rafmacalaba/data-use-mentions-extended huggingface dataset:
line per se (Kochhar 2015). In converting our health expenditure data to international dollars, and in deriving the IPLs, we convert values in different years to 2011 values using local CPIs and LCUs to international dollars using 2011 PPPs.

We checked the numbers underlying our estimates against published numbers. We checked our estimated consumption-per-capita estimates against the numbers in the World Bank’s PovcalNet when available and those in the World Development Indicators (WDI) when not; large discrepancies were flagged.<sup>6</sup> We checked our household health budget share against numbers from the WDI which are based on data from the WHO Global Health Expenditure Database; again, large discrepancies were flagged.<sup>7</sup> Last, we checked our $1.90-a-day poverty headcount estimates against the numbers in PovcalNet, flagging large discrepancies.<sup>8</sup> Estimates of catastrophic and impoverishing expenditure incidence were not automatically rejected when flags were raised, but our tendency was to prefer estimates where flags were not raised, and we typically only retained datapoints where the flag was only marginally up. This quality control process led to many datapoints being dropped, and some entire survey families.<sup>9</sup> In our database, many countries have series from multiple surveys or at least multiple adaptions of a survey. For any given year for any given country, we retained at most only one estimate of each measure, using the same survey for each
It should be noted that even if skills data from job vacancy postings is not best suited to identify green jobs, it brings valuable information to understand and design skills policy. Skills analysis from big data allows profiling of green jobs, including their skills requirements, and understanding other key features such as skills demand variation across industries and regions.

# **3. Using text analysis to develop an alternative methodology for the task-content approach**

This section introduces the text analysis methodology that identifies green occupations based on a green dictionary developed by the authors. The methodology falls under the task-content approach, which is our preferred approach for skills policy. It applies the green dictionary to a task database that is used for data collection in many countries, called the international standard classification of occupations (ISCO-08). One advantage of the methodology is that the green dictionary can also be easily applied to any other task database or adjusted to other country settings. This section also discusses the extent to which the methodology can be trusted by conducting two reliability tests. The first test compares the outcome of applying the methodology to the O*NET task database against green occupations listed in the O*NET Green Economy Program. The second test assumes that green skills are necessary to perform green tasks and checks the correlation between a green task index and a green skills intensity measure, both constructed by applying the same methodology to different databases.

## **A. Applying text analysis to the International Standard Classification of Occupations (ISCO) to produce the green task intensity index**

Classifying occupations according to their level of “greenness” consists of three steps: (1) creating a green dictionary, (2) applying it through text analysis techniques to an occupational database containing task statements, and (3) calculating the GTI index.

### Step 1: Development of the green dictionary

The first step was the development of a green dictionary, a database of terms that were used as the basis for the text analysis. The terms in
34 <u>Figure 10: Levels and inequalities in inpatient admission rates and pap smears compared</u>

# **Inpatient admission last 12 months**

# **Pap smear last 5 yrs, women age 30‐49**

<!-- Start of picture text --> 12% 90%<br>80%<br>10%<br>70%<br>8% 60%<br>50%<br>6%<br>40%<br>4% 30%<br>20%<br>2%<br>10%<br>0% 0%<br>High income countries Low income countries High income countries Low income countries<br>Q1 (poorest) Q2 Q3 Q4 Q5 (richest) Q1 (poorest) Q2 Q3 Q4 Q5 (richest)<br><!-- End of picture text -->

# **Financial protection data**

In this section, we report details of the financial protection part of the 2018 HEFPI data set, listing the indicators included, the reasons for including them, their sources and definitions, how they were computed, how we derived data for different subpopulations, our quality checks, and lastly some illustrations of the use of the data.

## <u>Indicators included</u>

The indicators in the financial protection part of the 2018 HEFPI database (cf. Figure 2) are: (i) the incidence of ‘catastrophic’ health expenditures (health expenditures exceeding a certain percentage, _x_ , of a household’s total consumption or income), and (ii) the incidence of ‘impoverishing’ health expenditures (expenditures without which the household would have been above the poverty line, but because of the expenditures is below the poverty line). Indicator (i) (catastrophic expenditures) is one of the two UHC SDGs (3.8.2). The catastrophic expenditure indicator tells us
), and we would expect visibility / salience to be negatively correlated with hesitancy as well.

Our second proxy for pandemic severity, in column (3), is the degree of stringency in the government’s policy measures, as captured by the Oxford policy stringency index. We expect ex-ante that a more stringent the policy response, signaling higher severity of the pandemic and more restrictions on movement, would be positively associated with willingness to be vaccinated.

