rafmacalaba/data-use-mentions-extended huggingface dataset:
as follows: where _x_ represents the amount of exports, the subscript i stands for a specific product (or market) and n is the total number of products (or markets). When a single export product (market) produces all the revenues, _H_ =1, as opposed to _H_ =0 when a large variety of products compose the revenues (Haddad et al. 2010; Lederman and Maloney 2012; Cadot et al. 2013). Higher (lower) values of the Herfindahl index, _H_ , imply lower (higher) degree of export diversification. We use COMTRADE data on exports by product at a 4-digit disaggregated level from the SITC Revision 1.0 to compute Herfindahl indices of export product concentration. Bilateral data from COMTRADE are also used to compute Herfindahl indices of export market concentration.

_Table 1_ displays the evolution over time of the Herfindahl indices of export product and market concentration in the periods 1990-94 and 2010-14. In the period under analysis, the region’s Herfindahl Index for export product concentration declined from 0.41 in 1990-94 to 0.3 in 2010-14 —thus, signaling an improvement in terms of trade diversification. In contrast, the region’s Herfindahl index of export market concentration remained invariant.

_page 12_
high-quality anthropometric measures on every household member, which were taken by trained personnel from the National Institute of Public Health (INSP). In order to control for age and gender specific differences in child height, height-for-age z-scores are computed using the 2000 CDC Growth Charts for the United States provided by the National Center for Health Statistics (NCHS). To compute those z-scores, we calculate age in months using the information on date of birth and interview date available in the data.

With respect to the roll-out of the program, the MxFLS survey collects information on Oportunidades current participation at the individual, household and community level. However, we do not know participation histories. Therefore, in order to identify the year in which each MxFLS community was incorporated to the program, we combine MxFLS data with Oportunidades administrative records to assign the year of entry of the program to a locality. In particular, we use two sources of information. One source is the complete list of Oportunidades beneficiaries as of December 2009. These records have individual-level information on locality of residence and date of enrollment in the program. Based on the households’ date of entry, each of the 246 MxFLS localities is associated with the year in which the largest number of households was enrolled in the program. The second source of information is a locality-level data set with the number of families enrolled each year. Combining these two data sets, together with the selfreported participation rates reported in MxFLS, we assign a year of incorporation to each MxFLS locality. Figure 1 illustrates the pattern of expansion of the program for the rural and urban areas separately. MxFLS self-reported participation rates in 2002 and 2005 are very similar to the ones obtained from administrative records, which should be expected as we have a populationrepresentative sample in 2002.

9
.88|
|2017|0.15|0.80|0.41|0.58|0.61|0.66|0.33|0.63|0.74|0.73|0.34|0.88|
|2018|0.16|0.80|0.45|0.39|0.92|0.66|0.35|0.64|0.74|0.76|0.35|0.90|
|2019|0.17|0.75|0.47|0.39|0.90|0.67|0.35|0.65|0.75|0.75|0.34|0.92|
|2020|0.18|0.77|0.41|0.58|0.70|0.65|0.35|0.63|0.75|0.76|0.33|0.90|
|2021|0.20|0.73|0.41|0.38|0.87|0.61|0.32|0.58|0.68|0.73|0.34|0.88|

_Notes:_ Based on data from EH 2007-2021. Estimation using a rescaled score from the first principal component of the PCA.
are all bound, though the average rate for industrial products is 14.9 percentage points above the current applied rate. We refer to this gap below as “ _binding overhang_ .” India and Thailand’s tariffs are partially covered by bindings, again with significant binding overhang.

As in the case of industrial tariffs, the stage for the agricultural negotiations was largely set by the Uruguay Round. One key difference from industrial products is that essentially all agricultural tariffs are bound. However, in both industrial and developing countries, there is a large degree of binding overhang resulting from “dirty tariffication” or the use of “ceiling bindings” (Hathaway and Ingco 1996).

