rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 3: Government import penetration/ Private import ratio by country <!-- Start of picture text --> 1995 2015<br>ROWSWETWNMARKHMNORVNMROUGRCHKGKORMYSARGBGRMEXGBRCHNHUNSGPRUSDNKDEUBRNCANCHEHRVCYPPERCOLSAUBRASVNUSAAUSTUNTURPOLESPSVKCHLNLDFRAAUTCZEPRTPHLLUXESTMLTBELJPNNZLZAFLTULVACRIIDNINDISRFINIRLISLITA ROWSWETWNVNMMARKHMROUGRCNORMEXBGRHKGMYSKORGBRARGHUNCHNSGPCANRUSDEUDNKBRNCHEHRVCYPCOLSVNPERBRAAUSUSASAUTURTUNSVKPOLESPNLDCHLCZEAUTFRAPRTLUXPHLESTMLTBELNZLJPNZAFLTULVAINDCRIIDNISRFINIRLISLITA<br>0 .5 1 1.5 2 2.5 0 1 2 3<br>Goods Services<br><!-- End of picture text --> Note: Raw data are sourced from the TiVA database. Public procurement flows are computed using the “typical” definition (see the main text for details).

While purely illustrative, this descriptive analysis delivers some messages that are relevant to the subsequent econometric analysis. Government procurement is mainly about services, which are generally less traded than goods. These two observations alone mechanically increase home bias in the public sector at the country level as services are weighted more in the public expenditure basket than in the private one. The sector-specific gravity model in eq (6) controls for this influence as it allows comparisons of estimates across public and private markets _within_ the goods or services aggregate. The analysis of import penetration ratios indeed suggests that already within services, public markets are less open than private ones, with important heterogeneity across countries and over time. The ensuing empirical analysis aims to investigate this variation further.

# **5 Empirical results**

In this section, we discuss the estimates from the gravity equation (6) and the estimated CHB. The objective is to estimate the border effect in government procurement and the effect of TAs (relative to private markets) by applying the empirical framework described in section 3.

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response of government, both central and state, to the food-price crisis of 2007-08, as PDS coverage was expanded (Khera, 2011), though take-up of the existing program may have also increased. Since 2004-05 is the base year for the welfare analysis, I initially compute _νj,PDS_ from NSS61-CES data to reflect PDS coverage circa 2004-05. However, to address PDS coverage expansion, I also calculate for each PDS commodity _j_ and decile of per-capita expenditures _q_ within each state _s_ the ratio _Rj,s,q_ of average PDS expenditures in 2009 to average PDS expenditures in 2004. Adjusting for 2009-10 PDS coverage is, then, just a matter of multiplying _νj,PDS_ in equation (15) by the _Rj,s,q_ corresponding to that household’s state and decile.<sup>33</sup> The quantitative implications of these adjustments are shown in Figure 5. PDS coverage circa 2004-05 leads to a substantial “improvement” in the welfare elasticity for the poorest two or three deciles. When higher 2009-10 PDS coverage is factored in, the elasticity profile shifts up again ever so slightly, but only for the bottom two deciles. Although the PDS evidently plays a significant role in protecting the welfare of the rural poor, even the distributional effects of this giant safety net scheme are dwarfed by those of the rural wage channel, as we will see next.

# **4.4 General versus partial equilibrium**

Partial equilibrium welfare analysis _assumes_ that _ψ_ = _δ_ = 0 so that, from equation (13), Ω= _λ_ Π _/αK − νA_ . Since _λ_ Π _/αK_ is equivalent to the ratio of crop revenue to total consumption,<sup>34</sup> Ω _−νA_ is just the net consumption ratio, Deaton’s (1989) justly celebrated workhorse. Figure 6 compares the partial equilibrium welfare elasticity profile (dashed curve) to the general equilibrium case already discussed. In partial equilibrium, the poorest rural households in India would
0.969|
|Sample Size<br>Notes: Attrition defined as not havin|105<br>g firm sales and empl|54<br>oyment data|reported f|33<br>rom firm re|37<br>cords in Dece|35<br>mber 2017.|This|