Our third proxy for pandemic severity, in column (4), is the number of excess deaths associated with the COVID-19 pandemic, as reported by the WHO. The excess mortality measure quantifies the direct and indirect impacts of COVID-19, measured in terms of the difference between the total number of deaths for a specific place and time period and the number of deaths that would have been expected in the absence of COVID-19. This difference is inclusive of both deaths directly attributable to COVID-19 as well as deaths indirectly associated with COVID-19 through its impact on health systems and society. As a prior, we would expect higher levels of excess COVID-19 deaths to be positively correlated with willingness to get vaccinated, although, at the same time, higher hesitancy can also bring about more excess deaths.

12
We also estimate Probit models for every natural hazard separately. Figure 3 presents the average marginal effects of each natural hazard of these estimations. Positive marginal effects indicate a positive relationship between household poverty and the hazard of the event. In Brazil, earthquakes and droughts show positive effects, _ceteris paribus_ . In the Dominican Republic, the number of earthquakes and landslides' potential damage positively correlate to poverty. The number of landslides and the earthquake's potential damage in Ecuador positively correlate to poverty. The droughts and the number of landslides matter in El Salvador. In Honduras, mainly earthquakes are associated with household poverty. In Mexico, cyclones are the natural hazards associated with household poverty. In Peru, the number of landslides positively associates with poverty. Owing to the differences in natural hazard effects, below, when we present the results of vulnerability that come from the disasters separately models, we are going to present the results from the models that only include earthquakes in Honduras, cyclones in Mexico, floods in Peru, droughts in Brazil and El Salvador, and landslides in the Dominican Republic and Ecuador. Additionally, it is important to highlight that we do not count on the potential compounding effects of these hazards by incorporating their interactions into the models (for example, including the interaction between floods and landslides). This is an aspect that remains to be studied.

**Figure 3.** Average marginal effects from the models with natural hazards separately.

<!-- Start of picture text --> TH index Floods Number of Floods TH index Landslides Number of Landslides<br>TH index Earthquakes Number of Earthquakes TH index Cyclones Number of Cyclones<br>TH index Droughts Number of Droughts<br>B R AZIL M E XIC O PE R U DOM INIC AN E L HONDUR AS E C UADOR<br>R E P. SALVADOR<br>0.027<br>0.026***<br>0.024*<br>0.019<br
# **I. Introduction**

Micro-firms account for roughly 50% of employment in Latin America and substantially more in Africa and Southeast Asia. Raising their productivity therefore ranks high as a development goal and eliminating impediments to their dynamism as a central concern of policy. Using a very detailed micro-firm data set with a panel dimension from Mexico, this paper examines how micro firm performance is affected by several dimensions of their business environment: access to formal and informal credit, training programs, business associations or guilds, and government taxation and services. Following the convention of the literature on evaluation in quasi-experimental contexts, we conceive of engaging with these institutions as _treatments_ and attempt to measure their impact.

In our sample the share of treated firms is low: less than 10% of micro-firms have received credit or training services and less than 17% participate in industry or trade associations. This is often seen as evidence of supply bottlenecks that limit access to financing, human capital, and information. Releasing those constraints would permit firms to approach the steady state size dictated by their intrinsic entrepreneurial ability.<sup>1</sup> However, it may also be that for many firms, the steady state size is very small and thus there is, in fact, little demand for these treatments. For example, a sizable literature dating from Harris and Todaro (1970) argues that most informal micro-firms are run by individuals who did not enter the sector due to their higher entrepreneurial ability or other cost advantages, but rather are essentially queuing for entry into salaried jobs and are unlikely to be thinking of future growth. In sum, while in one approach increasing the supply of credit, training and information services would lead to improved business performance, in the other it would amount to pushing on a string and have little impact.

Empirically, estimating the magnitude of any treatment impact is not trivial. Both views imply self selection into the treatments considered which potentially imparts serious bias to naïve ordinary least squares (OLS) estimates. As
. The job destruction rate is endogenized by introducing idiosyncratic shocks to job productivities:<sup>28</sup> productivity shocks arrive to jobs in sector _i_ at Poisson rate _λ_ , changing the productivity to a new level _x_ . These new productivities are _iid_ draws from the conditional distribution _Hi_ ( _x | y_ ).<sup>29</sup>

The same mechanism that governs the job creation process applies to job destruction: a match ends when it is in the mutual interest of the worker and the firm to do so, i.e., when a sufficiently bad draw of _x_ is realized.<sup>30</sup> The threshold productivities for match dissolution in the informal and the formal sector are _RIU_ ( _y_ ) and _RFU_ ( _y_ ), respectively.

> 24I am assuming no congestion effects in the informal sector ( _α_ is not a function of _θ_ ), so the measure of job seekers does not make it harder for an individual to find an informal sector opportunity. It may be the case that while job seekers are eager to find a formal sector job, they are not eager to find an informal sector job.

