rafmacalaba/data-use-mentions-extended huggingface dataset:
Table IV.2 (cont'd)

||||S|ubsadlzation||
|---|---|---|---|---|---|
|Title of Project.<br>Country|Contraceptives<br>Donated|Company Subsidy|Donor Subsidy<br>for Serviceg|Client Pays Fees|Pollcy<br>*nd Estimates<br>of<br>Coats/Revenues<br>or BWget|
|uNOTlrCPfITAhXINY|EXlS<br>IN<br>**_DAN*L?_**PL*L|IN (cont-d)||||
|Thaillnd:<br>Family Planning<br>Narket Survey<br>q<br>Marketing<br>Capa-<br>bility Evaluation|N/A|N/A|N/A|N/A|Project cost- $59.038|
|Ibxicos<br>Nedical Service<br>NicGenterprises<br>In metropolitan<br>Areas|In first year<br>only|N/A|Only for first<br>M.D|Yeo|Project cost- $150,039|
|Self-Financing<br>Family Planning<br>Project|Sold to NDs at<br>cost and donated<br>to Social<br>Security<br>na|Social Security<br>services only|so|**eas**butbelow<br>market price for<br>IUDbecauseof<br>subsidy.<br>Consul-<br>tationfeefor<br>private<br>M6Dm|Over 3130,096/year|
|SOCIALhAWING||||||
|**N15erla:**<br>Sterling Products<br>Private Sector<br>Family Planning<br>Project|Tea to both dis-<br>tributlon projects|**Tea.**for provider
Fredrik Sjoholm, and Susan Zhu, 2010 "Globalizaton and Imperfect Labor Market Sorting" University of Nottingham Research Paper 2010/30.

- Dix-Carneiro, Rafael 2010 “Trade Liberalization and Labor Market Dynamics”, mimeo, Princeton University.

- Eaton, Jonathan, Sam Kortum, Brent Neiman, and John Romalis. 2009 “Trade and the Global Recession.” Unpublished working paper.

- Ebenstein, Avraham, Ann Harrison, Margaret McMillan, and Shannon Phillips.  2009 “Estimating the Effect of Trade and Offshoring on American Workers Using the Current Population Surveys.” NBER Working Paper No. 15107, Cambridge, MA.

- Feenstra, Robert and Gordon H. Hanson.1997 “Foreign Direct Investment and Relative Wages: Evidence from Mexico’s Maquiladoras” _Journal of International Economics_ 42: 371393.

- Feenstra, R.C., 2010. Offshoring in the Global Economy: Microeconomic Structure and Macroeconomic Implications, MIT Press, Cambridge, Massachusetts.

- Felbermayr, Gabriel, Julien Prat, Hans-Jörg Schmerer, 2011 “Globalization and Labor Market Outcomes: Wage Bargaining, Search Frictions, and Firm Heterogeneity” _Journal of Economic Theory_ , 146(1) (January): 39-73.

33
15598.</mark>

- <mark>Schlenker, W., & Lobell, D. B. (2010). Robust negative impacts of climate change on African agriculture.</mark> _<mark>Environmental Research Letters</mark>_ <mark>,</mark> _<mark>5</mark>_ <mark>(1), 014010.</mark>

- <mark>Somanathan, E., Somanathan, R., Sudarshan, A., & Tewari, M. (2021). The impact of temperature on productivity and labor supply: Evidence from Indian manufacturing.</mark> _<mark>Journal of Political Economy</mark>_ <mark>,</mark> _<mark>129</mark>_ <mark>(6), 1797–1827.</mark>

- <mark>Thorbecke, E., & Ouyang, Y. (2022). Towards a virtuous spiral between poverty reduction and growth: Comparing sub Saharan Africa with the developing world.</mark> _<mark>World Development</mark>_ <mark>, 152, 105776.</mark>

- <mark>World Bank. (2016).</mark> _<mark>World development report 2016: Digital dividends</mark>_ <mark>. World Bank: Washington.</mark>

- <mark>World Bank. (2021). World Bank estimates based on data from the Global Subnational Atlas of Poverty, Global Monitoring Database. World Bank: Washington.</mark>

