rafmacalaba/datause-encoder-data huggingface dataset:
Table 13: Gender Gaps in Components of the Job Quality, 2007-2021 (differences in percentage points)

||(1)<br>Social<br>security<br>benefts|(2)<br>Minimum wage<br>compliance|Job<br>(3)<br>Low incidence<br>of child labor|Quality Compon<br>(4)<br>Low incidence<br>of relative<br>underpayment|ents<br>(5)<br>Low incidence<br>of compensatory<br>moonlighting|(6)<br>Tenure|(7)<br>Low incidence<br>of<br>overqualifcation|
|---|---|---|---|---|---|---|---|
|2007|4.27|19.20|14.88|19.84|16.52|4.84|-3.73|
|2008|3.62|21.87|16.41|21.81|17.44|2.83|-7.34|
|2009|3.73|19.60|14.86|22.31|18.32|0.45|-7.28|
|2011|4.87|22.56|16.79|23.10|20.78|-0.57|-7.15|
|2012|3.09|19.58|17.41|17.16|17.29|1.25|-4.63|
|2013|4.23|20.53|16.67|19.38|18.43|-0.09|-4.34|
|2014|2.19|20.82|20.67|20.36|19.14|0.81|-5.15|
|2015|3.78|20.46|20.81|18.29|19.79|1.88|-1.76|
|2016|2.72|20.49|19.18|19.45|22.76|-0.82|-5.67|
|2017|2.82|19.49|22.33|18.53|21.32|0.35|-6.27|
|2018|2.93|18.04|19.39|18.82|21.74|0.46|-6.55|
|2019|4.85|21.11|20.93|19.00|22.87|0.76|0.58|
|2020|2.84|18.19|22.17|19.97|18.84|1.95|-4.90|
|2021|0.53|19.69|19.51|17.56|21.55|2.02|-1.84|

_Notes:_ Based on data from EH 2007-2021.

This likely explains the significant variance (and weight) assigned to this component in the Principal Component Analysis. Furthermore, it is important to acknowledge that strict labor regulations limit the creation of formal jobs that provide access to the AFP. Only 5.6 percent of the country’s unemployed population (including the unemployed and low-income self-employed) secure formal sector employment in a given quarter. Combined with over twothirds of the population being self-employed or employed in micro and small enterprises, this has contributed to low levels of access to social security, ultimately impacting job quality. Moreover, these figures highlight the ongoing fragility of establishing a safety net for the future elderly population.

It is critical to note that the decline in job quality observed between 2013 and 2014 was primarily driven by a decrease in the share of workers with social security benefits and compliance with the minimum wage, as evident in Columns (1) and (2) of Table 4. Furthermore, the deterioration in access to social security predominantly occurred in the mining sector, with the share of jobs with access to social security decreasing from 63 percent to 40 percent between 2013 and 2014. Other sectors, such as financial and enterprise services, utilities, and public administration, also experienced a reduction in access to social security.<sup>25</sup>

> 25For these cases, access to social security benefits declined by 10 p.p., 9 p.p. and 8 p.p. in the financial and enterprise services, utilities and public administration sectors, respectively.

29
# **5.1 Simulation design**

Our Monte-Carlo setup is similar to that in Ackerberg et al. (2015), based on Syverson (2001) and Van Biesebroeck (2007). The labor equation is determined by the first condition of Equation 5. The model parameters, in most cases, are borrowed from Ackerberg et al. (2015). The production function is assumed to follow either a Cobb-Douglas or a CES data generating processes (DGPs). For the Cobb-Douglas case, we simulate while, for the CES case with _βL_ = 0 _._ 7 and _βK_ = 0 _._ 3. Productivity, _ωjt_ , follows an AR(1) process inclusive of regulation.

with _φ_<sup>_ω_</sup> = 0 _._ 1, and _ε_<sup>_ω_</sup> _jt_<sup>are normally distributed with mean zero and a 0.1 standard deviation.</sup> Meanwhile, _zjt_ is a dummy variable, equal to 1 or 0, and is randomly assigned to firms. The stock of capital evolves according to _Kjt_ +1 = (1 _− δ_ ) _Kjt_ + _Ijt_ with _δ_ = 0 _._ 05. Optimal firm investment is based on maximizing the expected (discounted) value of future profits, with convex capital adjustments costs. Based on Angelopoulou and Kalyvitis (2012), we simplify the investment equation for the Cobb-Douglas case as follows 22
corporation on HAZ scores, controlling for the base set of factors. The reference category for the effect of birth location is mother born in the current city corporation. The reference category for the effect of reason for move is mother always resided in the current city corporation. Mothers who moved for reasons other than work or family are omitted from the regressions. The regressions for the effect of time since the mother’s move is conditional on moving to the current city corporation.

