and correspond to the right‐axis. Across all 3 samples, non‐impacted observations (0‐40 mS/m) represent about 50‐60 percent of the sample. The next bin (40‐80 mS/m) is about 30 percent of the sample, and all subsequent bins are less than 10 percent.

# 5. Robustness Tests

In this section we present results from two additional specifications that have been tested to corroborate the main results presented in section 4. First, we show results where we vary the distance threshold to each upstream monitoring station. Then we show
risk of dropping out</u>

The second component of ENTRE is a list of sixth-grade students at high risk of dropping out in the transition to lower secondary, providing school actors with more information on _who_ needs the most help. The list was estimated using routinely collected administrative panel data on students and combining linear regression models with a simple algorithm. These models are able to correctly identify 82% of the sixth-grade students who will drop out within the next year, performing better than other commonly used targeting approaches, and as well as models used in the U.S. (For more details on the methodology, please see Adelman et al., 2018).

9
27.7||28.8|27.8|
|Log GDP per capita<br>9.2<br>6.69|***|9.3<br>6.42|***<br>9.4<br>5.93<br>***|
|Share of Agriculture<br>in GDP<br>11.5<br>-0.67|***|10.9<br>-0.65|***<br>10.8<br>-0.58<br>***|
|Trade Openness<br>93.1<br>0.05|***|97.2<br>0.05|***<br>95.1<br>0.05<br>***|
|Age Dependency<br>Ratio<br>60.6<br>-0.34|***|59.4<br>-0.35|***<br>59.3<br>-0.30<br>***|

_Notes:_ The dotted line represents the result of a bivariate linear regression of revenue on the indicator shown on the x-axis. _Source:_ Authors’ calculation based on the variables and data sources described in Section III.

_Notes:_ Significance levels (Sign.) correspond to p-values in bivariate regression of revenue on respective indicator: _* p<0.10, ** p<0.05, *** p<0.01. S_ amples consist of a maximum of 182 countries (including high-income countries). _Source:_ Authors’ calculation based on the variables and data sources described in Section III.

**Table A3** . Data on World Bank Group Engagement in DRM

|Country|Income<br>|Region
0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br><!-- End of picture text --> <!-- Start of picture text --> Tunisia: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram-2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br><!-- End of picture text --> <!-- Start of picture text --> Yemen: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>Libya: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br>6<br>6.00<br>