rafmacalaba/data-use-mentions-extended huggingface dataset:
level|Haver Analytics|
|_Rit_|Ex-ante real interest rate|Level|Haver Analytics; OECD|
|_Cit_|Real private consumption expenditure, seasonally adjusted<br>|Log Level|Haver Analytics|
|_Iit_|Real gross fxed capital formation, seasonally adjusted<br>|Log Level<br>|Haver Analytics|
|_ERit_|Real efective exchange rate, CPI-based|Log frst diference|Bruegel; Haver Analytics|
|_Sit_|Equity prices|LogLevel|Haver Analytics|

> 6The VAR model assumes that fiscal policy makers require at least one quarter to respond to output shocks.

11
41 radio access on school fee payments. Households pay fees for a larger share of children in villages with greater radio access. Because of noisy responses to this question, the effect is not significant. However, when household listenership is instrumented with radio access, the effects on fee payments are large and significant: households that listen to community radio pay informal school fees for one additional child.

The results in this section are notable in demonstrating for the first time the channels through which media access operate. First, we document that a particular kind of media access, community radio, is strongly associated with greater village exposure to information about education and health. Second, access to this information does not increase government accountability: it is associated neither with greater government inputs into local schools nor with greater knowledge about government policies that affect those inputs. Third, though, media does increase household investments in education. Moreover, after isolating the exogenous component of household listening behavior explained by radio access, households that listen more to community radio also invest more in their children‘s education.

# **8. Conclusion**

The analysis here contributes to the fast-growing literature on the role of media and information in influencing household behaviors and government accountability. We use unique data from Benin to shed light on several issues that, because of data limitations, were beyond the reach of earlier research. First, the results demonstrate for the first time a substantial effect of media access on education performance: the literacy scores of eight and nine year-olds are significantly higher in villages with greater access to community radio.

Second, the data permit the channels through which media access matters to be separately investigated. Prior research has not been able to document whether media actually broadcast relevant information. The data allow us to verify that one type of radio, community radio,
where _L_ is the total loss; _PV_ is the present value of lost earnings; _Y_ is the mean annual earnings; _α_ is an adjustment factor to account for the part of the school year when schools were closed; _r_ is the rate of return on one year of schooling; _S_ is the total number of students; and _β_ is an adjustment factor to allow for some distance learning during the period of school closure.

Previous studies have documented average rates of return to schooling by income level. The returns vary by country and country group, but not by very much (see Table 1).

<u>Table 1: Returns to Schooling (Percent)</u>

|Incomegroup|Returns|
|---|---|
|Low|9.3|
|Middle|9.2|
|High|8.2|
|World|8.8|

_Source_ : Psacharopoulos and Patrinos (2018) We use an even more conservative estimate and assume that every additional year of schooling equates to about 8 percent in additional future earnings.<sup>44</sup> Therefore, the returns to education estimate, _r_ , that we use here is 8 percent. We then use the number of months of education closures to estimate the loss in marginal future earnings. For example, if _Country X_ closes its schools and universities for four months, – that is, 1/3 of the school year – then the loss in marginal future earnings would be 2.64 percent per year over a student’s working life. Therefore, our earnings adjustment for _Country X_ , α, is 2.64 percent.

We use mean annual earnings, _Y_ , of employees in 2011 PPP dollars from ILO or the World Bank JoIn database. The data is country-specific where available; otherwise we calculate the average for a country's income class. The ILO (2020) data are from the ILOSTAT database <u>(https://ilostat.ilo.
Table B9: Platform Survey (cont.)