# **3. SOME TARIFF REDUCTION FORMULAS**

A range of tariff-cutting formulas has been considered or implemented under GATT and regional trade arrangements. In this section, we focus on formulas implemented on a line-by-line basis, leaving discussion of discretionary approaches to Section 5. Stern (1976), Laird (1998), Laird and Yeats (1987) and Panagariya (2002), WTO (2003c) and World Bank (2003) survey a range of alternative line-by-line formulas. The first is a simple proportional cut, frequently described as a linear cut in policy discussions:

> 2 This omission has recently been remedied for applied rates in a few countries in the WTO’s Integrated Database and the UNCTAD TRAINS database available through the World Bank’s _World Integrated Trade Solutions_ (WITS) program, and in data compiled by the International Trade Centre and CEPII.

3
ensure that only low-cost private schools self-select to participate in the program; the original amount was also purposely set at half the estimated per-student cost in the public school system.<sup>1</sup> In return for receiving the subsidy benefit, the program school has to, among other things, waive tuition and fees for all students and ensure that the school achieves a minimum student pass rate in the Quality Assurance Test (QAT). Program schools that satisfy the above conditions are also eligible for other substantial cash benefits offered on an annual basis: group-based bonuses for teachers in schools that achieve high QAT pass rates/mean scores and competitive bonuses for schools that rank highest in the QAT in each main program district.

Each of the above incentive features (the structure of the benefits as well as the benefit eligibility rules) represents key program innovations in the education field that may induce participation, equity, and learning gains.<sup>2</sup> We are not aware of any other program in a developing country which combines these innovations into a single intervention. As of June 2010, the FAS program has proceeded through six phases of expansion and supports 798,000 students in 1,779 schools in 29 of the 36 districts in the province, making it one of the largest PPP initiatives in education in the developing world.

The learning data used in the study come from the QAT. The QAT is a curriculum-based, multi-subject, written test offered biannually in program schools. It is designed by professional subject specialists in PEF, and, starting with QAT 4, the administration of the test was competitively outsourced to independent testing agencies. The testing agencies are expected to adhere to the testing protocols set by PEF, with testing agency compliance monitoring by PEF at all program schools. Importantly, the testing agencies are instructed by PEF to follow preestablished procedures to control for certain types of potential strategic responses of schools (i.e., cheating in a broad sense) that may artificially raise test scores
budget divided by the number of beneficiaries, and we randomly allocate this to eligible households until we reach (approximately) the (scaled) total number of beneficiaries covered by both government and donor disbursements. We assume that the per capita subsidy provided by government and donor programs is the same.

For UWEP, YLP and NUSAF we randomly allocate program-specific benefits to program-specific eligible household pools until we reach (approximately) the average number of beneficiaries and benefits delivered annually and per region according to program reporting.

Given that the YLP and UWEP programs provide loans rather than transfers, and in the absence of further information, we assume that the programs provide a benefit of foregone loan interest payments, at 25 percent of the amount of the principal disbursement.

# 3.4.3. Indirect Taxes

- <u>Excise and petrol duty: It is not possible to directly identify the excise and petrol duty tax amounts</u> paid, so instead we impute, for each purchased item, the share of the item’s value that is an excise charge using _effective_ rates of excise rather than statutory rates. The effective rates are calculated as follows: from the survey we generate a total consumption amount for each excisable good which is recorded in the survey. We also generate the share of total household consumption in the survey comprised by each good. We apply those shares of consumption to the National Accounts Final Household Consumption amount to estimate a “national accounts” total consumption amount for each excise good. We then divide the excise revenue from each good by the consumption estimate to determine an effective rate for each good.

- <u>Value-added tax (VAT): Similar to the excise duties described above, we impute the amount of VAT in</u> each purchase recorded in the UNHS by estimating the share of the good or service’s price that is a VAT charge.  This imputation assumes an effective (rather than statutory) rate of VAT applies to each purchase.  For goods and services that are
Table 1: Average Markups and Market Imperfections, By Sector

|ISIC|||P|re-WT|O<br>|P|ost-WT|O<br>|
|---|---|---|---|---|---|---|---|---|
|Rev. 2||Sector|ˆ_μ_<sup>_m_</sup><br>_it_|ˆ_μ_<sup>_l_</sup><br>_it_|ˆ_ψit_|ˆ_μ_<sup>_m_</sup><br>_it_|ˆ_μ_<sup>_l_</sup><br>_it_|ˆ_ψit_|
|31||Food|1.36|3.43|-2.13|1.26|3.79|-2.36|
|32||Textiles|1.55|2.36|-0.78|1.40|2.47|-1.04|
|33||Wood|1.88|1.89|0.24|1.91|1.89|0.13|
|38||Metals|1.72|2.59|-0.99|1.54|2.65|-1.18|
|||All sectors|1.63|2.57|-0.91|1.55|2.60|-0.95|

