rafmacalaba/datause-encoder-data huggingface dataset:
Figure 8: Temporary employment by gender. (Mid-1990s/Mid-2010s) <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s<br>Argentina Brazil Chile Mexico El Salvador<br>Female Male<br><!-- End of picture text --> Source: Own calculations based on Household surveys

# 4.1.2 Profile of Non-Standard Employment

The next objective of this work is to investigate possible changes in the profile of non-standard employment, usually associated with lower productivity and greater vulnerability. The analysis of the employment profile was made based on access to social security benefits, education level, labor income and the task content performed by the workers.

From the point of view of social security, we find that NSE shows a higher prevalence of informality compared with SE. For instance, the average prevalence of informality for our set of countries among NSE in the ending point of the study is 40% while the prevalence among SE is 20%. In this sense, a rise in the prevalence of NSE could be associated to a big set of workers without access to social security benefits.

From a dynamic perspective, there is no a common trend across countries regarding the prevalence of informality among NSE workers (Figure 9). Indeed, several countries (Uruguay, Brazil, Chile, and Peru) registered a small decrease in the prevalence of informality among NSE but there is another set of countries for which the opposite is observed (Mexico, El Salvador, Bolivia and Argentina).

It is important to note that the changes in the prevalence of informality among SE workers between the starting and ending point of the study show a very similar dynamic compared with the observed dynamic for NSE (the prevalence of informality among SE workers is presented in the Annex II of the paper). Then, the trend in the prevalence of informality among NSE mainly reflects the overall trend of informality in the labor market instead of a specific characteristic of NSE.

Figure 9: Prevalence of informality among NSE workers. (Mid-1990s/Mid-2010s) Page 13 of 51
ANNEX 2. Common Psychological Intervention Curricula Table 8. Comparison of Common Psychological Intervention Curricula

|**Intervention**|**Standards Themes**|**Structure and Delivery**|
|---|---|---|
|Cognitive Behavior<br>Therapy (CBT)|1. Recognizing automatic thoughts<br>(understanding and identifying thoughts/<br>beliefs and subsequent reactions; thinking<br>report)<br>2. Thinking of new ways to respond (slowing<br>down automatic thought, alternative<br>responses, responding to conflict, rational<br>self‐analysis).<br>3. Social mobilization (community connections,<br>how actions affect others, decision‐making<br>scenarios)<br>(CBT 2.0 Curriculum,Ideas42 and Crime Lab)|Group or individual; repeated long‐term<br>session (3‐4 months), also utilizing feedback<br>and homework; delivered in‐person, in groups,<br>over the phone, internet, videos, and so on;<br>mostly delivered by trained facilitators with<br>varying levels of expertise.|
|Mindfulness‐based<br>cognitive therapy<br>(MBCT)|1. Awareness and automatic pilot<br>2. Living in our heads<br>3. Gathering the scattered mind<br>4. Recognizing aversion<br>5. Allowing/letting be<br>6. Thoughts are not facts<br>7. Taking care of self<br>8. Maintaining and extending new learning<br>(Segal,Williams and Teasdale 2013)|Eight weekly group sessions of approximately 2<br>hours each, plus a 6‐hour practice session;<br>individual daily homework in between sessions;<br>can be delivered online or through trained<br>facilitators.|
|Rational Emotive<br>Behavior Therapy<br>(REBT)|1. Activating event<br>2. Beliefs (rational and irrational)<br>3. Consequences<br>4. Disputing irrational thoughts/feelings<br>5. Effective adaptation<br>(David and others 2005)|Adaptable: individuals, couples, families or<br>groups; facilitated by trained therapists; a<br>higher number of sessions is better.|
|Problem<br>Management Plus<br>(PM+)|1. Managing stress<br>2. Managing problems<br>3. Get going, keep doing<br>4. Strengthening social support<br>#All based on CBT core principles<br>(WHO 2016)|Five weekly group of individual sessions of 90<br>minutes and 3 hours each, respectively; can be<br>adapted for groups; delivered by trained, lay‐<br>persons, that is, non‐mental health specialists.|
|Personal Initiative<br>Training (PIT)|1. Self‐starting behavior<br>2. Innovation, identification and exploitation of<br>opportunities<br>3. Goal‐setting<br>4. Access to finance<br>5. Planning<br>6. Feedback<br>7. Overcoming obstacles<br>8. Individual project<br>(Campos and others 2018)|Twelve half‐day group sessions across four<br>weeks; 36 hours of instruction; group and<br>individual components; individual component<br>includes in‐person mentoring for four months;<br>and three‐hour long business visits by trainers.|

Source: Adapted from Saraf and others (2018) 26
Compliance increased overall by 3.5%. Among visited properties, it increased 3.4% and we estimate that it would increase 3.8% in the non-visited properties. In comparison to the baseline values (Table 3), the program had the potential to increase overall compliance by between 5.1% and 5.8%; it increased the compliance of the visited properties by between 1.2% and 8.6% and could have increased the average compliance among the control group to 58.9%, where we expect the program to be responsible for between 3.7% and 9.1% of the total compliance.

