rafmacalaba/data-use-mentions-extended huggingface dataset:
0.321|2.208|0.307|
|Observations|1589|1|743|3332|
|Number of months|7||7|7|
|Number ofphone numbers|227|2|49|476|

Notes: This table displays data for 476 Airtel phone numbers reported by our survey respondents. We do not include other phone numbers in the Airtel transactions data since we do not have the necessary information to map these to our study locations. Column 5 shows the p-value of the difference in treatment and control group means, clustered at the EA cluster level and conditional on strata and month dummies.

22
violent activities (Besley et al., 2019), and public support for foreign military interventions (Fetzer et al., 2021). Our paper focuses on news media coverage and sentiment about economic reforms in the GCC.

We use newspaper data from the Dow Jones News Snapshot API. This global news database provides access to newspaper articles from thousands of sources in 28 languages. In addition to the full text, it also includes tags for each document, which identify the following: (i) country or countries discussed in the article, (ii) the country of origin of the newspaper, and (iii) the subject(s) of the articles. For this paper, we carefully select articles in the 28 languages which contain terms relating to "economy" and "reforms" for each of the six GCC countries (see Appendix A4). Our methodology yields 138,782 articles that are tagged to one or more of the six GCC countries. Of these, 46% are in English, 39% are in Arabic, and the remaining 15% are in 26 other languages. Therefore, this paper focuses on English and Arabic media.

# IV.1 Engagement in Discussions on Economic Reform the from News Media

We begin by analyzing the evolution of reform coverage across the GCC countries. We find that the number of economic reform articles varies significantly over time and across countries. To address this, we create an index for each country that captures _the percentage of all news articles that discuss economic reforms_ . In other words, we normalize the number of economic reform articles about a country by the total number of articles about that country. For country in year , the index has the following form R R R R I = 100 ×ii E E E tt <u>,</u> ~~.~~ iiEE R RREERREE AARRttiiEEAARRssii tt CChaat IIRRIIii,tt TTEEttTTAA AARRttiiEEAARRssii,tt The reform chatter index can be classified by source: local news (e.g., UAE
://rgdoi.net/10.13140/RG.2.1.3241.0962.

- ICIMOD, 2013. Land cover of Nepal 2010 [Data set]. Available from https://doi.org/10.26066/rds.9224.

- Jacoby, H.G., 2000. Access to Markets and the Benefits of Rural Roads. The Economic Journal 110, 713–737. doi:10.1111/1468-0297.00562.

- Jafino, B.A., Kwakkel, J., Verbraeck, A., 2020. Transport network criticality metrics: a comparative analysis and a guideline for selection. Transport Reviews 40, 241–264. doi:10.1080/01441647.2019.1703843.

- Jalan, J., Ravallion, M., 2002. Geographic poverty traps? A micro model of consumption growth in rural China. Journal of Applied Econometrics 17, 329–346. doi:10.1002/jae.645.

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- Jia, H., Ord ́o ̃nez, F., Dessouky, M.M., 2007. Solution approaches for facility location of medical supplies for large-scale emergencies. Computers & Industrial Engineering 52, 257–276. doi:10.1016/j.cie.2006.12. 007.

- Khan, S.A., Rehman, S., 2013. Iterative non-deterministic algorithms in on-shore wind farm design: A brief survey. Renewable and Sustainable Energy Reviews 19, 370–384. doi:10.1016/j.rser.2012.11.040.

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sup> In the paper, we analyze the adoption and use of technologies in Senegal through three key angles: (1) standard measures of technology related to general purpose technologies; (2) use of technologies applied to general business support functions; and (3) use of sectorspecific technologies. The standard firm-level measures of technologies refer to “traditional” measures of general-purpose technology (GPT) adoption, which enable firms to apply more technologies in non-specific tasks. It includes the access and usage of GPTs such as electricity, phone, computers, internet, cloud computer and digital platforms. We define general business support functions (GBF) as those tasks necessary in any firm, regardless of the sectors they are in, such as business administration, production planning, sales or payments methods. The sector specific business functions (SSBF) are those applied for business functions that are industry specific (e.g. land preparation in agriculture industries, or input testing in the

> 1This paper is part of a series of studies analyzing country-specific data from the FAT survey. Cirera et al. (2021b) and Cirera et al. (2021a) describes the results for Vietnam and the state of Cear ́a, in Brazil. Cirera et al. (2020) provides a cross-country analysis and provides further details about the FAT survey.

