rafmacalaba/data-use-mentions-extended huggingface dataset:
inantly in the manufacturing sector. Third, by their nature, domestic workers in the service sector cater mostly to domestic demands and are not easily replaced by workers abroad. Hence, we conjecture that the presence of the labor substitution channel should be consistent with pronounced negative employment effects in the manufacturing sector.

Figure 3: Event Study Analysis: All Sectors <!-- Start of picture text --> 2013 2014 2015 2016 2017 2018 2019<br>MNCs Non MNCs<br>.08<br>.06<br>.04<br>.02<br>0<br>Log Employment -.02<br>-.04<br>-.06<br>-.08<br><!-- End of picture text --> _Notes:_ The figure plots event study analysis results from equation (1) where the dependent variable is the log of employment. The results are illustrated separately for firms with foreign affiliates (blue circles) and firms with no foreign affiliates (red circles). Coefficients along with 95% confidence intervals are illustrated. Standard errors are clustered at the sector level.

Figure 4 repeats the event study regression in equation (1) by sector. Panel (a) reports the results in the manufacturing. As in the previous analysis, we do not find differential pretrends between the two groups. More importantly, for the MNCs, the employment levels dropped by 2.2% in 2017, 4.9% in 2018, and 5.1% in 2019, respectively.<sup>23</sup> In contrast, we do not find any downward trends in employment after 2017 for both groups in the non-manufacturing sector. Indeed, none of the coefficient estimates in panel (b) is Taken the event results to the statistically significant. together, study appear support presence of a labor substitution channel in the manufacturing sector. The rest of the paper corroborates these baseline results in various dimensions.

>
the household could not confirm that the target respondent’s eligibility criteria as described above, the field team would replace this household.

Based on our power calculations, our target was to interview 1,124 individuals: 562 women and 562 men (partners of these women). Early stages of fieldwork proved that we had over-estimated the co-habitation rate in the Philippines (80% estimated, 55% actual). Therefore, while we met our target of interviewing female respondents (564 actual, 562 target), we were only able to reach 85% of our target of interviewing male respondents (478 actual, 562 target).

_Table 1: Sample Summary_

|**Respondents**|**Treatment**|**Control**|**Total**|
|---|---|---|---|
|**Male**|237|241|478|
|**Female**|284|280|564|
|**Total**|521|521|1,042|

We collected the data between November 7, 2019, and February 2, 2020, from 1,042 individuals, spread across 149 barangays. The survey data was collected using two types of questionnaire: the household questionnaire, which was administered to the most knowledgeable member of the household, and the individual questionnaire (administered to the target women and her partner). Each survey interview took

> 9 The motivation for the third criterion is that most GBV studies focus on women in reproductive age, i.e., 15-50 years old.

8
We explored additional Twitter handles that focus on traffic and road safety in Kenya. These include twitter handles such as RoadAlertsKE, KenyanTraffic and ThikaTowntoday. The majority of tweets from these other handles are already tweeted out by Ma3Route; therefore, including these additional handles does not produce many new tweets to incorporate into the dataset. An additional source of data is including tweets that mention Ma3Route but are not necessarily posted by Ma3Route. While these tweets are not included in the current analysis, they can be easily incorporated to expand the data set that is used to generate additional crash reports. We have already done this for the data set of crashes that we are producing for the Government of Kenya.

# **Building a Truth Data set of Tweets**

We build a truth data set of Ma3Route tweets where tweets are labeled as to whether they refer to a specific traffic crash and, if they do, are geocoded. We code all potentially crash related tweets from July 2017 to July 2018. We define a tweet as potentially crash-related if one of the following words appeared in the tweet: _accident, accidents, ajali, axident, collision, crash, crashes, crashs, crush, crushed, damage, disaster, emergency, fatal, fatality, fender bender, fender-bender, hazard, hit, hit-and-run, incident, incidents, injuries, injury, magari zmegongana, mishap, overturn, overturned, ovrturn, ovrturned, pileup, rammed, read end, rear ended, roll, rolled, smash, smashed, wreck, wreckage, zilicrash, zimecrash_ To account for misspellings of select words, we also include tweets if they contained a word that had a Levenshtein distance of two or less to “accident” or “incident” or a Levenshtein distance of one to “crash” or “crashed”.

Six coders were trained to process the 9,480 tweets defined as potentially crash related. Coders were instructed to label a tweet as reporting a crash if the tweet referred to one or more specific crashes; general comments about
12 such an argument is that when a recipient government depends heavily upon transfers, greater control can be maintained over local governments' decisions. While there is little doubt that grants provide the potential for considerable fiscal control by the granting government, simple proportions ignore the types of grants used, some of which provide more control than others (see below). The assumption also ignores the fact that other factors, such as expenditure mandates and control over personnel decisions, also can play especially important roles in local government decision making.