26
48

# **Figures**

## Figure I: Dynamic Ban Effects on Imports

<!-- Start of picture text --> −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10 11 12 −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10 11 12<br>Month relative to period when treatment first changes (t=0) Month relative to period when treatment first changes (t=0)<br>(a) Extensive Margin (b) Intensive Margin<br>.5 3<br>.4 2<br>.3 1<br>0<br>.2<br>−1<br>.1 −2<br>0 −3<br>−.1 −4<br>−.2 −5<br>−.3 −6<br>Effect on Pr(imports>0) −.4 Effect on log(imports) −7<br>−8<br>−.5 −9<br>−.6 −10<br>−.7 −11<br><!-- End of picture text --> _Notes_ : The figures show De Chaisemartin and d’Haultfoeuille (2022) estimates of the effects of import bans on the extensive and intensive margins of imports, relative to the last month before the ban takes effect, using an HS8 product-month level panel. The sample covers the January 2019September 2022 period (but only coefficients in January 2021-May 2022 are reported) and excludes non-fertilizer HS8 products whose imports were ever banned. The treatment variable is a set of dummies _ban_<sup>_τ_</sup> _ct_<sup>indicating whether fertilizer product</sup><sup>_c_was first banned</sup><sup>_τ_months before month</sup><sup>_t_, and</sup> the
|[1.224]|[1.425]||[0.026]|[0.035]||[52.685]|[45.289]||
|_First girl_ * _Post2_|0.061|0.506||0.039|0.059||-54.144|-58.160||
||[1.871]|[1.746]||[0.039]|[0.049]||[59.750]|[53.773]||
|N||10,965|||20,562|||10,293||
|Baseline mean||18.931|||0.704|||186.426||
|_Xijt_||x|x||x|x||x|x|
|_1st Girl_ x Birth year FE|||x|||x|||x|
|_Female_ x Birth year FE|||x|||x|||x|
|_Female_ x State FE|||x|||x|||x|
|_Female_ x Birth order FE|||x|||x|||x|
|Birth order x State FE|||x|||x|||x|
|State-specifc time trends|||x|||x|||x|
|_Firstborn girl_ x State FE|||x|||x|||x|
|_Firstborn girl_ x Birth order FE|||x|||x|||x|

NOTES: This table reports investment effects for children of second- and higher-order birth order using the REDS sample. Each column is from a separate OLS regression. We control for _Female_ , _Firstborn girl_ , and fixed effects for birth year, birth order, and state in all columns. The vector
9. **Water and sanitation** is measured at the household-level and includes three outcomes: an index that tracks households’ access to potable water and toilet facilities; an index that measures public water and toilet facilities in each community; and an index of questions related to households’ satisfaction (measured on the four-point Likert scale) with water, sanitation, and cleanliness in their community.

10. **Economic outcomes** is measured at the household-level and comprises four outcomes: indices of physical assets, agricultural assets (e.g., livestock, farm tools), and dwelling materials; as well as an index capturing total consumption expenditure over the last month.

Families 1–4 (general utilization, maternal utilization, health outcomes, and satisfaction) convey the main effects and mechanisms for our medium-run results. Families 5–8 (clinic quality, health service delivery, contributions to clinic, and CDPE) represent other plausible but unsupported mechanisms. Finally, families 9 and 10 (water and sanitation and economic outcomes) are additional outcomes presented in the appendix.

# **3.2.2 Ebola Case Data**

We rely on the Epi Info Viral Hemorrhagic Fever (VHF) database, which was the primary data management system used for case and contact tracing during the outbreak. The Ministry of Health and Sanitation, with support from the CDC implemented and maintained the VHF database through the end of the epidemic, and McNamara et al. (2016, 39) describe it as “the most comprehensive epidemiologic and laboratory data on Ebola cases available in Sierra Leone.” As noted above, the VHF cannot be used to measure the underlying incidence of Ebola; rather, it reflects reported cases — a particularly important outcome for stopping contagion and containing the epidemic (Enserink 2014).

We use a de-identified version of the VHF database (where patient names and characteristics have been redacted).<sup>16</sup> While the data were compiled at the National Ebola Response Center
br>A S|O N D|
|Applications||||||||
|Induction workshops||||||||
|_Interventions_||||||||
|Training workshops||||||||
|Consulting||||||||
|Insourcing||||||||
|Outsourcing||||||||
|_Follow-up Data Collection_||||||||
|Follow-up 1||||||||
|Follow-up 2||||||||
|Service Provider Survey||||||||
|Social Media||||||||

46
## 2.3. OpenStreetMap network data

The transportation network data and the city outlines used in this analysis are sourced from OpenStreetMap (OSM)<sup>4</sup> . OSM is a free project that creates and continuously updates a crowd-sourced map of the whole world. By providing contributors with extensive manuals on how to provide and edit data, OSM achieves standardized data across different regions. Specifically, OSM classifies roads according to different categories such as motorways or residential streets that allow for the differentiation between roads of varying importance. It should be kept in mind that the road network data used here do not account for local infrastructure measures that were implemented specifically to increase a road’s resilience to flooding but are rather described to provide an overall picture of exposure of the road network to flooding based on spatial overlap.

