rafmacalaba/data-use-mentions-extended huggingface dataset:
that are not directly measurable on the ad platform itself. Malaria prevention campaigns offer an example. Malaria is a disease which disproportionately affects rural people in India. Behaviorally informed ads could provide information and persuade individuals to take precautionary measures. However, while digital advertising provides the ability to micro-target messages to specific subgroups of the population, certain segments remain harder/more expensive to reach than others. At the same time, measuring the impact of such ads on offline behaviors like using bed nets is challenging, and requires availability of individual survey responses that could be connected with ad exposure data.

In this paper, we first introduce a new methodology and a novel toolkit for conducting surveys and experiments on social media. We then use this tool to evaluate a Facebook malaria-prevention ad campaign designed and run by Malaria No More, a global public health non-profit with a strong presence in India. The campaign’s goals were the promotion of behaviors to prevent malaria infection and to seek timely treatment in case of symptoms. It consisted of more than 50 creatives targeted to different personas. With an average frequency of 3.6 impressions per user, the campaign had a total reach of about 130 million people on Facebook and Instagram over 6 months. In the evaluation, we consider the question “Did the campaign work?” along with the question “Did it work for those most at risk?”.

We recruited two groups of respondents (a longitudinal and a cross-sectional sample) on Facebook and Instagram offering mobile credit as an incentive to fill out a series of chat surveys conducted on Messenger. We stratified the recruitment across 80 districts in three high-burden states, and used real-time optimization to guarantee representativeness of individuals living in non-solid dwellings, where malaria risk is higher. We then worked with the non-profit advertising team to exclude 40 randomly-picked districts from the delivery of the campaign. Relying on a set of selfreported attitudes and behavioral outcomes, we found evidence that the ad campaign increased the use of bed nets among individuals living in solid houses (by
between crop mask and the accumulated index was then used as a second set of dependent variables. Two advantages of this procedure are that it covers areas cultivated by non-registered enterprises and that it can be applied even in settings where statistical yield data may be entirely unavailable.

# **3.2 Descriptive statistics**

Table 1 combines data on area cropped from form 29 in 2018 (column 2) with the same information for 2005 (column 1). In 2018, of 27.7 mn. ha of cultivated land captured by the statistical system, 33% was under wheat and barley, 22% under sunflower, 17% under maize and 6% under soybean. Compared to 2005, area expanded by some 3.6 mn. ha and crop composition shifted with quadrupling of the area under soybean, tripling of maize area, and a 75% increase in sunflower. While wheat area remained constant, area under barley declined by 40% and that under root and fodder crops by 30%. These shifts were partly policy-induced as in the case of sunflower for which Ukraine introduced 23% export duty in 1999 that was gradually reduced to 10% in 2014. The associated expansion of sunflower crushing capacity helped Ukraine to develop into the leading global supplier of sunflower oil , creating demand for local sunflower seed and resulting in the area expansion visible in the data. Data on yield, input cost and imputed profit from form 50, an expanded questionnaire for the subset of farms greater than 200 ha, suggest this reflected a move towards relatively more profitable crops. Imputed profits from maize and sunflower are 2.4 and 2.2 times that of barley while soybean and wheat profits exceed those from barley by 56% and 22%.

To assess implications for crop rotation, table 2 combines remotely sensed with statistical information for the four main crops in 2017 and 2018. In 2017, some 20% of cereals, 10% of sunflower, 24% of maize, and 17% of soybean was mono cropped (
Policy Research Working Paper 7745

# **Abstract**

Commodity traders are often the focus of popular resentment. Food price hikes in 2007–2008 resulted in protests and food riots, and spurred governments to regulate traders. In March 2011, Government of Bangladesh banned delivery order traders in the edible oils market, citing cartelization, and replaced them with a dealer’s network appointed by upstream refiners. The reform provides a natural experiment to test alternative models of marketing intermediaries. This paper develops three models and derives testable predictions about the effects of the reform on the intercept of the margin equation and passthrough of international price. Using wheat as a comparison commodity, a difference-of-difference analysis of high frequency price data shows that the reform led to (i) an increase in domestic prices and marketing margins, and (ii) a weakening of the pass-through of imported crude prices. The evidence is inconsistent with the standard double-marginalization-of-rents model wherein intermediaries exercise market power while providing no value-added services, or with a model where delivery order traders provide credit to wholesalers at below-market interest rates. The evidence supports a model where delivery order traders relax binding credit constraints faced by the wholesale traders.