Mother born in another urban center has a significant positive effect of 0.18 SD in the pooled sample. The pooled-sample effect is driven by the slum-sample effect (0.25 SD). Mother born in a rural area has a significant positive effect of 0.14 SD in the nonslum sample. Mother moving for work reasons has a significant positive effect of 0.30 SD in the nonslum sample. Consistent with the flat patterns in the bivariate plots in Figure 5, Panel C, time since the mother moved has positive, but insignificant, effects in all samples. Discretizing years since the mother moved to the current city corporation, Ahsan et al. (2017) also find that this factor does not have a significant effect on child stunting status in their slum and nonslum regressions. At the very least, the collective evidence suggests that mother’s migration to the current city corporation, whether to a slum or nonslum neighborhood area, does not appear to have a negative effect on child growth.

In related research, Islam and Gagnon (2016) use 2006 BUHS data to examine the effects of mother’s migration on the use of RMNCH services. Specifically, the authors look at the effects of whether the mother moved to a city corporation; and, for migrant mothers, how long the mother had resided in the current city corporation, whether the mother was born in a rural area, and whether the mother self-reported moving for employment or education reasons. They find that mother’s migration has significant negative effects on the use of different maternal and child health services, while years lived in the current city corporation only has a positive effect on the use of antenatal care services. Given these findings, we re-estimate the effects of our various measures related to the mother moving to the current city corporation, controlling for the use of maternal and child health services in the relevant child subsamples. Our results generally hold.

G. _Decomposition of slum-nonslum difference in child HAZ scores_ 29
estimate of the collected tariff rates where we adjust for the fact that most imports from CIS countries enter with zero tariffs.

Briefly, our key results are the following. The average tariff in Russia has increased between 2001 and 2003 from about 11.5% to between 13% and 14.5%, but it has held steady in 2004 and 2005. This places Russia’s tariffs at a level slightly higher than other middle-income countries and considerably higher than the OECD countries. The tariff structure became much more diverse between 2001 and 2003, but the dispersion of the tariff moderated in 2004 and 2005. Notably the trade weighted standard deviation of the tariff approximately doubled from 9.5 percent in 2001 to 18 percent in 2003, but then fell to 15.2 percent by 2005. “Tariff peaks,” that is, tariff lines with very high tariffs, are more of a problem in 2005 than in 2001, but less so than in 2003. The reason for the increase in the tariffs is the specific tariffs, as the ad valorem rates have not increased. More tariff lines are subject to specific tariffs in 2003-2005 than in 2001, and the appreciation of the euro, relative to 2001 has increased the ad valorem equivalent of the specific tariffs. The food sector and light industry are the aggregate sectors with the highest tariff rates—their tariff rates in 2005 were 23.1% and 19.5% on a trade-weighted basis. At the two digit level, motor vehicles, footwear, leather products and sugar are among the most highly protected.

Regarding the previous efforts to assess the Russian tariff regime, Tarr (1999) has assessed the average level of the Russian tariff and the degree of tariff escalation by stage of production. Afontsev has used a Grossman-Helpman model in a few papers on Russian tariff policy. In Afontsev (2002), he assessed to extent to which industrial lobbying, consumer welfare or government revenue can explain the formation of Russian tariff policy in 1992-1997. Afontsev (2004) assesses the motivation of the government for the tariff unification policy of 2000-2001. In addition, there are several unpublished efforts to assess the average MFN tariff level in Russia.<sup>3</sup> Although the previous studies of the Russian tariff have provided a reasonable assessment of the Russian ad valorem tariffs, previous efforts have been hampered by two problems: (1) about ten to fifteen percent of the tariff lines of Russia use a “combined” tariff rate system. For these tariff lines, both ad valorem and specific tariffs are indicated, and the actual tariff applied by Russian customs is the maximum of the

> 3 Staff of both the World Bank and the International Monetary Fund have calculated the average Russian tariff on several occasions. We briefly discuss those results below.

3
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# International Trod*

WPS 610

**This paper-** a ploduct of the Interamaon **Trade** Division, **I** rematon Economics Deparlment - is part of a larger effort in PRE to improve developing countries' ability to make more effective use of their financial resources in the procurement of imports vital to industrialization and growth. Copies ame available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Jean Jacobson, room S7-037, extension 33710 (31 pages).

Many developing countries use preshipment inspection (PSI) firms to counter the adverse effects on their foreign trad. .f certain pricing and business practices. These firns may also perform some national customs functions, but Lheir key responsibility is normally to verify that imports (and sometimes exports) meet quality and quantity standards and that prices are within established norms.