<mark>Notes: Attrition defined as not having firm sales and employment data reported from firm records in December 2017. This can arise from firms refusing to provide this information, as well as from firm death. P‐value in column 3 is for a t‐test of equality of means by attrition status. Columns 4 through 6 provide baseline means by treatment status for the sample of non‐attritors. P‐value in column 7 is for F‐test of equality of means.</mark> 55
government spending to address this possibility. In these specifications, I use current and lagged changes in my measures of disbursements on World Bank loans as instruments. A first caveat is that these richer dynamics are much more weakly identified. In the bottom row of this panel, I report Cragg‐ Donald statistics which provide a summary of instrument strength in the case of multiple instruments and endogenous variables. Comparing these with the Stock‐Yogo critical values reported in the table suggests that there are likely non‐trivial size distortions in hypothesis tests based on the usual asymptotic approximations. With this caveat in mind, I do find estimated coefficients on lagged government spending that are positive in all four specifications, suggesting that longer‐run multipliers could be larger than the impact multiplier I have been estimating thus far. However, these are quite imprecisely estimated, and I cannot reject the null hypothesis that the estimated coefficient on lagged government spending changes is zero.

A third possible violation of the exclusion restriction might occur if disbursements on existing World Bank projects are triggered by policy reforms that are required as part of the conditionality associated with the project. If policy reforms lead to faster growth, this would induce a spurious correlation between contemporaneous changes in disbursements and growth that is driven by omitted policy reforms. To investigate this possibility, I include a measure of changes in policy as an additional control variable in the bottom panel of Table 5. The specific measure I use is the World Bank’s Country Policy and Institutional Assessment (CPIA) ratings, that are produced annually by World Bank country economists for all client countries. These provide a rating on a six‐point scale of the quality of policies and institutions, based on a checklist of various policy areas.<sup>9</sup> Annual changes in the CPIA are significantly positively correlated with changes in output. Consistent with the idea that disbursements respond positively to policy reforms, I find that after controlling for policy changes, the estimated multipliers are slightly smaller than those reported in the benchmark
state, a complete upgrade implies an increase of primary roads by about 70 percentage points. This translates into a price reduction of about 39 percent for tradable goods as the average prices in the states are 48 percent higher than in Juba. Applying this reduction in price to equation (3), we estimate that the average consumption of tradable goods increases by 63 percent on average. With about 45 percent of tradable consumption for the average household, this road improvement increases total consumption on average by 28 percent. The computations with the upper bound coefficient -6.61 (derived from column 9 in **Error! Reference source not found.** ) yields an average increase in consumption of 57 percent. Hence, the first-order impact of upgrading the quality of all roads to that of primary roads increases total consumption by between 28 percent and 57 percent for the average household.

A simulation confirms the results of the back-of-the-envelope calculation. Based on the NBHS 2009 data for South Sudan, the impact of the price reduction due to road improvement can be simulated. As Juba in Central Equatoria is the reference for the price differential, the impact of road improvement in Central Equatoria is simply estimated as the average price consumption effects per tradable item over the remaining states. We use the lower (-3.22) and the upper bound (-6.61) for the elasticity of reduction of the price differential with respect to road improvement . The simulation considers upgrading major roads connecting the state capitals assuming that the upgraded roads would yield a road quality of 100 percent for the corresponding states.

25
prices have been falling since January 2010 (1990-2015)<br>World Bank Commodity Price Data (The Pink Sheet) . . . . . . . . . . . . . . . . . . . . .|9|
|10|<br>Kenya’s imports of intermediate goods is rising quickly<br>UN Comtrade 2015||
|11|China’s share of Kenya’s intermediate goods imports is growing<br>UN Comtrade 2015||
aggregate, the choice of the most appropriate imputation method is not trivial, as different approaches generate households’ re-ranking along the welfare distribution within country and may potentially imply reshuffling in intertemporal and international comparisons. Therefore, all practitioners in charge of the preparation of welfare aggregates for distributional analysis should add to the list of sensitivity checks a careful examination of the distributional impact of different imputed rent methods, as proposed in this study.