> 2See section Appendix C for more details about sampling and data in Senegal.

2
model specifically designed for the distributional analysis of subsidies and the simulation of subsidy reforms. The model is called “SUBSIM” (Araar and Verme 2015).

The analysis consists of the following steps. First, we update household expenditure from HEIS 2018/2019 by applying projected nominal GDP per capita growth and the poverty line by projected increases in the Consumer Price Index in order to obtain expenditures and the poverty line in 2019/20 prices, from which can be obtained a forecasted poverty rate for this period.<sup>2</sup> Second, we analyze the distribution of gasoline expenditures and quantities consumed across the household consumption distribution in the Islamic Republic of Iran. Third, we simulate direct and indirect impacts from the increase of gasoline prices on population well-being, poverty and inequality using the publicly available SUBSIM Stata package (Araar and Verme 2012). Finally, we assess the impact on government expenditures and revenues after the reform, including the cost of any mitigation measures.

2. Preparing the microdata and initial parameters 2.1 Forecasting household budget expenditure and initial parameters The Household Expenditure and Income Survey (HEIS) conducted by the Statistical Center of Iran in March 2018-March 2019 is used to analyze the potential impact on poverty and inequality from higher gasoline prices and reintroducing a rationing system. In order to have the most up to date poverty and expenditure numbers, we forecasted poverty for the period March 2019-March 2020. This is done by adjusting household expenditures by the estimated nominal gross domestic product (GDP) per capita growth rate and the poverty line by the estimated increase in the Consumer Price Index (CPI). Inflation is assumed to be 35 percent; GDP per capita nominal growth is predicted to be around 32 percent and population growth around 1 percent (World Bank staff estimates before the breakout of COVID-19). Resulting daily expenditure and population numbers are shown in Table 1.

Table 1. Population and daily expenditures in IRR in 2019/20, forecasted numbers

||**Population,**<br
10 data on actual wage and price movements (to estimate **_1)_** and consumer income and expenditure surveys (to estimate 9*). This section brings together results from various sources to determine for Bangladesh the directions and magnitudes of the rural welfare effects of a change in the price of staple foodgrains under induced wage responses.

In a companion paper to this one, Boyce and Ravallion (1987) have estimated a dynamic econometric model of agricultural wage determination in Bangladesh over the period 1949-50 to .980-81. The long-run equilibrium of their model can be interpreted as a log-linear approximation of equation (11). This was embedded within a short-run dynamic process which permits sluggishness in wage adjustment and lags in response to changes in the market clearing wage rate. The long-run agricultural wage rate was assumed to depend on the prices of rice (also interpreted as a proxy for other staple foodgrains), cloth and jute, the manufacturing sector wage rate, agricultural yields per acre, and a quadratic function of time. The long-run equilibrium was found to be homogeneous of degree zero in all nominal prices. The fitted model performed well by all diagnostic tests performed on its residuals (including the Durbin-Watson, Box-Pierce, and Breusch-Godfrey tests for residual autocorrelation, Engle's test for ARCH type heteroscedasticity and the Jarque-Bera test for normality; for details see Boyce and Ravallion). Checks were also madc for simultaneity bias using a generalized instrumental variables estimator in which only the lagged values of wages and prices were included in the set of instrumental variables. A model was also estimated in which the manufacturing wage rate was assumed to be influenced by the price variables, so as to pick-
those overlapping locations before the other three MNOs, we compared the coverage of MNO-4 with the coverage of the other operators in 2012. This highlighted nine enumeration areas (less than 1%) from the GHS data set that were covered only by MNO-4. This is also likely to be an overestimate as MNO-4 will have had lower coverage in 2012.  These tests therefore give us confidence that the lack of data for MNO-4 has a small effect, if any, on the accuracy of our coverage data set.