The impact on the logarithm of total payments was observed at 0.422, with visited units experiencing an effect of 0.377, and non-visited units seeing a higher impact of 0.492. These outcomes suggest an average uplift in payment values ranging from 31.9% to 76.3%. Notice that this result is mainly due to the ATU. If we consider only the group of non-visited units, there are larger potential gains, between 46.9% and 82%. Due to the presence of outliers, we do not see any statistically significant effect when analyzing just the value paid.

**Result 3** _The effect of the door-to-door visits in 2022 compared to 2021: (i) Are on average higher; (ii) Have narrower confidence intervals; (iii) Have lower difference between ATT and ATU._

First notice that as the partial payments virtually disappear from our dataset in 2022, then _Y_<sup>_Co_</sup> converges to _Y_<sup>_Pa_</sup> , therefore _τ_<sup>_Co_</sup> converges to _τ_<sup>_Pa_</sup> . Overall the intervention increased Tax Participation/Compliance by 6.3% in 2022, that is between 5.4% and 11.1% of the overall compliance. These results are higher than what we found for 2021.

Results 3 are in line with the literature on the economics of tax compliance that consistently finds time persistence in the effects of interventions. However, as time passes those results lose strength. We do not have an explanation for the increase in the effect, but we speculate that it may be due to the economic effects of the COVID-19 pandemic that lost strength in 2022. Therefore, it is also plausible to imagine that the results we find underestimate the potential gains in regular years.

The difference between ATT and ATU is lower in 2022. However, when we compare the re19
- 40 -

The previous analysis revealed several anomalies that could indicate Caribbean ocean freight rates are higher than might be expected on a priori grounds. First, Caribbean countries were found to encounter generally favorable (and in many cases sizeable) cost differentials for air transport, but no similar cost advantages were found in ocean freight rates. These results could be due to natural differences in the relative efficiency of airsea transport systems or by discriminatory pricing practices of liners. Second, the fact that the previous analyses did not show Caribbean countries have a major cost advantage in ocean transport vis-a-vis other exporters is somewhat surprising given these nations proximity to the United States market. Empirical studies of the determinants of ocean freight rates have found that distance is normally an important explanatory factor for transport cost differentials among countries (see Moneta 1959 or Bryan 1974).

If other factors are held constant (i.e., variations in port facilities, the volume of exports, qualities and types of vessels used, etc.) an important question centers on what cost advantage should distance provide the Caribbean countries with over other exporters. While the results must be qualified, some useful insights into this question can be gained from a study sponsored by the U.S. National Bureau of Research (Lipsey and Weiss 1974) that derived a transport cost estimation equation for United States imports. The NBER study was based on the premise that U.S. import freight rates (FR) expressed in dollars per ton can be largely explained by the following equation, where all variables are in logarithimic form, 4

- (5) FR = 3.53 + 0.52UV + 0.30DI + 0.35ST + 0.30SW - 0.51TA 2= 0.81 (57.35) (13.51) (18.18) (6.22) (12.25)
over night, but to get started a wide range of reforms are needed. Key policy steps, in our view, are as follows.

First, governments must for an extended period of time pursue a macroeconomic policy that is both sound and credible to domestic and international investors. The whole issue of governance is central, because if governance is poor, uncertainty will persist and investors’ response to other initiatives will be weak. We know from the firm data that uncertainty is a major factor in explaining why investment among African manufacturing firms has been low over the last decade. Reducing the uncertainty related to movements in the exchange rate and pursuing credible export programs is of course important to promote exports. Overall, the government needs to string together several years of good governance and good policies. This can be hard in a situation where people do not see quick results, and resisting policy reversals and populist policies will therefore be a challenge.