Developing countries make substantial - payments for PSI charges appear to average about I percent of the value of the goods in- spected but have undertaken no comprehensive cost-benefit studies of PSI.

Using data from Madagascar's experience, Yeats analyzes the impact of PSI on Madagascar's relative impon prices. The results suggest that Madagascar paid considerably higher prices than other developing and industrial countries both before and after PSI was adopted.

In other words, preshipment inspection failed to reduce Madagascar's import prices to the level of those paid (on average) by other importers. Extreme prices (150 percent or more above average) occur for all typcs of goods imported by Madagascar but are clustered in chemicals (SITC 5) and basic manufactures (SITC 6).

Evidence suggests that collaborative false invoicing by Madagascar importers and industrial country exporters is one reason for the excessive prices both before and after adoption of PSI.

The PRE Working Paper Series disseminates the findings of work under way in the Bank's Policy, Research, and Extemral AffairsComplex. An objectiveof the series is to getthese fmdings out quickly, even if presentadons are less than fully polished. l The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the PRE Dissemination Center
7 opposite prediction: vote-buying increases the rents of those whose votes are bought relative to what they would have received if vote-buying were not possible.

In contrast to nearly all of the research on clientelism and vote-buying, research on political budget cycles is explicitly concerned with the temporal phenomenon of government spending just prior to elections, and why that spending is much higher in some countries than in others. The nature of this spending is not well-documented, but as Hanusch and Keefer (2013) argue, in view of the resources dedicated to vote-buying in many countries, vote-buying is likely to play a role in political budget cycles. It is reasonable, therefore, to ask whether the literature on political budget cycles can explain variations across countries in the reliance of politicians on pre-electoral transfers to voters. In that literature, in contrast to the analysis here, information asymmetries play a large role.

In Khemani (2004) and Drazen and Eslava (2008), the identity of pivotal voters changes over time. Since politicians do not know who will be pivotal at election time, they wait until close to the election to direct resources to them. Much of the political budget cycle literature focuses on a different information problem: voters are uncertain of politician competence and their perceptions of competence shift over time. Politicians can spend resources to demonstrate their competence, but because perceptions shift over time, they undertake this spending just before the election (see Shi and Svensson 2006, Alt and Lassen, 2006a, Saporiti and Streb 2008, and Hanusch 2012 a, b, all drawing on Lohmann, 1998)). Brender and Drazen (2005, p. 1273) argue that in ‘new’ democracies “fiscal manipulation may work because voters are inexperienced with electoral politics or may simply lack the information needed to evaluate fiscal manipulation that is produced in more established democracies”. Alternatively, as in Brender and Drazen (2007), new democracies are more vulnerable to coups, particularly at election time, so incumbents use election-year spending to convince voters about the ability of elected officials to deliver benefits to them.
associated with such an intervention, it amounts to around $5,300 per case. Compared to a typical benchmark for cost-eectiveness of $150, targeting all travelers would be 35 times as costly. Therefore, if a policy were to target travelers, only a subset should be targeted.

There are two ways that a policy can be targeted: (1) it can target certain destination areas for travelers or (2) it can target travelers from specic origin areas. For both cases there are two sets of costs: (1) xed cost, which consists of training community health workers, health post nurses, and district health supervisors in investigation of travel cases as well as costs for weekly electronic data transmission; and (2) variable cost, which are the costs associated with investigating and treating travelers.<sup>26</sup> I use the Fiscal Year 2015 Malaria Operational Plan for Senegal for the policy simulations. Based on this plan, $400,000 was allocated for case investigation and targeting individuals in districts with incidence less than 5/1000. Given there were 19 such districts in 2015, I assigned the funding proportional to district population in order to assign an amount to the ve districts that are part of the simulation. This leads to $127,850 dedicated to the ve low malaria districts studied here. I assume an even split between xed and variable costs. To calculate the variable cost per person, I use the information available from Richard Toll where 3,609 people were targeted in 2015. Variable funding for that district of $10,286 leads to a per person cost of $2.85.

The rst type of targeting focuses on destination areas. Given some set of resources R meant to target travelers, those resources can either be distributed across all health facilities or they can be concentrated in a smaller set of facilities in certain areas and in certain months. The most naive method for targeting would be to randomly select health facility areas and months and target all travelers in the areas/months randomly selected. This is in eect equivalent to not targeting. In practice this would never be done since at minimum policymakers know when malaria is most prevalent and would not target travelers during months the country is eectively malaria-free. Therefore, a second strategy would be to target randomly certain health areas during the months of high malaria prevalence rst (August

> 26For simplicity, I assume the variable cost scales proportionally with the number of travelers.