This paper offers three main messages. First, the poverty estimation process must be internally consistent in terms of methodology. Most prominently, poverty lines should always be adapted to the definition of the welfare aggregate. Second, methodological consistency should be maintained over time, by using the same definition of welfare aggregate, including using the same rent-imputation approach. Trends in poverty, inequality and shared-prosperity risk otherwise to be fictitiously driven by differences in the adopted rentimputation approach. Third, methodological consistency should be sought internationally, after conducting an accurate sensitivity analysis of the distributional impact of different methods on a larger scale. Implementing these three key points calls for a coordination effort of researchers and practitioners involved in all stages of data collection and analysis: from the design of homogeneous questionnaires over time and across countries, to the construction of welfare aggregates allowing for consistent comparisons. In an effort to push forward the research agenda, new data sources may be explored to obtain more reliable and standardized estimates of housing quality and of rental prices. A promising avenue, recently applied in a study for Sub-Saharan Africa, could be the combination of high-resolution satellite photography with household survey data (Tusting et al 2019).

14
as customs controls, quota licensing, pre-shipment inspections, and additional fees paid at customs, among many others.<sup>14</sup> Table 1 presents the list of importing countries included in our sample, together with their coverage ratios of border NTMs. There are a total of 49 importing countries with 96 exporting countries. The sample is largely determined by the availability of NTM data in the UNCTAD TRAINS database. There is a wide range of variations in terms of the coverage ratios across these di¤erent importing countries. We exploit this wide range of variations across countries to estimate the AVEs at the HS 6-digit level.

Trade discrepancies are constructed by matching import data with export data at the HS 6-digit level according to Equation (8) for both quantity and value, based on UNSD Comtrade HS 6-digit level import and export data. Since we do not observe the true quantity, Q; we replace it with the average value of import and export quantity and value. The quantity data were thoroughly examined, and we include only observations in which the importer and the exporter use the same quantity unit in their reporting, to make sure that the discrepancies are not due to di¤erences in the units of measurement in quantity. In addition, the quantity units remain the same within any given HS 6-digit product.<sup>15</sup>

# 4 Estimation of the Ad Valorem Equivalent of NTMs

In the theoretical model, the impacts of NTMs on trade discrepancies work through the ad valorem equivalent (AV E) of NTMs. To empirically test the hypothesis, we will need estimates of these AV Es; which vary by product and importer-exporter pair. There is no such detailed estimates available in o¢ cial published statistics. We will need to estimate these AV Es: The closest estimates are provided by Kee, Nicita and Olarreaga (2009), at

> 14Please refer to Ederington and Ruta (2016) for details.

> 15For the regression analysis, we always control for product ...
The agricultural sector in Ghana suffered a dual shock in 1983, as the migrants to Nigeria were expelled back into the country, and the country experienced its worst drought in history. "But once the returnees settled down in the rural areas and started fanning activities, and when the rains also returned in 1984, the situation improved considerably. The index of food output per capita rose from 80.9 in 1983 to 114.6 in 1984, 105.7 in 1985 and 113.7 in 1986 (FAO Production Yearbook, 1987, p.47)" (Tabatabai, p.720).

With the successful implementation of the ERP the economy turned around in the years following 1983. Exports, cocoa output, manufacturing and non-tradable services all showed significant real growth. We have detailed information about relative incomes from the GLSS after 1987.

**Table 6.2: Rural-Urban Differences in Income, 1987**

||Accra|Other Towns<br>Rural|
|---|---|---|
|Household Income|194.2|212.1<br>236.7|
|Income per Earner|165.7|146.8<br>140.0|
|Income per capita|62.3|59.2<br>59.6|
|Income per AEU|82.6|83.0<br>84.9|
|Median|||
|Household Income|122.9|141.4<br>160.3|
|Income per Earner|109.9|95.1<br>92.3|
|Income per capita|36.9|38.2<br>38.5|
|Income per AEU|52.5|_56.0_<br>61.1|

# _Source: GLSS 1987_

The differences in incomes between urban and rural areas, as reported in 1987, are summarized in Table 6.2. We
any materials or stationery (other than pens) into the testing area. The grades selected for the QAT are tested simultaneously in the same testing area and the seating for the students is mixed. All test materials provided to the test takers are collected back. All unused test materials are retained by the testing agency. ADU also reports that test item recycling across QAT rounds is presently rare given the size of its test item bank.