So as to provide some comparative perspective across several countries, Table 2 has been constructed from the IMF Government Finance Statistics data for both 1974 and 1984. Since shared taxes are not included in the transfers, the data do not fully reflect the flow of revenues under considerable direct control of the central government. Because lower level governments can include both local jurisdictions and intermediate level jurisdictions such as states or provinces in federal systems, we distinguish between them wherever available. Again, the sample was chosen arbitrarily based primarily upon data availability.

Again, the results do not suggest overwhelming evidence that there have been signif icant increases or decreases in the role of intergovernmental transfers in developing nations nor that there is any Countries," Environment and Planning, Vol. IV (1986), pp. 405-418; or Michael Wasylenko, "Fiscal Decentralization and Economic Development," Public Budgeting & Finance, Vol. 7, No. 4 (Winter 1987).
identification, the studies use micro panel data, higher frequency (monthly) data, or both. In our sample, the most studies use time series data in monthly frequency (940 estimates).

# [Figure 6 about here]

Typically, papers do not control for any confounding factors and estimate a bivariate relationship between the reference rate and a specific lending rate. However, some factors may indeed bias the passthrough coefficient (see Section 2). Therefore, we distinguish between literature that controls for confounding factors such as bank credit risk, bank competition, bank concentration, and the effect of the global financial crisis, and literature estimating only a bivariate relationship (see Figure 7). Studies control for credit risk and bank competition only rarely (59 and 71 estimates). This is rather surprising, considering that most studies are motivated by the Rousseas (1985) framework, which assigns a significant role to bank competition. Overall, 122 pass-through estimates control for a possible GFC effect using a crisis dummy. Somewhat less than half of these estimates consider the dummy also in an interaction with the reference rate (60).

# [Figure 7 about here]

Next, we review the quality of authors and the publication outlet in which the papers selected for our meta-analysis were published.

To measure the quality of authors, we use the individual author’s _h_ -index from the ResearchGate and Scopus databases. Because the _h_ -index of some co-authors is not available, we use the _h_ -index value for the co-author with the highest available _h_ -index. We construct three conditioning variables for the meta-regression based on the highest _h_ -index from among the co-authors. The first variable is constructed using the Scopus database. The second and third variables are constructed using the ResearchGate database, distinguishing between the overall _h_ -index and the _h_ -index that excludes selfcitations. Due to better data availability, we use the overall _h_ -index from the ResearchGate database in our baseline meta-regression. Figure 8 (left panel)
Policy Research Working Paper 10930

# **Abstract**

This paper presents comprehensive findings on the relationship between economic growth and poverty. Using a first-difference model applied to data from more than 80 countries spanning over 20 years, the paper investigates how changes in gross domestic product affect the Multidimensional Poverty Index and its subcomponents, considering variations in income level, region, and resource dependency. The analysis confirms that economic growth generally reduces the Multidimensional Poverty Index, although the magnitude of the effect varies significantly. It is less pronounced in low-income countries, Sub-Saharan Africa, Latin America and the Caribbean, and resource-dependent countries. The paper disaggregates gross domestic product growth by its dimensions, revealing that growth driven by total factor productivity, consumption, and sustainable growth significantly decreases the Multidimensional Poverty Index. In contrast, factors such as human capital development, capital deepening, investment, government spending, exports, and imports show ambiguous effects on the Multidimensional Poverty Index. These findings suggest that the effectiveness of these factors depends on country-level conditions. Given the clearer positive impact of total factor productivity, consumption, and sustainable growth on reducing multidimensional poverty, policy makers should prioritize strategies that promote these types of growth to fight poverty, especially in contexts where the effects of other growth contributors are uncertain or not well understood.

This paper is a product of the Poverty and Equity 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 fmulangu@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank. org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>
of consecutive 20-day periods with less than 40mm of rainfall.<sup>12</sup> We also include _30-year average rainfall_ to measure the long-term effects, and the current growing season corresponding to the survey reference period to measure short-term effects. In addition to these climate shocks, we also control for CSA adoption, crop diversity,<sup>13</sup> and access to credit, since these might influence how climate shocks affect vulnerability and

> 10 In terms of poverty transitions, vulnerable households are those who are chronically poor, or those who fell into poverty by the third survey wave regardless of whether they were poor or not in the first and second survey waves. This includes households that were poor in the first and thirds wave but not in the second wave, those poor in the second and third waves but not in the first wave or indeed those that were not poor in the first and second waves but fell into poverty in the third wave.

