rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10395

# **Abstract**

This paper takes stock of the insights and learnings from a COVID-19 emergency cash transfer program that was administered to vulnerable informal sector workers in Sierra Leone. It starts by reviewing relevant examples of cash transfer programs that were instituted in response to the COVID-19 crisis. It then describes the context, intervention, and data of the emergency cash transfer program, before presenting a quasi-experimental analysis of the emergency cash transfer’s potential impacts on various measures of economic security and subjective well-being of households with urban informal sector workers. The analysis is conducted by matching administrative data to survey data and using program eligibility criteria and inverse probability weights to identify the short- and medium-term relationship between a one-off US$135 cash transfer and various labor market, food security, human capital, and subjective well-being outcomes for recipient and nonrecipient households of the emergency cash transfer. The analysis finds a positive potential impact of the transfer and the number of hours worked as well as employment in the medium term. It also finds that program beneficiaries report higher chances of their main income increasing or staying the same compared to nonbeneficiaries. The positive correlation between the transfer and income disappears over the medium term, perhaps suggesting that one-off transfers work best to cushion vulnerable self-employed households and informal wage workers in the short term but do not impact medium-term employment or income.

This paper is a product of the Social Protection and Jobs Global Practice. 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 sadhikari2@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.
.036|
||||Panel D: DV is a (0,1|) "good performanc|e" indicator from p|rivate post-job survey|||
|(0,1) indicator that|-0.032*|-0.032*|-0.015|-0.054*|-0.036|0.003|0.014|-0.045**|
|worker is in India|(0.018)|(0.018)|(0.021)|(0.029)|(0.023)|(0.019)|(0.020)|(0.020)|
|Observations|5,900|5,900|3,619|1,244|5,900|3,338|2,879|4,803|
|Mean of DV|0.677|0.677|0.665|0.700|0.677|0.700|0.700|0.641|
|Relative effect|-0.047|-0.047|-0.023|-0.077|-0.053|0.004|0.020|-0.070|

Notes: Contract-level regressions estimate wage and performance effects from ethnicity-based contracts using variation among ethnic Indian company contacts located outside of India. Regressions are unweighted, include fixed effects for year x job type x country of company contact, and report standard errors that are two-way clustered by originating company and worker. Fixed effect regressions bootstrap standard errors. Performance observation counts are lower due to ongoing jobs (99% of cases) or missing values. Worker controls are those listed in Table 7. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
Policy Research Working Paper 9847

# **Abstract**

This paper uses machine learning methods to identify key predictors of teacher effectiveness, proxied by student learning gains linked to a teacher over an academic year. Conditional inference forests and the least absolute shrinkage and selection operator are applied to matched student-teacher data for math and Kiswahili from grades 2 and 3 in 392 schools across Tanzania. These two machine learning methods produce consistent results and outperform standard ordinary least squares in out-of-sample prediction by 14–24 percent. As in previous research, commonly used teacher covariates like teacher gender, education, experience, and so forth are not good predictors of teacher effectiveness. Instead, teacher practice (what teachers do, measured through classroom observations and student surveys) and _teacher beliefs_ (measured through teacher surveys) emerge as much more important. Overall, teacher covariates are stronger predictors of teacher effectiveness in math than in Kiswahili. Teacher beliefs that they can help disadvantaged and struggling students learn (for math) and they have good relationships within schools (for Kiswahili), _teacher practice_ of providing written feedback and reviewing key concepts at the end of class (for math), and spending extra time with struggling students (for Kiswahili) are highly predictive of teacher effectiveness. As is _teacher preparation_ on how to teach foundational topics (for both Math and Kiswahili). These results demonstrate the need to pay more systematic attention to teacher preparation, practice, and beliefs in teacher research and policy.

This paper is a product of the Development Research Group, Development Economics and the Education Global Practice. 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 ssabarwal@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues.
supplied to final customers, and the service area). Table 1 lists summary statistics of the data.