**_Source:_** Author’s analysis based on data from the World Bank Regional Project on Enterprise Development (RPED) and Ghana Manufacturing Survey (GMES) from 1992 to 2003.The surveys were conducted by the Centre for the Study of African Economies (CSAE) at the University of Oxford, University of Ghana, and Ghana Statistical Service. **_Note:_** Table reports average markups computed on materials and labor; as
them as a proxy for future climate scenarios. The return period hazard maps used for each of the future scenarios are outlined in Table 2. Although simplistic, this method allows areas that may be impacted by increasing riverine and extreme rainfall driven flooding to be identified. Clearly there are some significant assumptions and uncertainties arising from this method. However, given the impracticalities of modeling future flood risk in Vietnam, this approach provides a plausible and practical attempt to estimate changing flood risk at the national scale.

For each of the four return periods, four scenarios are modeled (historical, future with low sea level rise, future with medium sea level rise, and future with high sea level rise), combining the coastal and fluvial/pluvial hazard layers (Table 2). Importantly, the flood hazard models do not include flood protection (such as dikes and drainage systems), which can make a large difference in the flood hazard particularly in well‐protected areas. In these well‐protected areas, our flood maps may over‐estimate the flood hazard. For full details on the methodology used to produce these hazard maps, see Appendix 1.

_Table 1. Future scenarios used for Vietnam coastal modeling. RCP stands for Representative Concentration Pathway. We use two RCPs from the recent Intergovernmental Panel on Climate Change (IPCC) report (IPCC 2014) to represent a low climate change and a high climate change scenario. RCP2.6 is a low scenario consistent with temperature increases of 2°C, while RCP8.5 is a high scenario consistent with temperature increases of 4°C. The A1B scenario was taken from a previous IPCC report (IPCC 2007) and represents a medium climate change scenario, in between RCP2.6 and RCP8.5._

|**Simulations**|**Scenario**|**Percentile**|**SLR ‐2100(m)**|
|---|---|---|---|
|Low
are consistent with the vast literature on earnings functions. They include: age and age<sup>2</sup> , years of schooling, region of employment, status of employment, household size and category of principle occupation. Because age and work experience are correlated, I do not include work experience as an independent variable; moreover, the age<sup>2</sup> variable is used to capture the shape of the age-earnings profile. The survey data does not directly quantify skill level; as a result, I rely on the measure of years of schooling as a proxy for skill level.<sup>13</sup> j use additional individual characteristics on gender and citizenship to capture discrimination effects. The results from the two regressions are shown in Table 4 below. All of the variables are significant, and the adjusted R<sup>2</sup> terms are fairly high at 0.36 and 0.38 for dependent variables labor and net household income, respectively, suggesting that these 9 independent

> ]3 Despite common practice, Gupta (1998) points out that years of schooling is unlikely to reflect perfectly individuals' skill level or productivity.
R-Squared is adjusted and within all fixed effects.

consumption distributional data from more than 1900 household surveys spanning 1967-2019 and 168 economies.<sup>13</sup> As a caveat for this data, it is worth noting that not all these surveys are comparable in design and sampling methods. There are also sizable discrepancies between national accounts and household surveys.<sup>14</sup> In Columns 6-9, we investigate the inclusion of multiple signals at once. Column 6 shows that both NO2 and lights have explanatory power when included jointly. However, when any of the additional signals are included in Columns 7-9, the statistical significance of lights disappears. Thus, NO2 is always a useful predictor of cyclical output, even when additional widely-used signals such as electricity consumption are available.The same cannot be said about lights.

**Differences by-Grade.** Table 3 Column 1 replicates Table 2 Column 4, in which both NO2 and lights were used as signals. Then, in Columns 2-4, we break down the results by data quality grade.<sup>15</sup> Lights only have statistically significant explanatory power for cyclical fluctuations in the 37 out of 178 countries with the lowest data quality, consistent with the findings in Chen

> 13Chen et al.; see Chen and Ravallion (2010) and Pinkovskiy and Sala-i Martin (2016) for representative applications.