> 11 This includes households that were never poor across the three survey waves, those poor in the first and second waves but not in the third wave, or poor in the first wave but not in the second and third waves, or ones that were not poor in the first and third waves but were poor in the second wave.

> 12 We constructed this measure based on personal communications with agronomists based at the Zambia Agricultural Research Institute (ZARI).

> 13 Computed as the Simpson index based on area cultivated per crop relative to total area cultivated.

9
in exporting compared to non‐ exporting firms, but only for higher‐skilled jobs (Bøler et al. 2018). It is not clear if this channel also applies to a developing country context abundant in unskilled workers.

Our paper contributes to the strand of literature linking trade and labor in the following ways: (i) We modify the cost share functions that have been introduced by Feenstra and Hanson (1996) to assess the impacts of trade on different worker types. Rather than focusing on high‐ versus low‐skill workers, our emphasis is on female versus male workers. (ii) We use a rich harmonized micro‐level dataset that covers a large number of developing and emerging countries, while many studies on trade and female labor participation in a developing country context are performed at a more aggregate level.<sup>5</sup> (iii) Finally, we also assess the role of female entrepreneurship, specifically female ownership and management, for the female labor share premium of trading firms.<sup>6</sup> Our paper is closest in spirit to Juhn et al. (2013, 2014) who focus on the relationship between trade liberalization, rather than trading status, and female labor

> 5 Exceptions include Ederington et al. (2009) for Colombia, Chen et al. (2012) for China, and Juhn et al. (2013, 2014) for Mexico.

> 6 For an overview of female entrepreneurship and a discussion of different drivers, see, e.g., Carranza et al. (2018).

3
Table A4: Food Insecurity Experience Scale (FIES) Questions in Nigeria

|41|
|---|

||Pre-COVID-19 Surveys||COVID-19 Surveys||
|---|---|---|---|---|
|FIES|Question|Recall|Question|Recall|
|FS1|You or any other adult in your household worried<br>about not having enough food to eat because of<br>lack of money or other resources?|30 day|You or any other adult in your household were<br>worried about not having enough food to eat be-<br>cause of lack of money or other resources?|30 day|
|FS2|You, or any other adult in your household, were<br>unable to eat healthy and nutritious/preferred|30 day|You, or any other adult in your household, were<br>unable to eat healthy and nutritious/preferred|30 day|
||foods because of a lack of money or other re-<br>sources?||foods because of a lack of money or other re-<br>sources?||
|FS3|You, or any other adult in your household, ate only<br>a few kinds of foods because of a lack of money or<br>other resources?|30 day|You, or any other adult in your household, ate only<br>a few kinds of foods because of a lack of money or<br>other resources?|30 day|
|FS4|You, or any other adult in your household, had to<br>skip a meal because there was not enough money<br>or other resources to get food?|30 day|You, or any other adult in your household, had to<br>skip a meal because there was not enough money<br>or other resources to get food?
associated with one standard deviation in learning. The first uses simple descriptive data to characterize 7 Another, related effort seeks to combine the quality and quantity of education into a single cross‐country measure – the Learning Adjusted Years of Schooling, or LAYS – to allow comparison of education systems across countries (Filmer et al. 2018). While the LAYS allows an aggregate measure of the quantity and quality of education in an education system, it is not naturally suited to characterizing the gains from a given intervention.

4
# **1 Introduction**

Paying for performance (PFP) is a common contracting approach in settings with principal-agent problems (Prendergast, 1999; Lazear, 2000; Duflo et al., 2012; Rothstein, 2015; DellaVigna and Pope, 2018). Such contracts typically provide agents with a checklist of selected outputs and an incentive assigned to each output, and therefore engage agents through two economic channels. First, the checklist explicitly communicates what outputs the principal values and prioritizes. Agents may respond to this information alone, for example, because it reduces their uncertainty about how to allocate their effort or because they are intrinsically motivated (Arrow, 1963). Second, agents may respond to the financial incentives for the specified outputs. They may also respond differently depending on whether the incentives are framed as rewards or penalties (Kahneman and Tversky, 1984; Kahneman et al., 1991; Hossain and List, 2012).