**Table 1: Summary statistics**

|Inputs an<br>(Number of obs|d Outputs<br>ervations = 470)|Mean|Standard<br>deviation|Maximum|Minimum|
|---|---|---|---|---|---|
|Number of<br>Sales (i|employees<br>n GWh)|1953<br>6883|5209<br>20383|41063<br>175498|88<br>58|
|Number of<br>|customers<br>|868194|2296233|19760000|21368|
|Service ar|ea (in km<sup>2</sup>)|109613|275024|1889910|32|
|Distribution|lines (in km)|27778|78770|595170|105|
|Private fir|ms dummy|0.44|0.50|1|0|
|O&M expe|nses (PPP)|157881|259300|1833086|695|
|(Number of obs|ervations = 190)|||||
||**Sample distrib**|**ution of firms b**|**y country and own**|**ership category**||
||Number of<br>firms|Private firms|Firms changing<br>ownership|Number of<br>observations|Observations<br>corresponding<br>toprivate firms|
|Argentina|22|12|5|117|70|
|<br>Bolivia|2|1|0|12|7|
|Brazil|37|16|0|113|44|
|Chile|2|2|0|14|14|
|Colombia|5|1|0|17|2
functional difficulties (e.g. difficulty seeing), activity restrictions (e.g. difficulty feeding oneself) and participation restrictions (e.g. restricted in socializing). This paper considers disability as an interactional and biopsychosocial phenomenon, consistent with the ICF and other interactional models (Goodley 2018, Mitra 2018).

Besides varying underlying measurement objectives and conceptual definitions, there are different ways to collect disability data. These are “self-perceived and observed” data (Murray and Chen 1992). Self-perceived measures give an individual’s own perception of limitations, while observed measures rely on an external party’s assessment. Both types of measures provide complementary and valuable information (Murray and Chen 1992). There is typically no observed data in surveys and censuses in low- and middle-income countries. Thus, self-reported measures are used in this study and are therefore the focus of this paper.

# **3. Disability questions in household surveys and censuses**

As can be anticipated, there are different ways to measure disability through self-reports (Mitra 2018; Palmer and Harley 2012). We describe below the different types of questions that have been commonly used in surveys and censuses: functional difficulties, activities of daily living, broad activity limitations, general disability/impairment questions and other questions.

# **Functional difficulties**

Functional difficulties refer to difficulties experienced with particular bodily functions such as seeing and hearing. The term is used to also include basic activities such as walking and daily activities such as feeding oneself. The United Nations Statistical Commission (United Nations 2015, 2017) adopted revised guidelines for the collection of disability data in national censuses. "It is suggested that only those domains that have satisfied a set of selection criteria be eligible for inclusion in a short set of questions recommended for use in censuses. Criteria for inclusion include cross-population or cross-cultural comparability, suitability for self-reporting and space on the census form. Other suggested criteria include the importance of the domain in terms of public health problems” (United
.<sup>21</sup> The result of this approach is compared with a multilateral price index calculated by a GEKS-Fisher index method. Of particular interest in this comparison is how handling missing values by the CPD approach affects the spatial price measures.

## _Non-food prices_

As discussed in Section 2.2, the treatment of non-food items, particularly housing, is a vital issue in the calculation of spatial price indexes. The combination of using the CPD method and the CPI price data allows us to include many non-food items without losing quality information in the price data. In the approach, we compare the spatial patterns of (a) the food price index and (b) the food plus nonfood price index. In addition, we test the sensitivity of price measures to the omission of quality information (for both food and non-food items) by comparing the results with or without variety-level quality information in price observations.

For housing, we examine different approaches in the following aspects. First, we explore the influence of controlling for quality differences in housing across regions. We compare the results of spatial price measures based on the median values (that is, no control of quality) or hedonic regression (that is, with control of quality). Second, we also compare the results based on a price index that uses actually paid rents (that is, only rental units) and imputed rents (that is, owner-occupied and/or rent-free units included). The combination of these two aspects results in the following four housing approaches:

- _Median paid rent_ approach takes a median value of paid rents in each region.

- _Median imputed rent_ approach takes a median value of paid and imputed rents in each region.

- • _Hedonic paid rent_ approach estimates a hedonic regression model on paid rents in which coefficients for the regional dummy indicate housing price levels.

> 21 Results based on fixed weights are available upon request.

