rafmacalaba/data-use-mentions-extended huggingface dataset:
before short term credit. (b) **_Non-Miracles at the Micro level:_** This is the case of the following 5 episodes - Mexico, Indonesia, Russia and the two Turkey episodes – where there is no credit-less recovery. We can further split the non-miracles into _Phoenix Miracles only at the Macro level_ and _Non-Miracles at the Macro and Micro levels._ The macro evidence based on GDP and Private Credit in section 4.1 showed Phoenix Miracles at the macro level in Mexico, Indonesia, and Russia. However, as seen in Figure 5C, in each of these countries both Sales and Short Term Borrowing recover at the same time (at t+1 in the case of Indonesia and Russia and t+3 in the case of Mexico). In both the Turkey episodes (see Figure 5D), we find that both corporate sales and credit collapse during the crisis and show no signs of recovery even two periods after GDP recovery.

To summarize the results from sections 3.1 and 3.2, based on just the macro data, we could categorize only 6 of the 9 3S episodes as potential Phoenix Miracles. When we look at firm-level data aggregated up to the country level, we find that only 4 of the 9 3S episodes appear to be Phoenix miracles where corporate sales recover before a recovery in short-term borrowing.

# **4. Microeconomic Foundations of Phoenix Miracles**

# **_4.1 Identification of Firm-level Miracles_**

In this section, we disaggregate our analysis to the firm level to first identify how many firms recover in each of the economies and then to identify firms that may be classified as potential miracles. We next focus on their balance sheet and cash flow statements over the two periods following the trough of the crisis. Since we are aggregating across all firms in the economy, we use a balanced sample of firms with key non-missing balance sheet and cash flow statement data<sup>17</sup> in each of the five years from
procurement, logistics, stockpiling, and planning practices and strategic investments in domestic market infrastructure may help to reduce domestic food prices in the medium-run (Lampietti _et al_ , 2011). Such measures would need to be country-specific and depend on the local cost-build-up of imported food commodities. Countryspecific identification of major infrastructural bottlenecks may, therefore, be advantageous. Other areas may include an assessment of the role of regional trade, overall supply chain efficiency, and instruments of modern price risk management – all of which could help to smooth supply and cereal stock shortages.

This paper looks at aggregate food price pass-through effects only. We do so because of data limitations, but also because we are interested in the question of countries‟ vulnerability to global food price shocks in general. For a more in-depth understanding of pass-through effects, one would need to go beyond aggregate food price analysis, and estimate these effects by commodity and country. Thus, there should be efforts to collect more systematically domestic commodity price data. For a few MENA countries, we had only short time series, which arguably lowers the precision of the pass-through estimates. Finally, in countries where pass-through effects are large, in-depth country studies might be beneficial to disentangle more effectively the role and nature of different domestic factors of food inflation.

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- (i) Mapped VAT rates to PECS 2016/17 consumption items.

- (ii) Prepared the Augmented IO Matrix<sup>44</sup> for the West Bank and mapped the Augmented IO sectors to VAT rates and to consumption items (COICOP codes) in the PECS 2016/17.

- (iii) Calculated indirect effects based on the Augmented IO Matrix of the West Bank and the CostPush Model (Inchauste and Jellema 2018). The model applies the price shock of VAT rates to taxable IO sectors; output sectors that have used inputs that paid VAT will carry indirect effects.

- (iv) Excluded non-taxable categories such as self-production and gifts from the PECS 2016/17 consumption dataset. Netted down self-reported households’ expenditure.<sup>45</sup>

- (v) Calculated direct effects based on VAT statutory rates for the formal purchase share of all taxable items in the PECS 2016/17. The estimation of households’ formal purchase share (by decile) was imputed based on average consumption informal shares (by decile) estimated in similar countries available in Bachas et al. 2020.<sup>46</sup>

- (vi) Applied indirect effects<sup>47</sup> (calculated for the West Bank) for exempt formal items and for the informal purchase share<sup>48</sup> in the PECS 2016/17.

- (vii) Scaled-up the VAT simulation in the survey to match the VAT effective rate in the administrative accounts (total VAT relative to consumption).

**The VAT model accounts for the different tax systems applied by region:** We assume residents in the West Bank face Palestinian VAT rates, that residents in East Jerusalem face Israeli VAT rates, and that Gaza residents do not pay VAT.