29
||**Table 5: N**<br> <br>(Value, in|**ational Inc**<br>GDP<br>$billions)|**ome of**|**Selected O**<br>|**ECD Cou**<br> <br>(Value, in|**ntries**<br>GDP per c<br>$)|apita||
|---|---|---|---|---|---|---|---|---|
||Japan|USA|UK|Germany|Japan|USA|UK|Germany|
|1950|109|2883|377|234|131|1897|744|468|
|1960|440|5153|727|722|468|2852|1390|1302|
|1970|2033|10155|1249|1846|1949|4952|2254|3041|
|1980|10632|27319|5353|8185|9103|11996|9567|13296|
|1990|30522|58033|9946|15470|24718|23209|17377|24458|
|2000|47661|98247|14409|18752|37574|34796|24571|22844|
||(Index, US<br>|A=100.0)<br>||<br>|(Index,<br>|USA=100.<br>|0)<br>||
||Japan|USA|UK|Germany|Japan|USA|UK|Germany|
|1950|3.8|100.0|13.1|8.1|6.9|100.0|39.2|24.7|
|1960|8.5|100.0|14.1|14.0|16.4|100.0|48.7|45.7|
|1970|20.0|100.0|12.3|18.2|39.4|100.0|45.5|61.4|
|1980|38.9|100.0|19.6|30.0|75.9|100.0|79.8|110.8|
|1990|52.6|100.0|17.1|26.7|106.5|100.0|74.9|105.4|
|2000|48.5<br>(Source) IM|100.0<br>F|14.7|19.1|108.0|100.0|70.6|65.7|

28
# �� = �+ ������������� + ��,

where the unit of observation � can represent a woman, a CHW or a cooperative. � is an outcome measured at the follow-up survey. ������������ is a dummy for belonging to a sector assigned to the CHW cooperative performance payments treatment. When the unit of observation is women or CHWs, the error term is clustered at the sector level.

# _<u>Impact on targeted maternal and child health services</u>_

The first two columns of Table 5 report the results on the impact of the intervention on utilization of the targeted maternal health services: timely antenatal care and in-facility delivery. The sample used for the analysis is that of women with recent pregnancies that resulted in live births. There is no impact of the performance incentives on initiation of antenatal care within the first four months of pregnancy. There is also no impact found on the rate of women who deliver in health facilities. It is important to keep in mind, however, that by the time of the follow-up survey the rate of in-facility deliveries was 95 percent in the control group.

Results of the impact of the intervention on child growth monitoring are presented in the third column of Table 5. The indicator used is whether a child was measured in the 6 months preceding the survey to determine his or her nutritional status. We perform the analysis among children 6-59 months old of the sample of women with recent pregnancies. There is no statistically significant difference between the treatment arms with respect to the rate of children who received growth monitoring services. We report the results for children of women with recent pregnancies because, as discussed in the previous section, attrition was unbalanced for the other sample. Among ‘baseline women,’ attrition was higher for women residing in treatment sectors under the CHW cooperative performance payments scheme. We did however perform the analysis also for the larger sample of children of all women and found no evidence of impact of the program on rates of growth monitoring.

# _<u>Impact on use of modern family planning methods</u>_

The sample used for estimating the impact of the CHW cooperative performance incentives on family planning is that of ‘baseline women’. The two outcomes explored are an indicator for having had an additional pregnancy since the baseline interview and whether the women reported using modern 11
**<u>Table 4: Lease price regressions for agricultural land for commercial use</u>**