> 14See Deaton (2005) for a discussion of the sources of these discrepancies.

> 15We consolidate A and B, and D and E, into joint groups to be more comparable with the larger sample in grade C.

17
# 1 Introduction

Import demand elasticities are crucial inputs into many ex-ante analyses of trade reform. To evaluate the impact of regional trade agreements on trade fl ows or customs revenue, one needs to fi rst answer the question of how trade volumes would adjust. To estimate ad-valorem equivalents of quotas or other non-tariff barriers one often needs to transform quantity impacts into their price equivalent, for which import elasticities are necessary. Moreover, trade policy is often determined at much higher levels of disaggregation than existing import demand elasticities.<sup>1</sup> This mismatch can lead to serious aggregation biases when calculating the impact of trade interventions that have become more of a surgical procedure. Finally, to compare trade restrictiveness and its associated welfare losses across different countries and time, one would need to have a consistent set of trade elasticities, estimated using the same data and methodology that, ideally, would be consistent with trade theory. These do not exist. The closest substitute and the one often used by trade economists is the survey of the empirical literature put together by Stern et al. (1976). More recent attempts to provide disaggregate estimates of import demand elasticities (although not necessarily price elasticities, but Armington or income elasticities) have been country speci fi c and have mainly focused on the United States. These include Shiells, Stern and Deardorff (1986), Shiells, RolandHolst and Reinert (1993), Blonigen and Wilson (1999), Marquez (1999, 2002, 2003), Broda and Weinstein (2003) and Gallaway, McDaniel and Rivera (2003).

The primary objective of this paper is to fi ll in this gap by estimating import demand elasticities for over 4,625 goods (at the six digit of the Harmonized System (HS)) in 117 countries. We use a methodology that is consistent with trade theory (i.e., imports are a function of prices and factor endowments), and data sources are identical for all countries and goods. We then use these estimates
changes using log first differences. The 5-year breakeven inflation rate is used from 2003 onwards.<sup>6</sup>

# **3.2 Impact on EMDEs**

The data included in the panel local projection models are provided in table A2 and include financial, real and fiscal variables (for details, see appendix figures A1-A3). Seasonal adjusted data are used when available or adjusted using X13-ARIMA-SEATS (U.S. Census Bureau 2017). The data are mainly sourced from Haver Analytics, with all available data collected for as long a time period as possible at a quarterly frequency. Quarterly data is used since it is the highest available frequency for most fiscal-related variables—a key focus of this paper. The datasets used to measure the impacts of different U.S. interest rate shocks differ based on the dependent variable. The most restricted country sample size, in the short-term yields, includes 19 economies from 1997Q4 to 2019Q4—an unbalanced panel of 768 total observations (table A3).<sup>7</sup> The largest country sample used, for example with capital flows, includes 36 EMDEs over 1997Q2 to 2019Q4—an unbalanced panel of 1547 observations. The EMDEs included across all regressions are provided in table A4.

To transform the monthly monetary policy shocks identified earlier into a quarterly frequency and to reflect a one-percentage-point change in the 2-year U.S. yield, the shocks are adjusted in two ways. First, given that monthly shocks are in first differences, shifting to a quarterly change is done by adding monthly changes within each quarter. Second, to make interpretation across shocks comparable, the contribution of all shocks from the historical decomposition of the 2-year yield is used.<sup>8</sup>

# **3.3 Correlates of crises**

The probability of crises is estimated using logit and probit models based on a panel dataset of 139 EMDEs on annual data from 1985-2018. The variables selected are based on empirical findings in the early warning
**Figure 3.** Distance between the social contracts in MENA and OECD

# **4. Econometric analysis**

Our econometric analysis proceeds in three steps. First, since we are mainly interested in the Participation (P3) dimension of the SC, we perform some Granger-type causality tests to investigate its causal relationships with the Provision (P2) and Protection (P1) dimensions. Second, we run a Probit analysis that takes into account the fact that the data are fractional. Third, we test the robustness of our results by applying a propensity score matching analysis.