**We apply the latest methodologies by accounting for informality in households’ purchases based on Bachas et al. (2020), and including VAT indirect effects (e.g.,
this transaction-level information with firm-level income statements and balance sheet data on 62,653 publicly listed firms from 65 countries. Our matched dataset includes both publicly listed firms that issued equities and bonds during our sample period and those that did not issue securities during this time period. We employ a difference-in-differences empirical strategy around capital market issuances to assess the connections between firms issuing equities and bonds, firm investment in productive capabilities, and firm growth. Our analysis controls for firm and country-year fixed effects, which capture firm time invariant characteristics and country shocks occurring at an annual frequency.

We find, first, that compared to non-issuers, firms choosing to issue securities grow faster and boost their productive capabilities (employees, tangible capital, and intangible capital). Issuers grow faster than non-issuers prior to the issuance of securities, and this growth differential significantly widens as firms issue securities. The estimates indicate that the cumulative growth differential in sales, assets, and productive capabilities for issuers relative to non-issuers around the 5-year issuance window is about 19 percentage points. Furthermore, the increase in growth rates associated with equity issuances is larger than those associated with bond issuances. These findings are consistent with the view that firms use the funds raised in securities markets to boost their productive capabilities and realize growth opportunities.

Second, we find that the relation between firm growth and securities issuances is larger among firms with tighter financing constraints, which is consistent with the view that firms issue securities to finance growth opportunities. Specifically, if firms use securities markets to finance growth, then we should observe larger changes in growth following security issuances among more financially constrained firms because the marginal returns to increasing human, tangible, and intangible capital are likely to be greater. An extensive literature mentioned below finds that smaller, younger, and more innovative firms tend to be more informationally opaque and have less tangible assets to offer as collateral. Following this literature, we use firm size, age, and R&D (research and development) 2
by simulations on a previous or similar survey to gauge the performance of the estimation vis-à-vis the time savings in administering the questionnaire. Another consideration is that it is beneficial to balance the number of households for each optional module, ideally at the cluster level of the survey.

The next step is to select core consumption items. Although consumption in any given country will exhibit some variability, data on a few dozen key items will usually be sufficient to capture the majority of consumption. Important consumption items can be identified using average consumption share per household or across households, as estimated by previous consumption surveys in the same context or recorded consumption shares in neighboring and/or similar countries. While a good choice of core items will improve the performance of the estimation, the methodology still works if no core items are used, e.g. in a context without any prior information. The identified key items are then assigned to the core module that will be administered to all households.

Finally, non-core items are randomly partitioned into optional modules. It is important to note that the conceptual distinction between core and optional items should not be reflected in the layout of the questionnaire. Instead, all items per household need to be grouped into categories of consumption items

> 3 While the split-questionnaire design is more popular in other disciplines such as psychology (e.g. Graham, Hofer, and MacKinnon 1996), the approach has not yet been applied to large-scale household-based surveys, nor with the goal of reducing the time required to estimate consumption or poverty.

> 4 As is shown below, the core module is not strictly necessary, further reducing the interview time.

4
|**TABLE A1. Tests for differences be**<br>Variables|**tween include**<br>(1)<br>Mean and te<br>between incl<br>|**d and excluded s**<br>(2)<br>sts for differences i<br>uded and excluded<br>|**ubsamples**<br>(3)<br>n means<br>samples<br>|
|---|---|---|---|
||Included|Excluded|_p_-value|
|_Panel A. Maternal characteristics_||||
|Age of the mother (years)|31.43|33.73|0.235|
|Indigenous ethnic group|0.16|0.16|0.995|
|Less than primary school|0.57|0.42|0.085|
|Literate|0.79|0.77|0.567|
|Attended antenatal care in 2002|0.94|0.92|0.734|
|_Panel B.Child characteristics_||||
|Child is a boy|0.50|0.51|0.847|
|Weight at birth|3.21|3.20|0.835|
|Long-term health problems|0.09|0.08|0.123|
|Age (in months)|63.5|63.42|0.568|
|Child is the eldest|0.16|0.17|0.723|
|Height for age Z-score|-1.42|-1.62|0.167|
|_Panel C.Household characteristics_||||
|Wealth index|0.49|0.49|0.934|
|Housing quality index|0.41|0.42|0.
> (ii)<br>Predict|ed income & Asset in|dex (iii)|
|---|---|---|---|---|---|
|Elasticity<br>-0.3304|**-0.3778**|-0.2895|**-0.3424**<br>-0.2158|**-0.2177**<br>-0.1879|**-0.2416**|
|z-statistic<br>-8.38|**-6.37**|-8.96|**-7.53**<br>-5.03|**-3.50**<br>-5.01|**-4.61**|
|Pseudo R2<br>0.0334|**0.0539**|0.0327|**0.0552**|0.0367**0.0598**||