> 25The matching function has standard properties, so _m_ ( _θ_ ) is increasing and concave in _θ_ . In the calibration I assume a Cobb Douglas matching function given by _m_ ( _θ_ ) = _Aθ_<sup>1</sup><sup>_−αm_</sup> .

> 26 _′ ′_ Sufficiently ‘high’ means _y ≥ RUF_ ( _y_ ) for the formal sector and _y ≥ RUI_ ( _y_ ) for the informal sector.

> 27Another way of modeling this choice is by assuming that workers choose whether or not to accept jobs based on a reservation wage. This is analogous to the reservation productivity concept.

> 28There are two reasons why the productivity of a job may fall below the reservation value: idiosyncratic or aggregate shocks. Previous evidence for Colombia using plant-level data from the Annual Manufacturing survey estimates
the formal sector in Harare undertakes these same activities. The city’s formal establishments tend to be involved in non-food retail. To test if these sector composition effects are driving the observed productivity differences, survey-weighted regressions accounting for these sector fixed effects were also run. OLS and quantile regression results confirm that on average, informal sector businesses are 88% or 86% less productive, respectively.<sup>13</sup>

**Table 1: Formal and Informal Sector Activities, Share**

||Formal|Informal|
|---|---|---|
|Manufacturing|18%|17%|
|Food provision|10%|50%|
|Retail|42%|17%|
|Other services|30%|16%|

Note: weighted estimates. Food provision includes retail sale of food, restaurants, bakeries, and beverage production

# _<u>Bribery in Zimbabwe</u>_

Using globally comparable indices, Zimbabwe fares poorly in terms of corruption. Transparency International’s Corruption Perceptions Index for 2017 ranked Zimbabwe 157<sup>th</sup> out of 180 countries, tied with Burundi, Haiti, and Uzbekistan. Further surveys by Transparency International, from 2015, report that roughly one in five respondents report encountering a bribe request when soliciting public services. According to the same Transparency International survey, nearly one in four respondents encounter a bribe request when dealing with police. What is more, Harare itself fares poorly within the country: the 2017 ES report that, at 19%, the rate of formal firms encountering at least one bribe request is the highest in the capital (World Bank, 2016).

A brief look at news articles from the first six months of 2017 provides anecdotal flavor to these data.<sup>14</sup> In the first half of the year, the economy was stagnant, facing widespread cash shortages, as a result of government-introduced bond notes pegged to the US
on arbitrary definitions of what constitutes productive investments. For example, schooling often is absent from the list of productive investments. This probably is because expenditures on educating family members usually do not create direct, immediate employment and income linkages within migrant-sending economies. Housing expenditures are not considered productive investments in many studies, despite their potentially important effects on family health and their direct stimulus to village construction activities. By contrast, expenditures on farm machinery generally are regarded as productive investments, in spite of the fact that machinery is not produced within the village economy and may even displace labor in village production and produce negative income linkages.

Reported use of remittances for productive investments at times can be significant. In their review of studies carried out in Mexico, for example, Durand and Massey (1992) found that the relative share of remittances spent on production, although always under 50 percent, fluctuated considerably from place to place and often reached substantial levels. Remittances enabled many communities to overcome capital constraints to finance public works projects such as parks, churches, schools, electrification, road construction, and sewers (Reichert, 1981; Massey et al., 1987; Goldring, 1990). Other studies report that remittances have been critical to the capitalization of migrant-owned businesses. Escobar and Martinez (1990), for example, found that 31 percent of migrants surveyed in Guadalajara used U.S. savings to set up a business. Massey et al. (1987), in their survey of the same city, put the figure at 21 percent; and in a survey of businesses located in three rural Mexican communities, Cornelius (1990) found that 61 percent were founded with U.S. earnings. A number of 5
>self-reported harvest<br>expectations|Attainable yield based<br>on farmers’ post-<br>planting expectations|How much of [CROP] do you expect to<br>harvest during the main rainy season?|Malawi: IHPS10,<br>IHPS13, IHPS16,<br>IHPS19<br>Ethiopia: ESS15,<br>ESS18|
|**Attribution**|Q5: Relying on farmers’<br>self reported shock<br>identification|Consistency of self-<br>reported shock exposure|Plot-level reports of losses cause;<br>Household-level shock reports; Community<br>reports|Malawi: IHPS10,<br>IHPS13, IHPS16,<br>IHPS19<br>Ethiopia: ESS13,<br>ESS15, ESS18|
||Q6: Index-based shock<br>identification|Consistency of self-<br>reports with geospatial<br>rainfall data|Geospatial/sensor data on rainfall intensity|Malawi: IHPS10,<br>IHPS13, IHPS16,<br>IHPS19<br>Ethiopia: ESS13,<br>ESS15, ESS18|

# **4.1. Quantification: How high are losses?**

## <u>Question 1: Can we separately measure planted area and plant productivity losses in survey data and form a robust, compound loss estimate?</u>