This paper is a product of the Environment and Energy Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at fshilpi@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
preferences with a common elasticity of substitution _σ >_ 0 across products and a Cobb-Douglas aggregation across sectors with the expenditure share _φ_<sup>_s_</sup> for each sector _s_ . To characterize trade between countries, while we have multiple regions only in country 1, we assume that there is a national aggregator for each sector in country 1. The aggregator of each sector can source each product of that sector from the lowest cost region within the country at no trade cost. The national price of each product is thus equalized across consumption locations within the country. Then, this aggregator trades with partner countries. If no region in country 1 is the lowest cost supplier of a certain product for country 1, after taking into account cross-country differences in production cost and bilateral trade costs, then country 1 imports that product, and no region in country 1 produces that product at the equilibrium. To simplify our analysis, we assume that there is no production in the residual labor market of country 1 and further assume that there are only formal sectors in other countries _n_<sup>_′_</sup> = 1.<sup>31</sup> Labor, fixed factor, and composite intermediate inputs are used for production in country 1.

> 30Our model is able to allow for multiple regions and dynamic labor mobility for all countries. For quantification, however, we would need detailed labor mobility data from each country. Therefore, we restrict the analysis of the effect of trade shocks on internal labor mobility only to one country, which will be Brazil empirically. The general equilibrium feature of the trade part applies to all of _N_<sup> ̄</sup> countries.

> 31Combined with the previous assumption on the systematic component of the utility of workers in the residual labor market, we effectively allow for neither production nor consumption in the residual market.

17
addition, the definition of “panel data” may itself vary for different contexts. For example, a panel may be defined based on whether the household head remains the same or whether the residence, where the data are collected, remains the same. Our analysis is not affected by this issue since we construct the synthetic panels for all countries using the same method.

> 4 On a macro level, the fact that happiness is observed not to increase as a country's income rises has long been known as the Easterlin paradox (see, e.g., Easterlin et al., 2010).

4
an increase in overall borrowing (case 3).

If instead households are sensitive to loan labelling (and are therefore credit constrained for sanitation in the absence of a sanitation loan), then we would expect to observe an increase in toilet uptake, combined with either an increase in overall borrowing (case 4), or a particular change in borrowing composition - a shift away from another loan type labelled for an investment that is less desired than sanitation (case 5). The latter would only happen if the household continues being overall credit constrained, in spite of the availability of additional credit.<sup>29</sup> Whether or not we will be able to identify the underlying mechanism of impacts will depend on whether or not we find an increase in overall borrowing or a change in borrowing composition. If we observe an increase in overall borrowing, then that is not a sufficient condition for exclusion for any of the three possible mechanisms, as either case 1, case 2 or case 4 in Figure 4 could apply. If, there is no increase in overall borrowing, but there is a shift away from another loan type, then we can identify the channel by investigating the nature of the shift. In particular, while the lower interest rate can explain substitution away from more expensive loans (case 4), it cannot explain substitution away from loans with a similar or lower interest rate. The latter would be consistent with households responding to the loan label (case 5).

**Results** To examine changes in borrowing behaviour, we consider a number of dimensions of borrowing. First, we consider impacts on MFI loan uptake, overall from any MFI and from the implementing MFI in particular, using official credit bureau data and administrative data from the implementing MFI. Next, we assess impacts on borrowing from other formal institutions (including commercial banks, cooperatives,

> 29This is possible, for instance, because of the various loan caps imposed by the RBI and by the MFI.