|**Variable**|**Defnition**|
|---|---|
||_First and second order beliefs_|
|Own belief<br>Belief about women|Respondents are frst asked how likely they believe women who<br>ride the public space are to accept advances, compared to women<br>who ride the reserved space. They are then asked what men and<br>women believe.|
|Belief about women|Constructed variables are = 1 if the response is either “More<br>likely” or “Much more likely”.|
|Tabl<br>**Variable**|e B10: Implicit Association Test<br>**Defnition**|
|D-Score on Gender-Career IAT<br>D-Score on Advances IAT<br>D-Score on Safety IAT<br>Female respondent|Normalized diference in average response times between the<br>“stereotypical” and “nonstereotypical” paired tests for each in-<br>strument.<br>= 1 is respondent is female|
|Finished all IAT instruments|= 1 is respondent has valid scores for the gender-career, the ad-<br>vances and the safety IATs|
|Male respondent|= 1 is respondent is male|
|earthquake<br>wealth<br>quintile|household<br>s|%<br>Assets<br>losses<br>(June)<br>(1)|% Assets<br>losses<br>(February)<br>(2)|Variation<br>(1) – (2)|Main assets lost<br>(February)|% Agric.<br>assets<br>losses<br>(June)|Main agric.<br>assets lost<br>(June)|
|---|---|---|---|---|---|---|---|
|Poorest|250|19.6|17.6|-2|Radio,<br>cell phone, iron|12.1|Poultry, goats,<br>swine, cattle|
|2|259|39.0|38.2|-0.8|Television, radio|22.2|Poultry, swine|
|3|239|57.3|51.9|-5.4|Television, radio|31.8|Poultry, swine|
|4|254|74.0|79.5|5.5|Television, radio<br>Television,|6.7|Poultry, swine|
|Richest|244|70.5|86.5|16|radio,<br>fan|0.0|Poultry, goats|
|Total|1246|51.9|54.6|2.6|Television, radio|15.8|Poultry, goats,<br>Swine|

Source: Own computations using June 2010 (EFSA II) surveys. Notes: The sample is restricted to the households residing in the six strata that cover areas directly affected by the earthquake. *Among households
sector and interact electronically with potential farmers. Farmers can carry out the required actions digitally rather than by filling paper forms, including uploading scanned documents and photos or providing authorization for providers of certain services to access specific types of personal information stored on the system. Use of the SAR to implement an EU-supported producer support grant (PSG) as well as other programs<sup>15</sup> facilitated rapid sign-up, especially by small and medium-scale farms, many of which had previously been in the informal sector.<sup>16</sup> As the survey was launched in the second half of October, we set October 15, 2022, as the cut-off date to construct the sample frame by dropping farms without any registered land or a valid phone number. Appendix table A6 compares the resulting sample frame with all the registered farms in the database (Form 29) maintained by the SSSU in 2020, the last year for which such data are available, by farm size category (<50, 50-120, 120-500, and >500 hectares (ha)).

Panel A in appendix table A6 compares the number of establishments, showing that our frame includes 75,571 farmers, more than double the 36,167 in the Form 29 survey as used by the SSSU. Of these, 21% are legal entities, 11% FOPs, and 68% individuals, with the latter concentrated in the farm size class of fewer than 50 ha, of which 82% are individuals and 9% each FOPs and legal entities. Legal entities dominate in the larger farm size classes, making up 58%, 76%, and 97% of those in the 50-120, 120-500, and >500 ha groups, respectively. The SAR has better coverage, in terms of the number of farms, compared to Form 29, for the below 120 ha group (with 3.8 and 1.1 times the number of farms in the <50 and 50-120 ha groups, respectively) but worse coverage for
mark>_ <mark>2020)</mark> as well as crops’ differential nutrient requirements (Bowles _et al._ 2020). Together with data on prices for inputs and outputs, technical coefficients based on the varietal choice and crop management options (e.g., till- and no-till practices), availability of on-farm resources -most importantly labor in different parts of the growing season- and their shadow prices, as well as any exogenous constraints (e.g., regulation) can then be used to formulate decision-rules for optimum rotational choices at the farm level as a programming model (Boyabatli _et al._ 2019).