## 2.4. Enterprise Survey

This work also employs firm-level micro data provided by the World Bank Enterprise Survey (ES). Through asking firms in most developing and emerging economies in the world a set of uniform questions, the dataset provides a unique opportunity to compare business activity in different countries. This analysis is based on ES firm observations for a set of 13 large cities in Africa.

In chapter 3, infrastructure disruptions are analyzed through three dependent variables of disruptions experienced by firms: _Blackouts_ , defined as the average annual hours without electricity supply, _water disruptions_ , defined analogously as the average annual hours of water supply outages, and _transport problems_ , an indicator ranging from zero to four that describes the degree to which transport is perceived as an obstacle to business with higher values indicating a greater obstacle.

As firms’ non-randomized location was available, each firm could be classified according to their flood exposure using the flood maps described above. To get a large sample of flood-exposed firms for chapter 3 a methodology was developed to combine flood maps from several sources into a common flood exposure variable. After each firm’s exposure was determined using its city’s respective flood map, a
there are heterogeneous costs of learning, those most familiar with research findings need not overlap much at all with those who value research most for their work.

But that is not what we see in these survey data. Two-thirds of the responding senior staff put a high value on the Bank’s research for their work. They appear to have a reasonably strong incentive to learn, though stronger for some than others. Operational staff with a stronger incentive to learn tend to be more familiar with Bank research. Only 12% of staff fall into the category of ―independently well-informed‖—those who report high familiarity with research, but attach low value to it for their work. The Bank’s senior staff are mainly found in three other groups. The first are those who are ―happily uninformed‖ about research—they attach low value to it for their own work, and report correspondingly low familiarity with research; 23% of senior staff fall into this group. A second group puts a high value on Bank research but its members are not generally successful in accessing that research or find it of limited relevance. This group is not evenly spread across Bank units and sectors, suggesting that there may be scope for a targeted effort at research and its dissemination. The third and largest group is the ―functionally well-informed,‖ comprising 42% of staff. They put a high value on research for their work (a mean score of almost 8 on a scale of 1-10) and are (by their own assessment) quite familiar with research findings (also a mean score of 8 on a scale of 1-10).

While this paper’s results are consistent with the view that stronger incentives for leaning about research translate into greater knowledge, they are also suggestive of some significant frictions. The slope of the relationship between perceived value and familiarity with research— the effect of an increment to perceived value on familiarity, both on a 1-10 scale—is positive but significantly less than unity, suggesting frictions in how the
# **c. Administration of Payments**

P4P payments are administered by district steering committees comprised of members of the District Government, Providers and civil society (Ministry of Health Republic of Rwanda, 2006; Fritsche et al 2010). Facilities submit monthly activity reports ( _Ujit_ ) and quarterly requests for payment to the district steering committee, which is responsible for verifying the data and authorizing payment. For the referral indicators, the facility must also submit verification from the hospital that the referral was appropriate and the referred patient was treated. The committee verifies the reports by sending auditors to facilities on a quarterly basis on an unannounced randomly chosen day. The auditors review the utilization registry and facility records to verify the data reported is the same as the data recorded in facility records. During the 2006-2008 period, MoH conducted a survey of face-to-face interviews with approximately 1000 patients to verify the accuracy of the records. This survey found that false reporting on patients or services was less than 5 percent (HDP 2008).

Information used to compute each facility’s overall quality score is collected under the existing national monitoring system that requires all district hospitals to monitor and supervise the quality of health centers in their districts. Every quarter, a district hospital team from different services (e.g. prenatal, curative care, preventive care) visits each facility on an unannounced randomly chosen day to assess the facility’s quality through direct observation and review of patient records using a standardized tool. At the end of the visit, the team discusses their findings with the facility’s personnel and provides recommendations to improve quality. In P4P districts, the data are used to construct the overall quality score for the facility each quarter.