18
sales in West Forest between 1986 and 1987 is also clearly apparent in the survey data (table 6). The CSSPA data confirm that there was a fall in coffee production in the West Forest between 1985/85 and 1988/89, though the decline is not as dramatic as the fall in the survey data.

One problem with trying to match up the survey data with the CSSPA data is that urban areas within the forest region are considered either as belonging to Abidjan or Other Cities regions for the purposes of the household surveys. The CSSPA production estimates do not distinguish between the rural and urban areas within the Forest and Savannah regions. Even if we assume however, that in 1985, all the cocoa produced in Other Cities is attributable to the West Forest region, sales per capita in the West Forest would still far slightly below that of the East Forest. The disparity between the two regions in terms of total cocoa production would still be large, due to the difference in population size. And in 1988, even with the assumption that all cocoa sold by the households classified as living in Other Cities is attributable to the West Forest, per capita production in the West Forest would still be substantially below that of the East Forest. However, some of the disparity between the survey and the CSSPA production figures could be due to the possibility that households living in the East Forest reporting sales of cocoa may have actually grown cocoa in the West Forest. However, even if this were the case, it would not explain why sales declined so dramatically in the West Forest between 1986 and 1987, and not in East Forest.

Thus, the sharp decline in cocoa and coffee production in the West Forest survey data between 1986 and 1987 do not seem to be completely consistent with the CSSPA production data. The decline in per capita expenditures and thus the increase in poverty in West Forest may therefore be overstated to some degree. Indeed, there does seem to have been some sampling bias in the survey data. Demery and
42.8|23.2|

_Note:_ This table presented total amounts of income components for each year in the final microdata. It includes survey and tax records after the merging of the two sources, but before scaling up components to macroeconomic aggregates. Values are in nominal billions of Lempiras.

# **D Income definition using tax data**

In this appendix we provide a detailed description of how we construct income variables in the tax records. We start by presenting the composition of total income, then discussing in detail each of its components:

**Total individual income** =

- _+ Capital Income_

- ++ Dividend income (DMR 113)

- ++ Interest income (DMR 115)

- ++ Rental income (DMR 106)

- _+ Wage income_

- ++ Wages (reported on yearly tax filings and/or DMR 111 )

- _+ Mixed income_

- ++ Income from service provision (yearly tax filings and/or DMR 112)

++ Income from commercial activities (yearly tax filings and/or other thirdparty sources)

- ++ Income from non-banking interest, commercial rent and other income (yearly

- tax filings)

- _+ Undistributed corporate profits_

56
include height which can reasonably be treated as exogenous for this purpose; there is no significant effect of height on the probability of being a widow.<sup>21</sup> Table 6 also gives regressions including the wealth quintile of the widows‘ households. While this is possibly endogenous to widowhood, it is of interest to test its significance. If widowed women come from poorer backgrounds and married poorer men then, by the same logic, they are also likely to remarry into relatively poorer households (whether they choose the levirate or not) or to join a household ─ typically that of their parents, a brother or a son ─ that is also poor. Hence, if the US type of selection into widowhood is true for Mali, one would expect widowhood to be highly correlated with living in a low wealth household. But, as can be verified in Table 6, I find no such evidence. There is no significant partial correlation between widowhood and household wealth, controlling for age, height and ethnicity. The results without controlling for wealth are

> 21  The controls are age and age squared, age at first marriage, dummies for wealth quintile, education, whether born in an urban area, height, rural or urban residence and ethnic group. The wealth quintiles are calculated by the DHS based on an index of assets owned, housing and basic infrastructure attributes generated using principal components analysis.  It is a household level index, not a per capita one.