Source: Own computations using HLCS 2001 and DHS 2000 (2005 in bold) Table 3 shows that the pseudo R-square is approximately the same for the model with asset index and for the model with predicted income: it is slightly higher in 2005 for the former. Looking at the measures of correlations between actual and fitted values of the health and nutrition index in Table 4 shows that fitted values are better correlated to actual values when asset index is used as a regressor. Using both indices in a regression does not significantly improve the correlations. In conclusion, these findings suggest that analysts are not worse off, and may be better off, conditioning child nutrition models on the asset index rather than predicted income in their effort to predict nutritional outcomes and target programs.

10
that have been observed in the literature and highlights the importance of further research, particularly in developing countries. The remainder of this paper attempts to address this gap by examining the impact of FDI on market power within the developing country context of Vietnam.

# III. Methodology

This paper focuses on markups as our primary measure of market power. .<sup>3</sup> For the purposes of this paper, we modify this expression slightly to _P/MC_ (price divided by marginal cost) in line with De Loecker and Warzynski (2012) due to the ability to estimate this measure using firm-level data, but the interpretation remains the same. In conceptual terms, markups measure the ability of firms to exert their market power and price goods above the socially optimal outcome of prices equaling marginal costs under perfect competition. Thus, positive markups indicate market power and loss of social welfare. Nevertheless, in practice, firms may also charge prices above marginal costs in response to other incentives different than market power. For instance, firms may increase prices to cover high fixed costs, leading to higher markups in the market.

Market power can also be assessed by concentration measures such as the Herfindahl-Hirschman Index (HHI) or firm entry and exit data, which are commonplace among legal practitioners and regulators in part

> 3 The standard definition of markups is commonly termed the Lerner index as it is built upon the work of Lerner (1934). In practice, there are certain reasons why the Lerner index may not be a completely perfect measure of market power, but in general it provides a closer measure than more indirect approaches of studying market power such as measures of concentration. For more information, refer to the discussion in Elzinga and Mills (2011).

4
The paper focuses on staple cereal grains traded between 230 large regional hubs in 42 countries, and quantifies trade costs by estimating a structural model that features competitive traders (i.e., abstracting from markups), grain consumption, storage, and trade. The results in Porteous (2019) suggest trade costs that are qualitatively similar but higher in magnitude to those estimated in Atkin and Donaldson (2015), although the former potentially include nondistance dependent factors. Specifically, the median trade cost is over 5 times higher than international benchmark freight rates. In a counterfactual where trade costs on overland links are reduced to levels similar to the rest of the world, grain prices fall 30% on average and aggregate welfare increases by 1.6% of GDP. Teravaninthorn and Raballand (2009) estimate trade costs using trucker surveys in the U.S. and along several major transport corridors in Sub-Saharan Africa and find 1.88 to 3.28 times higher trade costs compared to the U.S., which are lower than the results of Atkin and Donaldson (2015). In terms of the trade costs in high-income countries, Agnosteva, Anderson and Yotov (2019) use gravity model techniques to understand the effects of intra-regional, inter-regional and international frictions on trade flows in Canadian provinces, and find that further and less-developed regions tend to have relatively low internal frictions and large border effects, while economically more developed and central regions show relatively low border barriers.

3
Internet‐based communication (e.g., Viber, WhatsApp). They seldom required more than three to four days of training to be able to manage the MIS. In many OSSs, annual turnover exceeded 30 percent, with many staff leaving shortly after they had been trained on the MIS. Turnover was also high in MFIs and the DBE, making training of staff on the MIS a continuous, ongoing process.