Second, scarce resources – land, skills, physical capital, raw material - must be channeled towards firms that are productive and in good position to export. One important issue is what to do with the now very large informal manufacturing sector in Africa. Two types of policy measures are conceivable here. The first involves designing policies purporting to encourage informal firms to grow and become formal. From the firm data we have some indications of how policy makers can best facilitate a transition process. Based on Kenyan firm data, Bigsten, Kimuyu and Lundvall (2004) argue that the informal sector is large primarily because being formal is costly. Formal firms in Kenya have experienced big problems in dealing with authorities relating to regulatory red tape, corruption, and lack of security. Informal firms, by and large, sidestep these problems, and so incur lower costs. Thus, addressing the basic governance and investment climate issues would be a step in the right direction as far as trying to encourage entrepreneurs to operate within the realm of the formal sector instead of outside it. The second type of policy is oriented towards facilitating good performance by informal firms within their current informal environment. Importantly, we know from the firm data that some of the key economic fundamentals, e.g. total factor productivity, in informal firms are relatively good (Bigsten, Kimuyu and Lundvall, 2004). However, capacity-building efforts have so far largely neglected the needs of informal firms, while formal firms have been the primary beneficiary of policies in areas such as vocational training. One likely welfare loss stems from the lack of access to credit, and hence foregone investment, for 36
To investigate what is the impact of these services on nutrition, we use data from the 1997 round of the Peru Living Standards Measurement Study. This survey was a multipurpose, modular, living standards survey following a format utilized in over twenty countries (Grosh and Glewwe, 2000). While many of the variables such as infrastructure and total expenditures are based on household data, the analysis is performed on individual observations of child heights for children sixty months of age or less.<sup>4</sup> The total sample is 2,154 children below the age of five were recorded in the survey, more or less equally divided by gender and among urban and rural populations (see appendix table 1).

The measure of nutritional status used in the study is height for age. This is considered a measure of long-run nutritional deprivation, (Alderman, 2000; WHO, 1995). This measure is converted into standardized units (Z scores) after comparison with the US data chosen as an international reference by the WHO. The Z scores are derived after subtracting the age- and gender-specific means from the reference data and after dividing by the corresponding standard deviation. In the reference population 2.3 percent have a Z score below -2, while 16.0 percent are below -1 Z score. These levels might be expected for a normal population, and provide a basis for comparison.

Commonly, rates of malnutrition in a population are based on the percentage of children below the age of 5 whose z-score is two standard deviations below the reference value. This is the measure of malnutrition reported in table 1. However, as there is no sharp difference in risk of mortality or functional impairment at this or any other commonly used cutoff level (Pelletier, 1994), the regressions reported below focus on nutritional status itself and not the probability of malnutrition defined in terms of a Z score below -2. That is, the dependent variable is the Z score for height for age.

Of central importance to this paper is the definition of the neighborhood variables. In all cases we derive the value of the variables from non-self means. These are derived by summing the variable of interest over the sample cluster and then subtracting the observation for the household and dividing this difference by the number of households in 4. See Annex 1 of World Bank (1999) for a detailed description of the household survey and the method of aggregation and deflating expenditures to obtain a consistent welfare indicator across households.

6
Table 6. Program impact on citizens’information.

|Dependent<br>variable|Health<br>information|Importance of<br>family planning|
|---|---|---|
|Speci...cation|(1)|(2)|
|Program impact|0.09***<br>(0.02)|0.07***<br>(0.02)|
|District ...xed e¤ects|Yes|Yes|
|Observations|4996|4996|
|R<sup>2</sup>|0.16|0.10|

a. *** [**] (*) denote signi...cance at the 1 [5] (10) percent level. b. Dependent variable in speci...cations: (1) Dummy variable indicating if the household receives information about the importance of visiting the health facility and the danger of self-treatment, (2) Dummy variable indicating if the household receives information about family planning. c. Robust standard errors in parenthesis. Disturbance terms are clustered within catchment areas.

51
weights for the IHPS sample that is used for analysis. The model controls for a range of individualand household-level demographic and socioeconomic attributes that predict response (and that are also potentially associated with land ownership).<sup>19</sup> Table 1 shows a within-household success rate breaking down the number of eligible adults versus the number of individual interviews completed. Across all households, regardless of the number of adults, all eligible adults were successfully interviewed 68 percent of the time. The remaining 32 percent of households had more than one adult but failed to interview at least one of them. Given that the household head or their spouse is most likely to be the household member owning or managing assets listed by a household, part of the analysis outlined in Section 4 has a focus on members of the principal couple. Of the 2,477 households included in the individual household sample, 72 percent had a principal couple, and in 75 percent of these cases enumerators managed to interview both the husband and spouse.