Whether such choices are not only individually but also socially optimal requires more detailed analysis. If property rights to land are well defined and can be enforced and the effects of crop rotation remain plotspecific, privately and socially optimal choices will coincide. However, even if property rights are clear, externalities may arise in several respects. Spatial externalities, e.g., because mono-cropping or short rotations can trigger accumulation of insects that may infect neighboring fields or, if a critical mass is exceeded, cause harmful infestation more broadly (Meisner and Rosenheim 2014). Effluents from more intensive use of fertilizer, pesticides, or manure than would be necessary with more diverse rotations may contribute to water pollution. Systematic overuse of chemicals can lead to pests developing resistance, as has been reported for GM crops (Qiao _et al._ 2017) and glyphosphate (Mitchell 2011). Moreover, if property rights are insecure, land users may maximize short-term benefits, e.g., ‘mine’ soil, without regard to long-term sustainability.

If supply is price elastic, modest price changes or subsidies may be sufficient to make farmers shift from monocropping to rotations. Hennessy (2006) shows that, depending on prices for maize and fertilizer, a 4
questions, choice of the sample and the demand features such as user‐charges and connection costs provides the ‘control’ that mimics an experiment. The ‘sterility’ of laboratories is replaced with field relevance by implementing the survey in the home environment of a typical user of the piped water service with questions that reflect service delivery programs that are both familiar to respondents and being considered by the service provider ‐ the National Water Supply and Drainage Board (NWSDB).

The questions used in WTP survey can be either open‐ended or closed‐ended. In an open‐ ended question, the respondent is asked to state the monetary amount that he or she is willing to pay for the service that is being valued. With a closed‐ended CV question (also referred to as a “dichotomous choice” or “referendum” question), the respondent is asked whether he or she is willing to pay a specified rupee amount for the good being valued. The respondent then answers yes or no. Open‐ended questions, despite the fact that they provide more information than closed‐ended questions, can be especially difficult for respondents to answer because individuals are typically not accustomed to performing such tasks in daily‐life decision making. Therefore, we will use a closed‐ended approach to elicit WTP for improved WSS services. Although easier for respondents to answer than open‐ended questions, this type of question may not reveal the respondent’s maximum WTP. The response to the closed‐ended question provides a bound on the respondent’s WTP, and the average WTP for respondents is estimated using a random utility modeling (RUM) framework and econometric models (Hanemann, 1984; Cameron and James, 1987; Cameron, 1988).

As stated above, we ask households to consider two scenarios: the improved service with certain quality and cost attributes, and the status quo. RUM model assumes the utility associated with a particular choice alternative is expressed as a function of individual characteristics and the attributes of the
necessary to reach this target in each country, assuming to begin with an unchanged distribution of consumption within countries and modeling population growth until 2050 according to UN forecasts.<sup>26</sup>

The per capita GDP growth needed to reduce extreme poverty to 3% ranges from zero to nearly 600% -- a sixfold expansion of the economy (Figure 1, panel A). Non-poor countries require zero growth as the poverty target is already reached. North America is the only region in which all countries have achieved this poverty target, while countries in Sub-Saharan Africa need to grow on average by 215% to reach it (Figure 1, Panel B). Targeting higher poverty lines ($3.65, $6.85 – poverty lines typically of lower and upper-middle income countries respectively<sup>27</sup> ) requires more growth in more countries than targeting extreme poverty, as more countries are considered poor against higher poverty lines (zero to 1117% at $3.65; zero to 2251% at $6.85; Extended Figure 5). A minimum consumption level of $15 per day in all countries would need per capita GDP growth ranging between zero (in 28 countries) and 5140% (Extended Figure 5). We show the implications of targeting poverty rates other than 3% (5%, 4%, 2%, 1%, 0%; Extended Figure 13).

**With historical energy-intensity patterns, how much energy is required for economic growth?** To link GDP growth with greenhouse gas (GHG) emissions, we first relate GDP per capita to energy consumption per capita and then relate energy consumption per capita to greenhouse gas emissions. For this, we combine GDP data from the World Development Indicators (WDI) with data from the Energy Information Administration (EIA) on primary energy consumption and with data on GHG emissions from Climatewatch/CAIT, all for 2010 to 2019 (the latest year the data are available)
the end of the interview. In our data of 1,417 respondents, 46 enumerators classified 1,071 respondents (75.51%) as “very truthful”, 341 (24.06%) “somewhat truthful” and 6 “somewhat untruthful” (0.42%). On the surface, these subjective evaluations provide us the ability to identify potentially untruthful respondents.