# **3. CONCEPTUAL FRAMEWORK**

# **a. Model**

In this section we use a simple behavioral model to hypothesize how the introduction of P4P would likely affect provider behavior. We have in mind a rural clinic with no close substitutes locally. We assume for simplicity that a
With the SITC time-series data, we correct for inflation to make discoveries comparable across time periods (using US producer PPP data from the US Federal Reserve), and use a moving window that defines a discovery for a particular year as a category for which exports were never more than $1,000,000 (1985 US dollars) before that year, crossed the $1,000,000 mark that year, and subsequently were exported for more than $10,000,000 ten, eleven and twelve years later. The dollar amounts used by this filter obviously have to be higher than with the 1990s HS data because the time series data are highly aggregated. This moving window identifies discoveries in each year from 1973 to 1990. In addition, any good that was not exported for more than $1,000,000 before 1991, but by the end of the 1990s was exported for more than $10,000,000 (specifically in 2000, 2001 and 2002) is recorded as a discovery in the 1990s.

The countries that reported data in the requisite years and are included in the sample are listed in Appendix II. We will now discuss some stylized facts based on the discoveries identified using this methodology.

# _Stylized Facts_

Using these filters to search for discovery events in the UN COMTRADE export data, we identify 332 instances of discovery in the HS 4-digit data, and 1710 instances in the more disaggregated HS 6-digit data. Discoveries by industry are shown in Table 1.

15
Total sample: 866 firms Participated in NaN business advice program in 2018 <!-- Start of picture text --> Randomly assigned to one of<br>five experimental groups<br>-<br>(0) Treatment group: 698 firms<br>Control group: 168 firms Receive standard 6-page NaN report, plus information<br>Receive standard 6-page NaN sheet with benchmarking and list of 5 actions to improve<br>report on business practices business practices, with four variations:<br>Free SEBRAE training session<br>Invited to enroll Pre-enrolled<br>Motivating sentence: No (1) No motiv, noenroll (2) No motiv, pre-enroll<br>“Your competitors who adopt (173 firms) (170 firms)<br>between 1 and 5 practices less than ;<br>you are 26% more likelyto close Yes (3) Motiv, enroll (4) Motiv, pre-enroll<br>within the next year.” (177 firms) (178 firms)<br><!-- End of picture text --> 1 Correlation based on SEBRAE data.
increasingly decouple agricultural production from its environmental impacts. Central to this will be the design of agricultural subsidies that profoundly influence the structure, function and trajectory of agriculture-food systems. As this paper has demonstrated, even when eminently justifiable, poorly designed subsidies are enmeshed in tradeoffs suggesting the need to clearly analyze the benefits and costs of whether, what, and how to subsidize.

# References

Abay, K.A, G. Blalock, and G. Berhane. 2017. “Locus of Control and Technology Adoption in Developing Country Agriculture: Evidence from Ethiopia”. _Journal of Economic Behavior and Organization_ 143: 98115.

Barrett, C. B., & Bevis, L. E. (2015). The self-reinforcing feedback between low soil fertility and chronic poverty. _Nature Geoscience_ , _8_ (12), 907-912.

Basu, N. B., Van Meter, K. J., Byrnes, D. K., Van Cappellen, P., Brouwer, R., Jacobsen, B. H. et al. (2022). Managing nitrogen legacies to accelerate water quality improvement. Nature Geoscience, 15(2), 97-105 Bevis, L. 2018. “Invisible Heterogeneity in Crop Zinc Concentration and Child Zinc Intake in Rural Uganda” HarvestPlus Working Paper Bevis, L. E., Kim, K., & Guerena, D. (2022). Soil zinc deficiency and child stunting: Evidence from Nepal. Available at: https://leahbevis.files.wordpress.com/2021/12/bevis-kim-and-guerena-2021-4.pdf Carter, M. R., Laajaj, R., & Yang, D. (2013). The impact of voucher coupons on the uptake of fertilizer and improved seeds: Evidence from a randomized trial in Mozambique. _American Journal of Agricultural Economics_ , _95_ (5), 1345-1351.

Carter, M, Lybbert, T and Tjernström, E (2015), "The Dirt on Dirt: Soil Characteristics and Variable Fertilizer Returns in Kenyan Maize Systems
Starting with the former, trade policy has been shown to have a substantial gender impact because of sorting by gender into exporting and non-exporting firms (Wood, 1991; Fontana and Wood, 2000; Ozler, 2000).<sup>7</sup> Rama (2002) considers the impact on women‘s employment and wages of a downsizing of state-owned enterprises in Vietnam using simulations based on the Vietnam Living Standards Survey and finds evidence for initial shedding of female workers (relative to men), but a reduction in gender pay gaps. Secondly, studies on gender differences resulting from macroeconomic crises have yielded conflicting conclusions about the responses of female workers relative to those of male workers (see e.g. Thomas et al., 2002).