5
a close relation play a critical role in facilitating or discouraging access to formal support services. Data from Brazil shows that women who seek help are much more likely to do so from family and friends than from formal institutions. Research from Foundation Perseu Abramo (2010) shows that help-seeking reaches one-half to two-thirds of domestic violence cases following threats or physical violence, with the majority of women reporting to mothers, sisters and other relatives. Of the 20 dimensions of violence investigated, mothers are the primary source of help-seeking in all but 4 dimensions.<sup>16</sup> Social norms suggesting toleration of abusive relationships and, more generally, relegating the phenomenon of domestic violence and its response to the private sphere are still quite prevalent in Brazil as in several other contexts. These norms play a significant role in constraining survivors in their search for support and in their effective access to services (Perova et al. 2013). Research by IPEA (2014) investigating perceptions related to VAW in Brazil finds that 42.7 percent of respondents agreed completely with the phrase “A woman that is battered by her partner continues in the relationships because she likes to be beaten,” and 22.4 percent of respondents partially agreed with the phrase. Furthermore, a common saying in Brazil - _“Em briga de marido e mulher – ninguém mete a colher”_<sup>17</sup> – reflects attitudes and behaviors defined by social norms that may influence family and peers within a survivor’s informal support network: namely, that conflict between men and women, especially between spouses, is private, and outsiders should not interfere. These findings are also consistent with population-based survey data from other Latin American countries, which find that substantial proportions of women did not agree that

> 15 Landenburger (1989) argues that women who have become entrapped in abusive relationships go through a four-stage recovery process, which includes periods of denial, self-blame, and endurance before they are able to recognize the abuse and identify with women
|TV ownership|Share of men in delegation _d_ that own a TV|The 2014 Tunisia Census of Pop-<br>|
|---|---|---|
|(rate)||ulation and Housing (INS)<sup>[</sup><sup>_a_]</sup>|
|Unemployment|Share of men in delegation _d_ that are unemployed|The 2014 Tunisia Census of Pop-<br>|
|(rate)||ulation and Housing (INS)<sup>[</sup><sup>_a_]</sup>|

_Notes:_ [a] The 2014 Tunisia Census of Population and Housing was conducted by L’Institut National de la Statistique (INS). [b] Tunisia administrative boundaries for delegations are obtained from The Humanitarian Data Exchange (HDX) and produced by The United Nations Office for the Coordination of Humanitarian Affairs (OCHA). Date of access: March 20, 2019 [link]. [c] The National List of Persons, Organizations and Entities Associated with Terrorist Crime is produced by La Commission Nationale de Lutte Contre le Terrorisme (CNLCT, 2021). Date of access: April 19, 2021 [link].[d] Elections in Tunisia are conducted by the Instance Sup ́erieure Ind ́ependante pour les Elections ISIE (2015). Date of access: September 20, 2021 [link]. [e] The Ansar al-Sharia in Tunisia’s Clerical Establishment dataset is described in Zelin (2020). Date of access: March 25, 2021 [link]. [f] The Mountains of the World 2002 dataset, produced by The UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), is described in UNEP-WCMC (2002). Date of access: April 28, 2021 [link]. [g] The Armed Conflict Location and Event Data Project (ACLED) is described in Raleigh et al. (2010) [h
<!-- Start of picture text --> Concentration Index - All Interventions<br>Special Education = -0.7)<br>Tertiary Education Ms (0.24<br>Secondary School -0.16<br>Primary School -0.23<br>Pre-School -0.24<br>Education in-kind Transfers -0.15<br>Community Center -0.07<br>Inpatient Care Mmm 0.12<br>Outpatient Care Mm 0.10<br>Health in-kind Transfers mm 0.07<br>In-Kind Transfer -0.04 &<br>VAT 0.42<br>Indirect Taxes 0.42<br>Personal Income Tax (PIT) es (0.87<br>Direct Taxes es 0.87<br>All Taxes 0.52<br>School Feeding Program -0.31<br>SEED -040<br>Transfers -0.39 DE<br>-1.00 -080 -060 -040 -0.20 0.00 020 040 060 0.80 1.00<br>m Concentration Index Baseline<br><!-- End of picture text -->
|
|Never enrolled|0.012|0.015|0.019|.518|.234|
|Observations|1,621|4,954|1,708|||