# **WEDP: A complex project**

Ethiopia has achieved high economic growth in the past decade, averaging 10.7 percent per year, establishing it among the fastest growing economies in Africa and the developing world, but it is falling behind its peers in terms of credit to the private sector. According to the World Bank Enterprise Surveys, micro (41 percent), small (36 percent), and medium (29 percent) enterprises in Ethiopia perceive access to finance to be the main business environment constraint, compared with Sub‐ Saharan Africa averages of 24 percent, 20 percent, and 16 percent, respectively.

Opportunities for female entrepreneurs in Ethiopia lag far behind those for men. In _The Economist_ Women’s Economic Opportunity index, Ethiopia ranks 107 of 112 countries. Most growth‐oriented female entrepreneurs fall into a “missing middle” trap, in which neither commercial banks nor MFIs serve them. High minimum loan sizes and excessive collateral requirements restrict women’s access to loans from commercial banks. MFIs primarily cater to micro‐firms with group lending schemes that provide very small loans and tend to have poor outreach to women (30 percent). Growth‐oriented women‐owned enterprises are therefore starved of the investment they need to thrive.

To address this challenge, the Government of Ethiopia, with the support of the World Bank and the Governments of Canada and the United Kingdom, launched the WEDP in 2012 to support women‐ owned micro and small enterprises (MSEs). The WEDP aimed to increase the income of female entrepreneurs and to create employment and job opportunities
these events should also be affected. To measure the existence and robustness of alliances, we use the Correlates of War (COW) data set (Militarized Interstate Disputes, v3.1). We include a dummy variable ( **_Alliance_** ) depicting whether an alliance exists between the riparians over the respective period of the dyad. The data for the alliance variable are derived for the 1945-2008 period.

# _<u>Power and Wealth</u>_

The literature provides a variety of views relating to the role of power in international hydropolitics. Some authors have claimed that an asymmetric inter-riparian power relationship impedes international cooperation over shared rivers (Hijri and Gray, 1998; Just and Netanyahu, 1998). Others (particularly those echoing neorealist assumptions) have argued that hegemony, or 10
where δ and β are the corresponding share parameters in the CET export transformation and CES import aggregation functions.<sup>9</sup> The CET function describes the production transformation frontier between D and E for a fixed level of X̅ or real GDP (since there are no intermediate inputs). The assumption that X̅ is fixed is equivalent to assuming full employment of all primary factors. The composite good price P<sup>x</sup> corresponds to the GDP deflator. The composite price P<sup>q</sup> of Q corresponds to the consumer price index. Following the numerical implementation in Devarajan et al. (1997), GDP not sold to the rest of the world (i.e., E, exports of goods and services) defines the domestic good D. Given price indices, P<sup>x</sup> and P<sup>e</sup> , the implicit price for the domestic good, P<sup>d</sup> , can be derived from the GDP identities: P<sup>x</sup> X̅ = P<sup>q</sup> Q+ P<sup>e</sup> E−P<sup>m</sup> M= P<sup>d</sup> D+ P<sup>e</sup> E where Q is aggregate demand. The model can therefore be implemented using national data for macro aggregates (see Devarajan, Go, Lewis, Robinson, and Sinko 1997).

# _Estimating Equation_

The log-linear transformation of the supply and demand equations (3) and (4) provides a convenient way to estimate the elasticities: Note that equations 5 and 6 extend beyond the 1-2-3 model. As mentioned in the literature review, the import equation derived from the CES function is also known as the Armington import demand in the trade literature. Exports are also often expressed as Armington import
# that "price influences choice among providers and does so more strongly than its influence on

whether or not to use services at all' _The larger the private sector, the smaller the impact ofpublic spending._ Using a recent update of health expenditures data, Table 4 shows that in nearly all poor countries the largest part of health spending is made by private individuals. The share of health expenditures that is made by private individuals is 63 percent in the low-income country group, and reaches almost 75 percent in South Asia.

Table 4: Percent of total health expenditures that are private

||Average|Number of countries|
|---|---|---|
|All countries|53.6|115|
||Income groups||
|Low income|<br>63.1|32|
|Low-middle income|42.8|36|
|High-middle income|54.0|15|
|<br>High income, non-OECD|62.0|10|
|<br>High income, OECD|32.6|22|
||Regions||
|East Asia and Pacific|53.2|14|
|Latin America and Caribbean|58.1|32|
|Middle East and North Africa|47.4|11|
|South Asia|74.8|_5_|
|Sub-Saharan Africa|59.6|19|
|Rest of World|29.3|34|

Notes: Average are population weighted means of countries from each region/group that have non-missing data.