Unfortunately, this strategy is subjective to measurement error. In our data, we find that that the vast majority of cases of “somewhat truthful” or “somewhat untruthful” are concentrated in one region, Chittagong. While this region contains an important port city which could lead to higher levels of corruption, we find that across the region and across industries within Chittagong, firms are much more likely to be rated as less than very truthful. This stark regional pattern could lead researchers to infer that the region, or the type of firms in the region, are more prone to false response.

Our survey, like many firm level surveys, is organized by region. Thus teams of enumerators, supervised by a single individual, are assigned to survey firms within a single region. Chittagong’s team surveyed 264 firms, where each of the six enumerators individually surveyed between 33-52 firms. While the other five teams evaluated 75%100% of firms as “very truthful” the Chittagong team only evaluated 17.80% as very truthful, and 81.06% as “somewhat truthful” and the remaining 1.14% as “somewhat untruthful.” Given the lack of theoretical reasons to suspect such high levels of systematic lying in this one region we suspect that this subjective perception suffers from serious anchoring problems. The Chittagong team is likely to evaluate firms as “somewhat truthful” while the other teams are more likely to evaluate firms “very truthful.” Thus the 16
exclude expenditures on taxes and ceremonies. These consumption categories are not typically included in consumption aggregates due to their idiosyncratic nature and infrequency.

The direct comparison of results from the different survey modules sheds light on the relative influences of the various reporting errors discussed in Section 2. Since the frequently supervised personal diary is believed the most accurate in that it minimizes the magnitude of recall lapse, telescoping, and missed individual consumption, comparisons with this benchmark will be of particular use in understanding the net impact of the different reporting errors in each of the

> 18 For non-purchased products, we used the first available unit price in the following order: (i) Household-specific unit value, only available if household also purchased item (values more than five standard deviations from the district median price were replaced with district medians); (ii) EA median unit value; (iii) District median unit value; (iv) Overall median unit value; (v) EA median price from price questionnaire; (vi) District median from price questionnaire; (vii) Overall median from price questionnaire; and (viii) A price assigned by EDI staff, using best guesses based on the prices of similar items or units in the EA or district - only a handful of consumed items were valued in this way.

> 19 Forgotten items refer to the following. At the end of each of the 2 weeks of the three diary variants, the household is asked about the three main foods eaten daily and then the diary is reviewed to ensure that acquisition of these foods (including from stocks) are recorded. If these foods were omitted in the diary by mistake, the information is then collected by the enumerator (effectively in the form of recall).
Benchmark priors for _c_ = 1 (solid), _c_ = 0 _._ 1 (dashed), and _c_ = 0 _._ 01 (dotted) <!-- Start of picture text --> �8<br>c � 1 20<br>�9<br>�10 c � 0.1 15<br>�11<br>�12 c � 0.01 10<br>c � 1<br>�13 c � 0.1<br>5<br>c � 0.01<br>�14<br>�15 0<br>0 500 1000 1500 2000 2500 0.9990 0.9992 0.9994 0.9996 0.9998 1.0000<br>g ∆� g ��1� g �<br>�� gp ∆�� p<br>log log<br><!-- End of picture text -->

**Fig. 1.** Beta Benchmark Priors for _g_ and _δ_ ( _n_ = 72, _k_ = 41) Vertical lines correspond to _g_ = max _{n, k_<sup>2</sup> _}_

In fact, this would effectively correspond to taking the prior setting with the smallest _a_ of the two proposals in Feldkircher and Zeugner (2009). Note that even though the mean shrinkage factor is the same, this is a very different prior, where we achieve the mean shrinkage by a very small _c_ (with fixed _b_ = 1) rather than the benchmark choice of a very large _b_ (with fixed _c_ , chosen to give a reasonable prior spread of _δ_ ).