_Notes._ Column 1 shows baseline means among females in Dodoma and Iringa in the current study sample, collected in 2016. Columns 2 and 3 show baseline means (before ELA clubs were established) among females in the Buehren et al. (2017) study, collected in 2009. The Buehren et al. (2017) sample comprises a random sample of adolescent females from the same communities as the current study in Dodoma and Iringa. Buehren et al. (2017) treatment communities are where ELA clubs were eventually established. Column 2 shows means using the entire baseline sample from Buehren et al. (2017). Column 3 shows means using baseline data restricted to the Buehren et al. (2017) control communities. Column 4 presents the p-values from a test of equality between the means in columns 1 and 2. Column 5 presents the p-values from a test of equality between the means in columns 1 and 3. The latter comparison is made to alleviate concern that communities where clubs are established are different from surrounding communities. Age is age in years; Married is an indicator that the female is married; Own house is an indicator that the female’s household owns the house she lives in; Has child is an indicator that the female has a child; Ever had sex is an indicator that the female has ever had sex; Use condom is an indicator that the female uses a condom during sexual intercourse; Highest grade is the highest grade completed by the female; Dropout is an indicator that the female dropped out of school; and Never enrolled is an indicator that the female was never enrolled in school. _Source._ Column 1: Female baseline data, restricted to females in Dodoma and Iringa. Columns 2–3: Buehren et al. (2017) female baseline data.
Our dataset differs from ones used in the earlier survey based work by Ng et al. (1999), Giannetti et al. (2011) and others in several important ways. First, we use data from actual trade credit contracts rather than data based on survey responses, thereby mitigating the usual misreporting concerns associated with survey based data. Importantly, our dataset consists of trade credit contracts signed, while papers using the National Survey of Small Business Finance (NSSBF) database use trade credit contracts offered. Our dataset also covers a broader set of industries that includes technology firms, allowing for an analysis of trade credit contract terms across a broader range of industries, and includes trade credit terms not only for U.S. firms but also for international firms, allowing for a comparison of trade credit terms across different jurisdictions. Moreover, our dataset covers suppliers of all sizes, and not just small firms as in the SSBF. This is an important difference because the credit terms offered by large firms could be very different than those by small firms.

We begin by summarizing the main characteristics of buyers, suppliers, and contracts. Panels A and B of Table 1 show summary statistics of buyer and supplier characteristics. First, the buyers in our sample are very large – 33 out of 56 (or 59%) of buyers have over US$ 10 billion in sales and only one buyer has less than US$ 2 billion in sales.

The buyers are also creditworthy as measured by whether or not they are investment grade – about 75% of buyers in the dataset are investment grade. That we have mostly large, investment-grade buyers will make it harder to find evidence in this dataset of a financing motive for trade credit. We should, therefore, treat our results with appropriate caution.

Buyers are active in a range of industries, with the majority in retail industries. The sectoral distribution of buyers is: 16% in auto manufacturing, 13% in diversified retail, 29% in diversified manufacturing, 7%
unemployment rate is 16% among those with postsecondary education but virtually zero among those with no and primary education.

> 5 For reviews of advantages and disadvantages of using online labor market data, see Einav and Levin (2014) and Kureková et al. (2015).

> 6 For example, Deming and Kahn (2018) analyze skill demands by examining text information in online data.

> 7 For example, the use of an online job portal is already common in India (Nomura et al., 2017).

2
# **3 Quantifying the Impact of a Drought on Stunting**

## **3.1 Relationship between Droughts and Stunting**

In what follows, we document two important characteristics of the relationship between drought shocks and child stunting. First, we show that our measures of drought shocks are causally related to stunting and other child malnourishment outcomes. Second, we show that the average magnitudes of these impacts are comparable to the impacts associated with differences in wealth status, as well as with differences in a mother’s literacy level.<sup>20</sup> It is possible that droughts occur more frequently in areas that are less developed and that are characterized by a greater prevalence of child malnourishment. The most disaggregate spatial unit in the Demographic and Health Surveys (DHS) is the cluster. This is a village or a small collection of villages. By employing cluster x month fixed effects, we control for time-invariant influences at the cluster level (such as variation in remoteness, local governance and social structures, and in agro-ecological factors such as altitude and soil quality) interacted with the factors related to the month (harvest cycles, festivals, and school calendars). The drought coefficients are identified by variation in the incidence of shocks across years.