||<br>**All tran**|<br>**sactions**|<br>**With con**<br>**durat**|<br>**tract**<br>**ion**|**With ten**<br>**characteri**|**ant**<br>**stics**<br>**T**|**enant details**<br>**durat**|**& contract**<br>**ion**|
|---|---|---|---|---|---|---|---|---|
|Area (ha)<br>Area^2|0.071***<br>(0.001)<br>-0.004***<br>(0.001)|0.061***<br>(0.001)<br>-0.004***<br>(0.001)|0.068***<br>(0.001)<br>-0.004***<br>(0.001)|0.059***<br>(0.001)<br>-0.004***<br>(0.001)|0.068***<br>(0.002)<br>0.009***<br>(0.001)|0.060***<br>(0.002)<br>0.009***<br>(0.001)|0.069***<br>(0.002)<br>0.009***<br>(0.001)|0.061***<br>(0.002)<br>0.009***<br>(0.001)|
|Moratorium lifted|0.152***<br>(0.003)|0.153***<br>(0.003)|0.117***<br>(0.004)|0.118***<br>(0.004)|0.158***<br>(0.004)|0.158***<br>(0.004)|0.118***<br>(0.005)|0.119***<br>(0.005)|
|Legal entity (renter)|0.353***<br>(0.005)|0.333***<br>(0.005)|0.361***<br>(0.005)|0.341***<br>(0.005)|||||
|Legal entity * 2024|<br>0.081***<br>(0.011)|<br>0.085***<br>(0.011)|<br>0.071***<br>(0.011)|<br>0.075***<br>(0.011)|||||
|# transactions<br>in village|<br>0.037***<br>(0.001)|<br>0.034***<br>(0.001)|<br>0.033***<br>(0.001)|<br>0.030***<br>(0.001)|0.044***<br>(0.001)|0.042***<br>(0.001)|0.044***<br>(0.001)|0.042***<br>(0.001)|
|<br>Parcel part of field||<br>-0.055***||<br>-0.061***||<br>-0.044***||<br>-0.046***|
|||(0.005)||(0.005)||(0.007)||(0.007)|
|Field area (ha)||<br>0.014***||<br>0.018***||<br>0.016***||<br>0.018***|
|||(0.002)||(0.002)||(0.003)||(0.003)|
|Field area^2||<br>0.004***||<br>0.003***||<br>0.001**||<br>0.000|
|||(0.000)||(0.000)||(0.000)||(0.000)|
|Unmapped area||<br>-0.329***||<br>-0.321***||<br>-0.322***||<br>-0.313***|
|share in field||(0.012)||(0.013)||(0.016)||(0.016)|
|Lease term 14-20|||0.019***|<br>0.017***|||-0.053***|<br>-0.053***|
|years|||(0.003)|(0.003)|||(0.004)|(0.004)|
|Lease term > 20|||-0.159***|-0.163***|||-0.212***|-0.213***|
|years|||(0.003)|(0.003)|||(0.004)|(0.004)|
|Tenant's cult.|||||-0.052***|-0.052***|-0.037***|-0.038***|
|area (ha)|||||(0.003)|(0.003)|(0.003)|(0.003)|
|<br>Tenant's area^2|||||<br>0.012***|<br>0.012***|<br>0.011***|<br>0.011***|
||||||(0.000)|(0.000)|(0.000)|(0.000)|
|Tenant uses <33%|||||<br>-0.105***|<br>-0.102***|<br>-0.116***|<br>-0.113***|
|of village land|||||(0.006)|(0.006)|(0.006)|(0.006)|
|<br>Tenant uses 33-50%|||||<br>-0.112***|<br>-0.112***|<br>-0.110***|<br>-0.109***|
|of village land|||||(0.007)|(0.007)|(0.007)|(0.007)|
|<br>Tenant uses >50%|||||<br>-0.064***|<br>-0.067***|<br>-0.064***|<br>-0.066***|
|of village land|||||(0.007)|(0.007)|(0.007)|(0.007)|
|<br>No. of obs (farms)|648,292|648,292|603,648|603,648|<br>382,653|<br>382,653|<br>359,323|<br>359,323|
|Adj R-squared|0.338|0.341|0.344|0.347|0.388|0.390|0.398|0.399|

_Note:_ Parcel characteristics, year dummies, OTG fixed effects and a constant included but not reported (see appendix table 2 for estimated coefficients). Standard errors in parentheses (* p<0.10, ** p<0.05, *** p<0.010).

25
# Damaged by the Disaster: The Impact of COVID-19 on Firms in South Asia

Arlan Brucal<sup>∗</sup> Arti Grover<sup>†</sup> Santiago Reyes Ortega<sup>‡</sup>

**Acknowledgements** : The project was carried under the overall supervision of Denis Medvedev (Manager, ETIFE). The team benefited from discussions with several members of the ETIFE global team, particularly Leonardo Iacovone. Business pulse survey Implementation in South Asia was primarily facilitated by the IFC team including Rathnija Arandara, Anuj Chaudhary, Harsh Jhanjaria, Ananya Wahid Kader, Ashim Nepal, and Assadullah Nissar, while Rafay Khan implemented the survey from the World Bank in Pakistan. The implementation was guided by ETIFE team members including Marcio Cruz, Subika Farazi and Arti Grover.

**Disclaimer** : The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. It does not reflect the views of the Governments covered by this study. The findings of the study, thus, would not be binding on the countries covered by the study.

> ∗University of Exeter Business School E-mail: a.brucal@exeter.ac.uk

> †Corresponding author; World Bank, 1818 H Street, Washington DC, USA, E-mail: agrover1@worldbank.org

> ‡World Bank, 1818 H Street, Washington DC, USA, E-mail:sreyesortega@worldbank.org