In the stylized framework set out in Section 3, losses can manifest in two complementary ways capturing different types of losses. The first are losses due to reductions in the size of the harvested 12
# **The Valuation of Flexible Work Arrangements: Insights from a Discrete Choice Experiment in Malaysia**

Yashodhan Ghorpade,<sup>1</sup> * Alyssa Jasmin* and Amanina Abdur Rahman*<sup>2</sup> Keywords: Flexibility, Flexible work arrangements, Gig work, JEL Codes: J30, J32, M52 The rise of flexible work arrangements, spurred globally since the COVID-19 pandemic, is a fundamental shift in the functioning of labor markets. Combined with the rise in non-standard forms of employment, including those in the ‘gig’ economy, different forms of flexibility in work (viz. the location, hours, and the duration of work) simultaneously represent an opportunity to improve work-life balance among workers as well as a threat of either reducing worker productivity or entrenching irregular work practices that can harm workers’ wellbeing.

Even before the COVID-19 pandemic, the growing prevalence of alternative work arrangements, in the context of increasing deregulation and liberalization of labor markets (particularly in Europe) spurred critical debates on their implications for worker protection and welfare, legal compliance, as well as for productivity. The evidence on these assertions has also been mixed. For example, Angelici and Profeta (2020), Barerro et al. (2021) and Choudhury et al (2021) found that more flexible work conditions enhance

> 1 Corresponding author: <u>yghorpade@worldbank.org</u>

> 2 *World Bank. The authors would like to thank En Luqman Ahmed (Government of Malaysia) and Mohd Redzuan Affandi and Muhammad Farhan Hizami (Malaysia Digital Economy Corporation) for their support for this study. The authors are grateful to Yasser el Gammal, Matthew Dornan, Piotr Lewandowski and Matteo Morgandi for helpful comments on earlier versions of this paper. Any remaining errors are our own.
Policy Research Working Paper 5483

# **Abstract**

This paper estimates the relationship between initial village inequality and subsequent household income growth for a large sample of households in rural China. Using a rich longitudinal survey spanning the years 1987–2002, and controlling for an array of household and village characteristics, the paper finds that households located in higher inequality villages experienced significantly lower income growth through the 1990s. However, local inequality’s predictive power and effects are significantly diminished by the end of the sample. The paper exploits several advantages of the householdlevel data to explore hypotheses that shed light on the channels by which inequality affects growth. Biases due to aggregation and heterogeneity of returns to ownresources, previously suggested as candidate explanations for the relationship, are both ruled out. Instead, the evidence points to unobserved village institutions at the time of economic reforms that were associated with household access to higher income activities as the source of the link between inequality and growth. The empirical analysis addresses a number of pertinent econometric issues including measurement error and attrition, but underscores others that are likely to be intractable for all investigations of the inequality-growth relationship.

This paper—a product of the Human Development and Public Services Team, Development Research Group—is part of a larger effort in the department to understand the relationship between inequality and economic performance. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at jgiles@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
COVID-19 lockdown measures disproportionately impacted the school attendance probabilities of children based on gender. Results of these estimations are reported in Table 3 reports the results of this estimation. First, when we consider the entire sample of children (Table 3, Panel A), we find that neither gender was spared by the socioeconomic impact of COVID-19’s lockdown measures. These measures reduce the school attendance of and this more probabilities boys girls. Although negative impact appears slightly substantial in magnitude for boys than for girls, the resulting gender difference, however, is not statistically significant. This implies that over the whole sample, COVID-19’s lockdown measures have no statistically significant effect on the gender gap in school attendance.

When we partition our sample by gender and by age group, we find that the above conclusion holds for all age groups, including the age groups 5 ́ 11 corresponding to ́ primary school age (Panel B), 12 18 corresponding to secondary school or higher (Panel C), and children aged 15 ́ 18—whose schooling is no longer compulsory in Nigeria— (Panel D). This result suggests that COVID-19’s lockdown measures have no statistically significant effect on gender inequality in school attendance in Nigeria.

We further explore the joint heterogeneity of gender and geopolitical zones. Given the non-representativeness of the COVID-19 NLPS data at the zonal level, we explore the gender effect by interacting COVID-19’s lockdown measures with the child’s gender and her geopolitical zone. We find that COVID-19 shock significantly increases gender inequalities in school attendance in the North-West and South-West zones (Table 4, Panel A). Surprisingly, in South-West Nigeria, we find that COVID-19’s lockdown measures reduce the gender gap in school attendance by roughly 4.32 percentage points. Specifically, the COVID-19’s lockdown measure reduces school attendance for girls living in the South West zone by 3.28 percentage points, while the reduction is 7.61 percentage points for boys living in the same zone. In contrast, in North-West Nigeria