When we discuss controlling for month effects, we refer to the month that a child was born. This is measured with error (Larsen et al. (2019)). In the appendix (see Tables A1 and A2), we show that our results are robust to removing children born at the start of the year (January and February), the end of the year (November and December), children with birth months corresponding to whole years, and including _age_ and _age_<sup>2</sup> as explanatory variables.

Table 2 describes results when child outcomes are anthropometric regressed against droughts, with cluster x month fixed effects. The sample includes rural populations of five Southern African countries (Mozambique, Malawi, Tanzania,
**6.7. Mechanism Analysis:** We conducted exploratory analysis to examine the mechanisms behind the effect of the bundled treatment that systematized inquiry on risk, reward and progress. We examined whether the differential effect in score by treatment and gender was driven by two mechanisms: the consistency of inquiry – by likelihood of asking a prevention-focused question to a startup (H2); or the investors’ assessment of dynamic improvement when evaluating startups (H3).

We reran the analysis from Stage 1 on the panel dataset and found that during the VilCap program (when VilCap provided due diligence questions for trainee investors to use), female founders were equally likely to receive prevention-focused questions as males, so the mechanism could not work as predicted (see Appendix F). Prompting investors to inquire about risk and reward had similar results to Stage 1, where males were asked more prevention-focused questions, and this effect was lower for females, but this did not affect the score. These results did not provide evidence in support for H2 in this setting, where investors and founders interacted repeatedly over a long period of time. Combining these results with results from Stage 1, we suggest two explanations. First, prompting systematic inquiry around risk and reward may be most valuable to retain startups presented by female founders at early stages in the evaluation pipeline, to keep them in consideration for the further selection process. Second, any prompt to systematically inquire (either from Vilcap during its program, or from the intervention we designed) may have a substitution effect. Both prompts increased scrutiny for startups led by all founders, but particularly for startups with all-male teams.

We next assessed whether investors’ assessment of dynamic improvement differed by treatment and control groups (H3). As shown in Appendix G, treated investors weighted the criterion of “improvement” as a higher part of their evaluation criteria (20.8%) than control investors (18.5%). This difference was close but not statistically significant at p = 0.
to be too high. Based on relatively high-quality US data, Gonzalez RR xx and Miguel (2015) suggested that RR m should not be greater than 0.9. An m of 0.89 was xx xx therefore chosen for our analyses. RR RR

# _Fertilizer Profitability_

We measure fertilizer profitability by calculating the Marginal Value Cost Ratio (MVCR) which represents the extent to which farm income will increase if the rate of nitrogen application increases. MVCR is expressed as follows: N M (6) NN ∗ PPYY MMRR= PPNN where N is the nitrogen use efficiency; and and are the prices of maize and nitrogen YY NN respectively. Maize prices change over time and space, so three prices of maize are considered in MM PP PP the profitability analyses – i) the farm gate price at harvest, ii) the lean season market price, and iii) the import parity price. All three of these prices are considered in order to account for potential 14
4|
|Monetary tightening; Δ in velocity|44|-46.0|23.9|-11.6|-9.4|12.6|36|-27.9|12.6|-9.7|-9.7|8.3|
|<br>Capital controls|44|1.00|5.00|2.00|2.09|0.83|36|1.00|5.00|2.00|2.03|0.94|
|<br>Initial level of income, share of G7|44|-2.54|1.81|0.09|-0.57|1.60|36|-2.54|1.13|-1.50|-0.72|1.78|
|Oil trade balance as share of total exports|44|0.03|0.28|0.07|0.09|0.06|36|0.02|0.30|0.14|0.13|0.08|
|Oil prices, index|44|11.6|40.0|23.8|25.2|10.1|36|11.6|28.6|13.3|15.3|5.0|
|Vulnerability index, VIX|44|-8.2|63.2|0.0|10.3|18.2|36|-7.4|29.7|-2.7|-0.8|8.8|

Sources: IMF’s World Economic Outlook database and authors’ calculations. Notes: Only the observations in the estimation sample for annual data are included in this table.

- <u>Policy variables. This category is self-explanatory and is the main area of focus of this paper