The QAT serves as the main source of data for the analysis of learning dynamics in program schools. While PEF has conducted nine QATs since the inception of the FAS program (QAT 9, the latest QAT, was administered in March 2010), this study uses data from QAT 4−QAT 8 which spans November 2007–November 2009, a two-year period, covering, in whole or in part, three academic years. QAT 4 is the first QAT round in the analysis as it represents the 13
**Figure 13. Moderate poverty headcount rates considering both pre‐ and post‐fiscal income aggregates, separately for households with different compositions in terms of headship, presence of child and presence of elderly**

Source: authors’ estimations based on PNAD-C 2019. Notes: family composition is determined by whether there is at least one household member that declared to be the spouse of the household head. Pre-fiscal poverty indicates the poverty headcount rate considering market income plus pensions, while the post-fiscal poverty indicates the poverty headcount rate considering consumable income. The household is considered to have children if there is at least one member aged 15 or less who is neither the household head nor the spouse, and to have an elderly if there is at least one member aged 60 or more. Similar colors highlight demographic groups with similar changes in poverty.

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# **Figure 1.  Liquidity and Concentration**

<!-- Start of picture text --> A. Number of firms by Liquidity<br>(firms with financial data only)<br>100%<br>80% Traded<br>60% Cosmetic<br>40% Not Traded<br>20%<br>0%<br>1997 1998 1999 2000<br><!-- End of picture text --> <!-- Start of picture text --> B. Market capitalization by the largest firms<br>100%<br>80%<br>101+<br>60%<br>50-100<br>40%<br>20-50<br>20%<br>10-20<br>0% 10 largest<br>1997 1998 1999 2000 2001<br><!-- End of picture text --> <!-- Start of picture text --> C. Value Traded by the top most traded<br>firms<br>100%<br>80% 101+<br>60% 50-100<br>40% 20-50<br>10-20<br>20%<br>10 most traded<br>0%<br>1997 1999 2001<br><!-- End of picture text --> 26
countries to return to fiscal rule compliance. We address this question in Section 3 using the IMF Fiscal Rule Dataset (IMF 2022), which provides us with a sample including every instance in which a budget balance rule was either suspended, revised upward, and/or an escape clause was used over the period 2000-2019.

A potential contributing factor to the increase in escape clause usage and temporary rule suspensions during the pandemic was the development of a “second-generation” of fiscal rules in the decade preceding the COVID-19 pandemic. As noted by Eyraud et al. (2018), in the years following GFC 2008-09, a “second-generation” of fiscal rules emerged which sought to construct rules in such a way that allows for an appropriate degree of short-run flexibility when necessary while still promoting long-term fiscal responsibility. In this context, many reforms made post-GFC introduced new escape clauses covering a larger set of contingencies during crisis periods in which unexpectedly large fiscal expansion is necessary, while providing guidance on the path back to compliance. The trend shown in Figure 2 highlights the fact that these enhancements allowed for a high degree of flexibility in fiscal rule enforcement through COVID-19 pandemic. Our empirical results reported in Section 3 provide evidence that with this added flexibility, fiscal rules continued to serve as a signal of fiscal responsibility during the pandemic, even for countries that enacted escape clauses and used discretion to suspend rules. Interestingly, we show that the existence of fiscal rules compressed sovereign spreads through the pandemic regardless of whether enforcement continued during the COVID-19 period.

**Figure 2. Suspensions or Activations of Escape Clause by Year**

<!-- Start of picture text --> Number of countries<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1986 1991 1996 2001 2006 2011 2016 2021<br>Suspension Escape clause<br><!-- End of picture text --> Source: Davoodi et al. (2022a
evidence from Benin implies that transparency interventions should focus on how issues are framed in order to address these other political constraints.