In this paper, we examine the direct and indirect impacts of adding incentives to information on health workers’ adherence to clinical guidelines. In an incentivized framed field experiment embedded in a survey of 691 primary care clinics in Nigeria, we randomized 1,363 maternity care workers to three study arms: information, rewards, and penalties. We asked workers to review records, so-called partographs, of five fictitious patients receiving labor and delivery care and to identify clinically necessary—or indicated—actions for each patient. All participants received a participation fee and a checklist of seven common clinical actions related to maternity care of which five were associated with varying prices. Participants in the rewards arm could receive the associated price for correctly identifying the need for the incentivized actions. The penalty contract is isomorphic to the reward contract: we deducted the same amounts for participants who did not recommend the same five actions when they would have been appropriate. We measure performance as the share of indicated actions participants recommend, using the universe of possible clinical actions, which includes actions that were not listed but may be indicated.
# The surveys

The in-person Palestinians’ Psychological Conditions Survey (PPCS) aimed to better understand mental health outcomes in the West Bank and Gaza, as well as links between those outcomes and exposure to violence, socioeconomic conditions, and attachment to the labor market.<sup>5</sup> A detailed adult individual questionnaire embedded in a larger household survey included modules assessing personal exposure to conflict events (Gaza only), food security, signs of aggressive tendencies, life satisfaction, risk for mental health problems, symptoms of depression, exposure to traumatic events, symptoms of PTSD, COVID exposure, and prosocial behavior. The PPCS was fielded in person in the West Bank, East Jerusalem, and Gaza between March and May 2022. Out of a planned sample of 7,057 households, 6,140 participated in the survey. The data collection team attempted to interview 6,138 adults for the detailed individual interview, and 5,877 individuals formed the final sample used in analysis. A back-of-the-envelope calculation shows that each completed survey cost approximately $23.40, excluding staff and consultant time.

From March 19 to 22, 2022, concurrent with the first month of data collection for the PPCS, the Poverty team ran a pared down version of the adult individual questionnaire through a link that was advertised on Facebook. This shorter questionnaire covered basic sociodemographic questions, personal exposure to conflict events, food security, signs of aggressive tendencies, risk for mental health problems, symptoms of depression, COVID exposure, prosocial behavior, and questions about Facebook usage and whether any other household members had completed the survey (for weighting purposes). Adverts with the survey link were sent to a random sample of Facebook accounts stratified by governorate and gender to try to achieve a realized sample similar in those characteristics to the overall population. During the four days in which the Facebook adverts were live, 18,896 people clicked on the link. Of those, 2,279 people started the survey, and 1,227 respondents formed
is much higher (greater than 20%) in the rural areas of Niassa, Tete, Manica and Sofala.

The negative welfare effect translates into an increase in poverty. Overall, the 2016 food price spike led to an increase in the poverty headcount ratio of 5.9 percentage points. The bulk of the poverty increase occurred in the rural areas, with the provinces of Tete and Manica bearing much of the brunt. We also examine the sensitivity of these estimates to model assumptions – in particular, assumptions about how much of the increase in consumer prices is transmitted to producer prices. We simulate the poverty estimates under various scenarios and find that under the assumption of no change in producer prices, the poverty headcount ratio increased by close to 8 percentage points. This corresponds to an upper-bound on the potential poverty impact of the price rise – this is also because the simulation does not account for behavioral responses such as substitution of crops produced and food consumed. At 3.5 percentage points, the negative effects 3
**Evidence shows that new SOEs have been created across several markets after 2011** . About 52 percent of the 1,399 SOEs  matched to the MoF firm-level data were incorporated between 2011 and 2019 ( <mark>Figure</mark> 3). The proportion of 2-digit sectors with at least one SOE present increased over the sample period of 2011-2020 by about 3 percentage points from 75 percent to 78 percent.

Figure 3: SOE Creation and Presence Across Sectors <!-- Start of picture text --> (a) New SOEs Created (b) Percent of Two-digit Sectors with at Least<br>One SOE Present<br>160 79.0%<br>140<br>78.0%<br>120<br>77.0%<br>100<br>80 76.0%<br>60<br>75.0%<br>40<br>74.0%<br>20<br>0 73.0%<br>1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br><!-- End of picture text --> Source: World Bank Global Businesses of the State (BOS) database, 2019. In the database, SOEs are corporations where national or subnational governments have ownership stake of at least 10%, either directly or indirectly. Corporations are business entities that are (a) capable of generating a profit or other financial gain for their owners, (b) recognized by law as legal entities separate from their owners who enjoy limited liability, and (c) set up for purposes of engaging in market production (See World Bank 2022 for details).

**The number of firm observations increased over the sample period, possibly confirming new business creation.** Table 3 presents the distribution of firms–private firms and SOE–with revenue and employment data for