**_Causality_** To test whether both and cause , we use Dumitrescu and Hurlin's (2012) variant of the Granger (1969) causality test, which is one of the most widely used tests for its simplicity PP2 PP1 PP3 and its suitability for heterogenous panels. Prior to running this causality test, we check whether the indices need to be differenced in order to make them stationary. Table 5 shows the results of a Pesaran (2007) second generation panel unit root test that rejects the null hypothesis of non-stationarity of the three s in levels when both the MENA and OECD data are used. PP

|<br>**Table 5.**Panel unit root tests<sup>+</sup><br>H0<br>MENA<br><br>|OECD|
|---|---|
|P3nonstationary<br>−3.83<sup>∗∗∗</sup><br><br>|−3.53<sup>∗∗∗</sup>|
|P2nonstationary<br>−3.41<sup>∗∗∗</sup><br><br>|−4.89<sup>∗∗∗</sup>|
|P1nonstationary<br>−3.95<sup>∗∗∗</sup><br>|−
**Table S1.3.** Effects of the Jordanian Refugee Surge, Polity IV Index

||**OI**<br>|**C all**<br>|**OIC mi**<br>|**nus PAK**<br>|**OIC**<br>|**SAL**<br>|
|---|---|---|---|---|---|---|
||**Effect**<br>**(1)**|**_p_-value**<br>**(2)**|**Effect**<br>**(3)**|**_p_-value**<br>**(4)**|**Effect**<br>**(5)**|**_p_-value**<br>**(6)**|
|1991|3.740|0.545|3.740|0.500|1.930|0.714|
|1992|5.740|0.455|5.740|0.417|3.620|0.714|
|1993|5.110|0.455|5.110|0.417|2.240|0.857|
|1994|5.110|0.455|5.110|0.417|2.240|0.857|
|1995|5.110|0.455|5.110|0.417|2.240|0.857|
|1996|5.110|0.364|5.110|0.500|2.240|0.714|
|1997|5.110|0.545|5.110|0.
# **1 Introduction**

As the second-largest means-tested aid program in the United States, the Supplemental Nutritional Assistance Program (SNAP) is a central element of the US safety net, providing about $ 70 billion in electronic benefits for food to more than 40 million Americans annually.<sup>1</sup> Although much attention has focused on the impact of SNAP on participating households, less is known about the response of retailers from the supply side.<sup>2</sup> Quantifying how producers respond to changes in SNAP, in particular by changing prices, is essential to understanding the welfare consequences of transfer programs. Price effects could attenuate the real spending power of government transfer payments and distort the redistributions such transfer programs intend to achieve. These effects are an empirical question.

We present new evidence on this question by estimating the impact of SNAP benefit changes on retail prices, sales, and household consumption, leveraging national retail and consumer scanner data between 2006 and 2015. To understand economic incidence, we develop and calibrate a partial-equilibrium framework. The mechanisms examined may help better understand implications of other social insurance programs such as universal basic income to the extent that electronic benefits for food are comparable, which we examine. Two main challenges arise when estimating the causal impact of SNAP. First, endogenous local economic conditions affect program participation, as eligibility for the program is determined by household economic characteristics. Second, because SNAP is a federal program, key parameters determining eligibility and benefits per recipient have been thought to leave little room for panel variation.

In this paper, we first formally document the benefit formula by analyzing computer algorithms used by the federal government to audit state-level-benefit disbursements, en route to introducing our identification strategy.<sup>3</sup> We investigate the factors that impacted the scale of the program during the Great Recession and its aftermath. We find that the program expanded by 140% between 2007 and 2014 not only because of the endogenous economic downturn, but also because of
for 14.3 months compared to 13.2 months for boys. Since all children were observed in primary school, there are no differences in enrollment rates though there is a small difference in duration of enrollment – on average girls have enrolled in primary schools for 0.5 months more than boys.

# _Quality of preschool services_

The quality of preschool services in this paper is measured using the Early Childhood Environment Rating Scale (ECERS-R) (Harms, Clifford and Cryer, 2005). Two raters assessed

> 9 The EDI domains have been shown to be correlated with children’s readiness for school. 10 EDI data were not collected for 9 year olds due to ceiling effects. There was very little variation at age 9 with almost all children scoring at the maximum end of the EDI scales, which range from 1 (low) to 10 (high). 11 2008 was chosen because it was the year before the Indonesia ECED project launched in 2009.

> 12 The maximum number of months is 10 months since we follow the Indonesian academic calendar.

8