**Table 1. Distribution of IHPS Households According to Number of Adults Interviewed**

||**Pan**<br>|**el**<br>|
|---|---|---|
||**Total**|**%**|
|**Households Interviewed**|**2477**||
|**All Eligible Adults Interviewed**|**1675**|**68%**|
|4 adults|115|5%|
|3 adults|225|9%|
|2 adults|1003|40%|
|1 adult|332|13%|
|**Subset of Eligible Adults Interviewed**|**802**|**32%**|
|3 out of 4|106|4%|
|2 out of 4|92|4%|
|1 out of 4|29|1%|
|2 out of 3|167|7%|
|1 out of 3|65|3%|
|1 out of 2|343|14%|
|**Average # of Adults Interviewed**|**1.8**|**9**|

We calculate weights to correct for non-response in the IHPS by running a logistic regression of individual response status among adults eligible for individual interviews. The results from the logistic regression are presented in the Appendix Table A1.<sup>20</sup> Subsequently, we (1) take the

> 19 Among the eligible men who did not respond, 44 percent were heads of household and 35 percent were children of the household head, and among the non-responding women, only 6 percent were household heads, 32 percent were spouses of the head, and 37 percent were children.

> 20 Van den Broeck and Kilic (2019) use a similar approach to correct for attrition bias in panel data samples, in a study of labor market dynamics in Sub-Saharan Africa. Our right-hand-side predictors of “response” include (i) fixed effects for districts, interview months and enumerators, (ii) individual covariates, including age; a dichotomous variable identifying females; a series of dichotomous variables on educational attainment; dichotomous variables identifying whether the individual is currently married, and separately, whether he/she is head/spouse of head; and individual’s

10
Policy Research Working Paper 9429

# **Abstract**

The World Health Organization attributes about 3.3 million annual premature deaths to outdoor air pollution in low- and middle-income countries. Comprehensive pollution monitoring in urban areas has been too costly for many developing countries; yet sparse information has hindered cost-effective pollution management strategies. Global information technologies offer a potential escape from this information trap, but their accuracy remains uncertain. This paper uses ground-based measures of fine particulates and nitrogen dioxide, provided by the CAMS-3 Darussalam monitoring station in Dhaka, Bangladesh, to test three global technologies: the European Space Agency’s Sentinel-5P, the National Aeronautics and Space Administration’s Moderate Resolution Imaging Spectroradiometer, and Google Traffic. The results indicate that all three global technologies can provide useful information for extension of air pollution measurement beyond the few areas that are currently monitored by ground stations. Each technology tracks ground-based fine particulates measures with high significance, and the European Space Agency’s Sentinel-5P and Google Traffic perform similarly for ground-based nitrogen dioxide measures. Google Traffic can provide accurate tracking at higher spatial and temporal resolution than the satellite sources, but only for emissions from motor vehicles in major metro areas. The Moderate Resolution Imaging Spectroradiometer and the European Space Agency’s Sentinel-5P capture the effects of emissions from other sources at all locations.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sdasgupta@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. 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._ Produced by the Research Support Team
pandemic that lingers for years to come. Consequently, there may be a need for government support to expand opportunities for workers to join formal firms. Third, firm-specific factors, more than structural factors, appear to be associated with whether firms experienced a recovery in employment. This implies that there may be further scope for some firms to fully return their number of employees to pre-pandemic levels by taking actions within their control.

This paper makes two contributions to the existing literature on the impact of COVID-19 on formal firms. First, we draw on two distinct but complementary data sources to track how firms were impacted by and have recovered from the pandemic. Previous studies have either relied on surveys of firms (e.g., Davies et al. 2021; Apedo-Amah et al. 2020; Cirera et al. 2021; Karalashvili and Viganola 2021) or tax administrative data (Angelov and Waldenström 2021; Bachas et al. 2020, 2021; Mascagni and Lees 2022). To the best of our knowledge, this is the first effort to combine these data sources, which allows us to present a far richer picture of how COVID-19 has impacted firms. Extensive efforts are made to identify whether differences (and/or similarities) in the findings across survey and tax administrative data can partly be explained by differences between the samples of firms.

Second, this is one of the first in-depth studies to specifically focus on the recovery of formal firms from the pandemic in a Sub-Saharan African country. The recovery in the region is likely to be distinct from what has occurred elsewhere in the world for several reasons (Aga and Maemir 2021). First, in general, firms in Sub-Saharan Africa received very low levels of government support and it has been argued that this led firms to be more likely to lower wages, lay off workers, and face bankruptcy (Davies et al. 2021). Second, at the outset of the pandemic, the operations of firms in the region relied heavily on face-to-face interactions, which were greatly disrupted by COVID-19 lock downs (Bachas et al. 2021; Davies et al. 2021). Lastly, small firms make up the bulk of economic activity in Sub-Saharan African countries, and due to having more limited financial, technological, and human resources, alongside higher dependence on supply chains, were more likely to fare worse than larger firms (Bachas et al. 2021; Davies et al. 2021; Muzi et al 2021).

3