Figure 2 helps us understand the differences between the various priors. It displays the log densities of _g_ and _δ_ for all the priors discussed here (using _n_ = 72 and _k_ = 41 as in the first growth dataset in Section 7). Interior modes for _g_ occur for the
Figure 1: Malaria Incident, Antimalarial Prescriptions, and Match Between Underlying Illness and Treatment <!-- Start of picture text --> A. Malaria Incidence and Antimalarial Prescriptions by In-Clinic Testing Status<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br>Not tested RDT Only Microscopy<br>Malaria Positive (Home Test) Antimalarial Prescribed<br>B. Match between Malaria Test Result and Prescribed Treatment<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br>Not tested RDT Only Microscopy<br>All Patients Malaria Negative Patients Malaria Positive Patients<br>Share of Patients<br>Share of Patients<br><!-- End of picture text --> _Notes:_ Sample limited to the subset of patients who consented to take an RDT during the home follow-up survey in the Basic Training group. Panel A graphs malaria positivity (measured in home RDT) and receipt of antimalarial prescriptions (recorded during the clinic survey), by type of malaria test conducted at the clinic (recorded during the clinic survey). Overall positivity rate is 23.12% in the control group. Panel B graphs the match between true malaria status (measured in home RDT) and receipt of antimalarial prescription by type of malaria test at the clinic. The share of patients prescribed an antimalarial is 69.36% in the control group.

33
specific to these countries. Specifically, we will include commodity prices as a control to understand whether the inclusion of these prices explains some of the volatility of fiscal policy. The idea is to test whether the fiscal volatility in commodity-exporting countries comes solely from commodity price volatility or not.

# **3.2 Data**

We make use of annual data over the 1990-2021 period. The choice of our sample period is dictated by data availability. The data comprises 184 countries, with 148 EMDEs and 36 advanced economies. We classify countries into ‘commodity exporters’ and ‘non-commodity exporters’ by applying the classification criteria used in World Bank (2022).<sup>9</sup> Based on this classification, our sample comprises a diverse set of 90 commodity-exporting EMDEs. ‘Non-commodity exporting’ EMDEs are simply the ones not classified as commodity exporters.

We analyze three fiscal policy variables from the government budget: primary expenditure, government revenue, and primary balance (Source: IMF World Economic Outlook). In addition, to understand whether different components of expenditures matter more than others, we also analyze government consumption as a measure of fiscal policy (Source: World Bank World Development Indicators).

Our data comes from multiple sources. For commodity exporters, we obtain data on natural resource rents (as percent of GDP) from the World Bank’s World Development Indicators. Fiscal rules are based on the IMF’s Fiscal Rules Dataset (Davoodi et al. 2022). Country-specific commodity terms of trade indices are obtained from the IMF. We use institutional and political variables from the International Country Risk Group (ICRG) and the Polity IV Database. We use the Chinn-Ito index as our measure of capital account openness. We provide details of the country coverage, variables included in the analysis, and data sources in Appendix 1.

# **4. Measuring fiscal policy volatility**

# **4.1 Characterizing fiscal policy volatility**

We start by checking the cyclicality of each of the four
of communal violence tend to be highly locally concentrated: many towns, only a few miles away from the worst cities, have insulated themselves from communal riots, entirely or substantially. In contrast to Ahmedabad, Surats old city (not the part where its shantytowns are) was argued to be such an example: yet again in March and April 2002, the violence in Surat was minimal, even as Vadodara and Ahmedabad, neither too far away from Surat, experienced carnage.

> 2Since the survey only provides information on the state of residence, we are unable to account for women who migrated to another state due to marriage. However, this should not significantly affect our results because interstate migration in India is very low (Biswas, 2017). According to the 2011 census, the share of intra-state migration was about 88% and the share of inter-state migration was about 12%.

8