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same sector decline by 5.7 percentage points.<sup>2</sup> Last, but not least, comparing growth in hearing aid trade – which is entirely 3D printed – with other similar products and controlling for a range of other relevant variables that might have changed during this period, Freund, Mulabdic and Ruta (2019) find that 3D printing increased trade by 58 percent over nearly a decade. They also indicate that there is no reversal in comparative advantage and early innovators in Europe, such as Denmark and Switzerland, remain the main export platforms.<sup>3</sup> This paper contributes to this evidence base by analyzing the association between the intensity of robot use in high-income countries and offshoring as manifested in greenfield foreign direct investment flows from high-income countries to low- and middle-income countries. Unlike trade flows and other investments, which can be sticky and slow to change in response to other factors, these greenfield FDI data represent announcements and are therefore forward-looking. The intensity in use of robots has been steadily increasing in high-income countries over the last two decades, albeit differently across manufacturing industries. Among key GVC sectors, whereas transportation equipment and electronics have been steadily robotized since the 2000s, apparel remains relatively labor intensive. The rate of adoption across countries is also different, providing important sector-country-time variation. Based on country-sector panel data on FDI and industrial robots between 2004 and 2015, we find a 10 percent increase in the number of robots per 1,000 employees in HICs is associated with a 5.5 percent increase in the rate of change of the FDI stock from HICs to LMICs.

However, the relationship is not a linear one. Indeed, allowing for the effect to vary with the extent of automation finds there is a significant non-linear effect. The linear effect remains positive and significant. Past a threshold, however, the continued robotization is associated with a statistically significant decline in the rate of change in outbound FDI from HICs to LMICs. At the sample mean of robots per
# **Appendix A: Description of the Future of Business Survey**

The Future of Business survey (FoB) is a collaboration between Facebook, the Organization for Economic Co-operation and Development (OECD), and the World Bank to survey the 150 million businesses on Facebook.<sup>1</sup> This initiative began in 2016 to leverage the Facebook platform to administer surveys to micro, small and medium-sized businesses (MSMEs) globally in a timely and cost-effective manner.

In May 2020, the Future of Business survey adapted its standard bi-annual approach to run six monthly waves of the Future of Business survey to provide timely insights on the impact of the COVID-19 pandemic on small businesses and to take the pulse of the needs and challenges of these businesses. The COVID-19 monthly waves sample from 50 countries around the world where the Facebook small business penetration is sufficient to achieve a meaningful sample.<sup>2</sup> The first of these monthly surveys (Wave I) from 28-31 May 2020 (Wave 2 from 24-30 June, Wave 3 from 23-31 July, Wave 4 from 24-31 August, Wave 5 from 23-30 September, and Wave 6 from 23-31 October) surveyed around 26,000 small businesses. Survey wave 7 was conducted a year later from 15-30 September, 2021. The monthly survey waves are a repeated cross-section of approximately 25,000 businesses sampled across 50 countries from the target population. The collaborators developed a brief enterprise survey instrument to collect data measuring the impact of the COVID-19 pandemic on business owners. The questionnaire is designed to measure the impact on some critical dimensions: operation of the business, revenues, employee adjustments, firms’ expectations and uncertainty about the future, and preferred mechanisms of public support. A number of gender-specific questions around time spent on domestic and care responsibilities were also included.

The target population of the Future of Business Survey are Facebook Page administrators that are identified as business owners and managers. There are over 80 million businesses estimated to be represented in this sampling frame (
sources generating pollution (e.g. less wood-smoke from heating) to be trapped by inversions that occur during the summer. Regardless, we show that omitting the summer inversions does not appear to impact the predictive ability of winter inversions (Table 2, Columns 2-3).

One might be concerned that the predictive power of our inversions instrument is due to some artefact of the construction of our measures of pollution. These are based on satellite measurements but also include inputs from dispersion modeling. To alleviate this concern we conduct the following robustness check. For 21 cities around India we download data on daily pollution levels from the Indian Central Pollution Control Board from 2015 to 2020. This gives us an unbalanced panel of pollution (due to missing data) at the daily level across these cities. We then use our weather data to construct daily measures of inversions and ground level weather controls for each of these cities. We test whether our measure of inversions successfully predicts higher levels of pollution in these cities using this pollution dataset that is unrelated to our primary pollution data. We find that a day with an inversion leads to PM2 _._ 5 levels that are roughly 5% ( _t-stat:_ 1.91) higher relative to the mean. This test, using daily data distinct from our primary pollution data suggests that the inversion instrument is both a good predictor of higher pollution levels and that this predictive power comes from true impacts in the real world rather than because of any feature of the pollution data construction.