Much of these private expenditures are likely made to private health care providers.

Indirect evidence of this flows from the fact that public care is free, or close to free, for the user in many countries, especially the poorer ones. Direct evidence is more rare. A recent study of health care in five Indian states estimated the share of the private sector in health care provision and the results summarized in Table 5 show that:
each fossil fuel induced by the climate mitigation policy. Sectoral fossil fuel intensities are obtained from direct requirements matrices of input-output tables. These tables are sourced from the GTAP-10 database,<sup>22</sup> which includes 2014 data for 65 sectors<sup>23</sup> that are, in turn, mapped to the CPAT non-fuel consumption good/service categories mentioned above to recompute equation (A). Summing the estimates across all non-fuel goods and services yields a measure of the loss in consumption from price increases of non-fossil fuel products (for example, food, clothing, housing, etc.) following the introduction of a carbon tax (that is, the ‘indirect’ incidence effect). Adding up the direct and indirect effects yields an estimate of the total incidence effect. We further adjust the total incidence effect downwards to

> 15 See: http://data.bbs.gov.bd/index.php/catalog/182 16 See: https://consumerpyramidsdx.cmie.com/ 17 See: https://microdata.worldbank.org/index.php/catalog/1000 18 See: https://www.pbs.gov.pk/content/household-integrated-economic-survey-hies-2018-19 19 See: http://www.statistics.gov.lk/IncomeAndExpenditure/StaticalInformation 20 To facilitate relative cross-country comparability of results, CPAT uses a standardized classification of goods and services across all countries, distinguishing among eight (8) fuel (coal, electricity, natural gas, oil, gasoline, diesel, kerosene, LPG) and fourteen (14) non-fuel (appliances, chemicals, clothing, communications, education, food, health services, housing, other, paper, pharmaceuticals, recreation and tourism, transportation equipment, public transportation) good/service categories. This classification is, in part, informed by the implicit carbon intensity of nonfuel goods/services (i.e., goods/services with similar carbon intensities are classified under the same category)
European Observatory on Health Systems and Policies (2006), “Health targets and (good) governance,” _Euro Observer: The Health Policy Bulletin of the European Observatory on Health Systems and Policies_ , 8(1), Spring.

Gaal, Peter, Paolo Carlo Belli, Martin McKee, Miklós Szócska (2006). “Informal Payments for Health Care: Definitions, Distinctions, and Dilemmas.” _Journal of Health Politics, Policy and Law_ , 31(2):251-293.

Gauthier, Bernard (2006). “PETS-QSDS in Sub-Saharan Africa: A Stocktaking Study,” HEC Montreal and World Bank, AFTKL. Washington D.C: World Bank. September.

Harding, April and Alexander S. Preker (2003), “A Conceptual Framework for the Organizational Reforms of Hospitals,” in A.S. Preker and A. Harding, eds. _Innovations in Health Service Delivery: The Corporatization of Hospitals_ . Washington, DC: The World Bank. pp. 23-78.

Hollingsworth, Bruce (2003). "Non-Parametric and Parametric Applications Measuring Efficiency in Health Care.” _Health Care Management Science,_ 6:203–218.

Kaufmann, Daniel and Aart Kraay (2008), “Governance Indicators: Where Are We, Where Should We Be Going?” _The World Bank Research Observer_ , 23(1):1-30.

Kaufmann Daniel, Aart Kraay and Massimo Mastruzzi (2007), “Governance Matters VI: Aggregate and Individual Governance Indicators for 1996–2006.” World Bank Policy Research Working Paper 4280. Washington, DC: World Bank. July.

Kotalik, Jana and Diana Rodriguez, eds. (2006). _Global Corruption Report 2006: Corruption and Health_ . London: Pluto Press and Transparency International.

La Forgia, Gerard M. and Bernard F. Couttolenc, eds. (2008). _Hospital Performance in Brazil: The Search for Excellence_ . Washington, DC: World Bank.

Lewis, Maureen (2006), “Governance and Corruption in Public Health Care Systems