The literature review that follows identifies the contribution of the paper both to prior research on media effects and to research on the determinants of bed net take-up by households. Section 3 describes elements of bed net distribution and the media market in Benin that are essential to understanding our empirical strategy. A discussion of that strategy – the identification of media effects – follows next in Section 4. The empirical hypotheses and results are then presented in Section 5. Our main results show significant effects of media access on the pricing of bed nets. We also present evidence on mechanisms and the effects of radio access on household knowledge. The main results are also robust to numerous alternative specifications. The conclusion in Section 6 highlights the policy implications of this work.

# **2. Literature review**

Prior research has found that greater media access by individual households or in narrow geographic jurisdictions increases the likelihood that these will receive targeted benefits from government programs. Citizens with greater media access are more informed about these programs and more likely to participate in political actions to hold government accountable for the delivery of these benefits. Anticipating this, governments target greater benefits to citizens with greater media access (e.g., Besley and Burgess 2002, Strömberg 2004). We find that, in a different policy context and institutional setting, governments respond to more informed citizens not by giving them greater benefits, but instead, by exploiting these citizens‘ greater willingness to pay for benefits.

The results here are related to those in Keefer and Khemani (2010), who use the same data from northern Benin and find that schools located in villages with greater radio access have no greater government-provided inputs (such as teachers or books), nor more responsive public agents (lower teacher absenteeism) nor more active PTAs. The research here provides a stronger test of whether more informed citizens receive greater benefits. First, both radio coverage and
**_Verifcations_**|||||||||
|Observations|15,189,831|63,757,882|13,787,845|15,229,631|40,457,210|65,789,353|87,894,462|302,106,214|
|Distinct verifcations|-|63,753,322|-|-|40,457,210|34,941,675|81,410,584|220,562,791|
|**_Payments_**|||||||||
|Observations payment|13,930,424|64,127,137|22,113,067|21,449,922|52,545,469|74,023,098|83,436,799|331,625,916|
|Distinct payments|13,243,955|64,127,137|22,109,079|-|52,545,469|72,428,292|69,293,735|293,747,667|
|Total amount of payments (billion BRL)|132.2|514.9|155.4|196.5|407.4|501.7|1691.6|3,599.7|
|Number of distinct sellers|548,503|1,710,005|1,326,612|-|758,735|1,541,115|3,141,460|9,026,430|
|Number of distinct municipalities|182|853|223|184|399|497|644|2,798|

**Notes** : This table presents the descriptive statistics from the budget execution dataset. The variables “Distinct commitments”, “Distinct verifications” and “Distinct payments” are simply the count of the identifiers that uniquely identify each table - _empenho_ , _liquidacao_
# 3 Empirical Model

The empirical specication is derived from a model of malaria that is based on previous models used in D. L. Smith and McKenzie (2004), Cosner et al. (2009) and Torres-Sorando and Rodriguez (1997). Four key assumptions allow me to simplify the model so that incidence in the current month is dependent on incidence in the last month using a linear functional form. Expected imported cases enter as a linear additive term as in Torres-Sorando and Rodriguez (1997). The model, assumptions and implications are described in detail in Appendix A. I start out estimating equation 1 using OLS, with imported incidence calculated using equation 2: In this model, _xit_ represents the incidence, or number of humans infected in location i at time t per 1000 people in location i.<sup>18</sup> Location i is one of the 36 health post catchment areas and t is at the monthly level. The _β_ 1 parameter estimated tells us on average how many new cases per 1000 are generated in the following month from cases in the current month. In contrast to epidemiological models, where this parameter would be estimated separately for each area, I want to causally estimate the secondary cases generated. Therefore, I estimate an average eect across locations and time in order to be able to include health post area xed eects, _γi_ and month xed eects, _δt_ . This allows me to control for malaria seasonality and unobservable characteristics of a health post area that might impact incidence.

I use the mobile phone data to calculate expected imported malaria cases entering and divide them by the population of the area they enter, _Hit_ , to calculate the expected imported incidence, E( _Iit_ ) . The likelihood of an infected case entering depends on the origin of an

> 18Population is based on the known population of each health facility catchment area. The appendix includes a robustness check where the annual population is adjusted monthly using the number of people entering and leaving