# **5.2 Second stage**

Our results indicate that higher levels of pollution result in smaller year-to-year changes in GDP. We find, instrumenting for pollution levels with the number of inversions during the fiscal year, a 1 _μg/m_<sup>3</sup> year-to-year increase in PM2 _._ 5 pollution reduces the year-to-year change in GDP by 0.7 percentage points (Table 3). In our sample the median year-to-year change in pollution is 0.8 _μg/
|(0.33)|(1.00)|(0.21)|(0.01)|(0.02)|
|Food consumption|1.22***|0.55***|0.10**|0.02***|0.02***|
||(0.13)|(0.13)|(0.04)|(0.00)|(0.00)|

Sources: « Pauvreté et Structure Familiale » (PSF), authors' calculations. Note : Test of differences between inequality indices computed using per capital household consumption and per capita cell consumption, presented in Table 3. N=1762, Bootstrap standard errors (250 replications). *,**,***: significant at the 10%, %, 1% level.

**Table S2.2: Differences between poverty indices from table 6**

||Nutrition p<br>Headcount<br>index|overty line<br>Poverty gap<br>index|Basic needs<br>Headcount<br>index|poverty line<br>Poverty gap<br>index|
|---|---|---|---|---|
|National|1.56***|0.88***|3.51***|1.78***|
||(0.45)|(0.12)|(0.64)|(0.15)|
|Dakar|0.29|0.16***|3.56***|1.63***|
||(0.39)|(0.06)|(1.14)|(0.22)|
|Other urban areas|2.07**|0.
As we discussed in the previous section and was summarised by the Kakwani coefficients, most fiscal interventions in Jordan are progressive when considered separately and with respect to the pre-fiscal income. But to assess whether an intervention increases or reduces inequality one must take the whole fiscal system into account, that is, whether it is progressive relative to the income generated with all the other fiscal interventions (Inchauste and Lustig, 2017).<sup>51</sup> With this purpose we assess the marginal contributions to the changes in the Gini coefficient. In-kind health and education benefits reduce inequality by 3.1 points. After in-kind benefits, direct transfers are the most equalising spending (reducing inequality by 1.2 points). The significantly large expenditures on water and electricity subsidies reduce inequality by a similar amount (1.4 points). On the revenue side, direct taxes (PIT) marginally decrease inequality (0.1 points) while indirect taxes (GST and SST) increase inequality (0.5 points).

Official poverty is measured using the household per capita consumption aggregate and results in a poverty rate for Jordanians of 15.7 percent. In the CEQ framework this is equivalent to disposable income.<sup>52</sup> When measured with the national poverty line but based on market income, poverty is 18.2 percent. That is, the combination of direct taxes and direct transfers reduces poverty by 2.6 percentage points. When indirect subsidies and indirect taxes, which have a disproportionate burden on the poorest, are considered, poverty measured at consumable income would rise slightly to 15.8 percent. That is, while poorer households do pay indirect taxes (12 percent of market income for Decile 1), this is compensated by the benefit from indirect subsidies (equivalent to 17 percent of market income for Decile 1). Nonetheless, there are a number of vulnerable households who live just above the poverty line, so the net impact on the cost of living from indirect taxes
makers and fostering datadriven insights for researchers on possible reforms to the fiscal system that could improve its capacity of shortening gaps and promoting welfare-enhancing interventions. The recent move towards increases in transfers to children residing in low-income households seems like a step in the right direction. Coupled with an integral sustainable fiscal strategy, such efforts can boost the poverty reducing power of the Brazilian fiscal system.

Certain assumptions of the current methodology can and should be addressed in future research. One of the most relevant ones is that of the uniform impact of government expenditures on educational or health services. As a work on the varying quality of learning has demonstrated (World Bank 2022a), it could be important to allow varying effects of expenditures that help effectively attain higher levels of human capital. On the health side, literature suggests the Brazilian public system encumbers patients with long waits for some health services that could be translated into economic costs that are not being appropriately accounted (Marinho, 2009). Information contained in indicators such as the World Bank’s Human Capital Index may provide a sensible _proxy_ measure to account for differences in quality.

39
15 in South Africa. In terms of markets, the average number of destinations per exporter is more similar across countries ranging from 1.4 in Botswana to 4.8 in Cambodia and 6.8 in Belgium. Interestingly, the average or median of these

> 33 The very large average exporter size in Cambodia is driven by a small number of extremely large formerly state-owned apparel and textiles producers, as gathered from statistics based on the World Bank Enterprise Surveys for Cambodia.

> 34 Using the same cross-country input dataset used in the construction of the Database, Freund and Pierola (2012) show that exports are dominated by a small group of very large exporters (so-called ‘superstars’). These firms are remarkably larger than the rest, they participate in many sectors and most importantly, they define the productive structures and drive the export growth observed in most countries.

19