19
# **Table C.22: Main results using husband’s data**

## **Panel A: Effects on promotions and income (Pooled waves)**

||(1)<br>Wife is<br>supervisor(Dummy)|(2)<br>Husband’s personal<br>income(Taka)|(3)<br>Household<br>income<br>(Taka)|
|---|---|---|---|
|selected|0_._26***|287_._84|475_._79|
||(0_._05)|(778_._84)|(1322_._48)|
|N|350|350|350|
|Nominated mean|0_._06|11427_._69|23436_._92|
|Nominated SD|0_._24|5098_._70|7692_._13|

## **Panel B: Effects on wife’s decision-making power**

||Decisi|on-making (Index)||
|---|---|---|---|
||Pooled waves|Wave 1|Wave 2|
|selected|_−_0_._03|_−_0_._06|_−_0_._14|
||(0_._09)|(0_._18)|(0_._11)|
|N|349|174|175|
|Nominated mean|0_._09|0_._06|0_._13|
|Nominated SD|0_._65|0_._76|0_._50|

## **Panel C: Effects on assignable goods expenditures and remittances (Pooled waves)**

||Women|Girls|Men|Boys|Remittances|
|---|---|---|---|---|---|
|||(All outcomes
**Figure 5: Female labor share, global firms vs. non‐global firms, by sector**

<!-- Start of picture text --> GVC participants vs. non‐participants<br>60%<br>50% 18<br>30<br>40%<br>16<br>30% 15<br>36 2422 17 33 19 32<br>20% 2531<br>26 20 21<br>2829 2334<br>10% 35<br>27<br>0%<br>0% 10% 20% 30% 40% 50% 60% 70%<br>Female labor share (%), GVC participant<br>FDI vs. non‐FDI firms<br>60%<br>18<br>50%<br>30<br>40% 16<br>15<br>30% 33 17 3219<br>22 24<br>20% 35 23 21 25 31 36<br>26 20<br>34<br>29 28<br>10%<br>27<br>0%<br>0% 10% 20% 30% 40% 50% 60% 70%<br>Female labor share (%), FDI<br>Female labor share (%), non‐participant<br>Female labor share (%), non‐FDI<br><!-- End of picture text --> Source: The female labor shares are averages using firm employment as weights. Sector numbers relate to ISIC Rev. 3 sectors. For the sectoral distribution of firms, see Appendix 2.

# **3.3 Differences across Countries**

This section shifts the focus to the average female labor share by country. The number of firms surveyed varies drastically across the country sample, ranging from over 6,000 firms in India,
or explored the differential effect that demographic trends may have across road user types.

The studies relying on within country data (often over relatively short time periods) are also limited in their ability to assess the relative contributions of different sets of factors to the historic decline in fatality risk. For example, the effect of changes in the motorization rate and changes in the composition of the vehicle fleet are difficult to capture without country-level panel data. This is also true for demographic factors: in virtually every country in the world the death rate due to traffic crashes is higher for persons 15-24 than for any other age group. Whether this reflects inexperience or low risk aversion, it suggests that demographic changes are likely to affect the crash fatality rate, an effect that can be studied using country-level panel data. Country-level panel data can also be used to examine factors such as medical services, which may well impact pedestrian and vehicle occupant accident victims differently. The handful of studies that have used cross-country panel data focused on explaining variation in the total number of fatalities only (Noland, 2003; Page, 2001).

To better understand how the death rate due to traffic accidents varies across road user groups, it is useful to develop a formal model of traffic fatalities, with separate equations for pedestrian and occupant fatalities.

# **3. Models of Pedestrian and Occupant Fatalities**

The expected number of traffic deaths occurring in a country each year is the product of the expected number of accidents times the probability that an accident results in a fatality. In section 3.1, we develop separate equations for the expected number of 7
are often individual high speed water tankers in use by SPSPs typically had a holding diesel generators, or simple photovoltaic (solar panel) capacity of nine cubic meters.  Many had been systems.

- **Mobile distributors** Across all countries studied,

- water tankers in use by SPSPs typically had a holding capacity of nine cubic meters.  Many had been purchased second-hand and may have transported other goods in the past.