Macroeconomic shocks have profound impacts on job flows and wages. Studies of manufacturing firms suggest that downturns due to business cycle fluctuations are typically associated with _excess_ churning of firms and workers (Davis and Haltiwanger, 1992; Davis et al. _,_ 1996). Gallego and Tessada (2009) use sectoral panel data on job flows to examine the employment impact of sudden stops in Brazil, Chile, Colombia, and Mexico and find that crises are associated with lower job creation and increased job destruction and that these effects are heterogeneous across sectors and countries. Hallward-Driemeier and Rijkers (2010) show that in the case of Indonesia, the East Asian crisis was particularly destructive, of firms and jobs, with the usual correlation of productivity and survival breaking down during the crisis.

However, very few studies examine heterogeneity in impact by gender. We are aware of only two papers that look at gender differences in job creation and destruction focusing on firm-level employment dynamics. Houseman and Abraham (1993) examine differences in employment responses of men and women to changes in output in U.S. and Japanese manufacturing from 19701990. Although their study does not explicitly focus on employment adjustment in response to financial crises, their findings suggest that, relative to men, women bore a disproportionate
are an option for reducing error in the measurement of rural agricultural labor.

The rest of the paper proceeds as follows. In section 2, we offer background on labor measurement. In section 3, we provide an overview of the empirical approach, including details on the survey experiment. In section 4, we present the results of our experiment on the reporting of labor inputs in family farming and outline the sources of bias in recall data. Section 5 concludes.

# **2. MEASURING LABOR**

## _2.1. Current practice_

The wealth of evidence on the quality and reliability of labor statistics in household surveys comes largely from the United States (for a thorough review, see Bound, Brown, and Mathiowetz 2001). In developing and agriculturally driven countries, for contrast, little is known about the extent to which the design of surveys influences labor statistics. Clearly, it is difficult to extrapolate from studies conducted in the United States to the African context. Moreover, the existing literature on data quality and survey methods in low-income settings rarely pertains to farm labor (see Bardasi

> 3 Examples include farm household models (Barnum and Squire 1979; Benjamin 1992; Rosenzweig and Wolpin 1985; Singh, Squire, and Strauss 1986), shadow wages in family farm labor (Jacoby 1993), trade-offs between hired and household labor (Chowdhury 2010; Deolalikar and Vijverberg 1987; Johnston and Le Roux 2007), the way households allocate labor to farm and off-farm work (Lanjouw and Lanjouw 2001; Matshe and Young 2004; Shapiro 1990), and intrahousehold labor allocation choices (Udry 1996).

3
11 the Soviet era, “taxpayers” were “large in size and small in number”, and the state had many other levers of control besides taxes (Martinez-Vazquez & Wallace, 1999). However, in a shift from a centrally controlled to a market economy, the fiscal system needed to be reformed. These changes represented significant shocks to ordinary citizens. In particular, for the first time, individuals were faced with the direct payment of taxes, including being asked to file different tax returns. The transition process brought up many policy questions, among others: the tax system, the structure of tax administration, and the degree of political participation. Voluntary compliance and self-filing, two important pillars in a modern tax system, were completely absent just after the planned socialism (Martinez-Vazquez & McNab, 2000). Following the early transition, tax evasion and avoidance reached very high levels, as the new tax administration was not prepared to enforce taxes in a market-based economy with large numbers of taxpayers. Tax administration was poorly equipped (e.g., inappropriate use of modern computers and office technology) and managed. Information was used ineffectively, the collection enforcement was weak and there existed substantial corruption practices (Trasberg, 2005). Further, the connection between the payment of taxes and the provision of public goods had been largely concealed under socialism, which might have reduced the identification with the state and thus the willingness to pay taxes.

Profeta (2005) stresses: “Radical reform means that not only do the new fiscal institutions have to introduce new instruments of taxation, a transparent fiscal system, and a well-working tax administration, but also that they have to correct attitudes, incentives and relations” (p. 65). Easter (2007), e.g., reports the findings of a poll conducted by the tax administration in the Tambov region in the early years of the transition, which indicated that only one-third of the respondents argued that paying taxes to the state would provide any benefit to themselves
# **Figure 19. Services Exports as Percent of Total Exports**

<!-- Start of picture text --> 40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>WCCEs SCCEs WCCIs SCCIs<br>1990 2015<br><!-- End of picture text --> Note: Country groupings follow Table 2. _Source: WDI._