# **4.3 Staffing**

Most SPSPs have flat structures with less than two layers between management and the client. Many owner/managers know their clients and are therefore considered more responsive by consumers. Most SPSPs reviwed in the literature had fewer than 50 employees— the majority of the reports for which employment patterns were detailed indicated that there were fewer than 10 employees (see Table 7). However, despite their small scale, SPSPs can play an important role in job creation. Data on water-supply vending activity in five cities (Port-au-Prince in Haiti, Dakar in Senegal, Nouakchott in Mauritania, Kayes in Mali and Bobo Diaulasso in Burkina Faso), indicates that the number of people employed in vending activities represents two to four per thousand people, or 1 to 3 percent of regular jobs in those cities [Water and Sanitation Program, 2000].

## **Table 7. Typical Staffing Profile  of SPSPs**

- **<u><mark>Water Electricity</mark></u>**

- • The number of staff  typically employed by SPSPs • **Grids or networked systems:** The Cambodia Rural involved in water supply ranges from two to four Energy Enterprise survey is one of the few pieces of employees, for **PNOs** serving fewer than 800 research that has sampled staffing questions for households.  The owner is typically the manager and electricity SPSPs.  There, the average number of fulleither relies on family members or employs a handful time employees per SPSP was found to be three, with a of additional staff on
**Table A6: Comparing household and HH head indicators in the short panel (2009-2012) dataset between non-refugee hosting and refugee hosting districts (including Kampala)**

||**Mean**||**_In the base_**<br>|**_wave=2_**|**_009/10_**||**Mean**||**_In the last_**<br>|**_wave=2_**|**_012_**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**(Non-**<br>**hosting)**|**Obs.**|**Mean**<br>**(Hosting)**|**Obs.**|**Mean Diff.**|**Std.**<br>**Error**|**(Non-**<br>**hosting)**|**Obs.**|**Mean**<br>**(Hosting)**|**Obs.**|**Mean Diff.**|**Std. Error**|
|HH welfare(consumption<br>aggregate per adult equivalent)|56666.446|1835|82389.101|394|-25722.655***|3317.328|60848.386|2008|88149.285|430|-<br>27300.899*<br>**|6505.562|
|Rural locations|0.823|2026|0.565|432|0.258***|0.021
sup>16</sup> legal entities dominated in the lease market, accounting for 94% of all transactions and 92% of the land transacted in the period (table 1 panel B). Of all the land parcels leased, 81.4% were categorized as land for commercial and 18.6% as land for personal use. By contrast, as legal entities were not allowed to purchase agricultural land before 2024, 95% of land sales transactions that account for 94% of the area transferred in land sales markets were by individuals or FOPs and 42% of sale transactions (accounting for 22% of area transacted) involved personal rather than commercial land. _Ceteris paribus_ , legal entities pay about a third more for leasing or buying land.

Monthly data provide a more granular picture: As figure 2a illustrates graphically, the number of monthly sales, which had reached almost 30,000 in December 2021, dropped to zero due to the war in early 2022 and then gradually recovered to about 10,000 in March 2023 and 15,000 by March/April 2024. Once legal

> 12 The date of parcel acquisition is used to determine whether the tenant’s or buyer’s farm was operational in the year before the transaction as well as the size of their operational holding.

> 13 The most immediate reason is that even if land can be used as collateral for loans, in the Ukrainian context, the transaction costs of doing so for individual farmers are prohibitive. The absence of a response to the opening of land sales markets to legal entities and the low number of mortgages even for non-agricultural land (with a total of 9,289 mortgages involving residential, recreational, or industrial land registered in the entire period since January 2021) points to more deep-rooted regulatory or institutional issues.

> 14 To fend off potential cyber-security threats, all land-related digital registries were taken offline immediately after the Russian invasion with only leases allowed to be registered locally on paper using simplified procedures.

> 15 26%