rafmacalaba/datause-encoder-data huggingface dataset:
Tetui M, Ekirapa EK, Bua J et al (2012). Quality of antenatal care services in eastern Uganda: implications for interventions. Pan Afr Med J. 13: 27.

Thandar MM, Rahman MO, Haruyama R, Matsuoka S, Okawa S et al (2022). Effectiveness of infection control teams in reducing healthcare-associated infections: a systematic review and meta-analysis. Int J Environ Res Public Health 19(24): 17075.

Tumlinson K, Pence BW, Curtis SL et al (2015). Quality of care and contraceptive use in Urban Kenya. Int Perspect Sex Reprod Health 41: 69–79.

Umscheid C, Mitchell M, Doshi J, Agarwal R et al (2011). Estimating the Proportion of Healthcare-Associated Infections That Are Reasonably Preventable and the Related Mortality and Costs. Infection Control & Hospital Epidemiology 32(2): 101-114.

United States CDC (2023). Healthcare Associated Infections. Atlanta: Centers for Disease Control and Prevention.

Upadhyay RP, Rai SK, Krishnan A (2013). Using three delays model to understand the social factors responsible for neonatal deaths in rural Haryana, India. J Trop Pediatr. 59(2): 100-5.

Watson J, D'Mello-Guyett L, Flynn E et al (2019). Interventions to improve water supply and quality, sanitation and handwashing facilities in healthcare facilities, and their effect on healthcare- associated infections in low- income and middle-income countries: a systematic review and supplementary scoping review. BMJ Global Health 4: e001632.

White Ribbon Alliance (2019). What Women Want survey: Demands for quality healthcare for women and girls. Washington DC: White Ribbon Alliance.

Woldeyohanes TR, Woldehaimanot TE, Kerie MW et al (2015). Perceived patient satisfaction with in-patient services at Jimma University Specialized Hospital, Southwest Ethiopia. BMC Res Notes 8:285.

WHO (2020a). Global report on the epidemiology and burden of sepsis: Current evidence, identifying gaps and future directions. Geneva: World Health Organization.

WHO (2020b). Global antimicrobial resistance surveillance system (GLASS) report: early implementation 2020. Geneva: World Health Organization WHO and UNICEF (2020). Global progress report on water, sanitation and hygiene in health care facilities: fundamentals first. Geneva: World Health Organization; 2020. Licence: CC BYNC-SA 3.0 IGO.

WHO and UNICEF (2022). Progress on WASH in health care facilities 2000–2021: special focus on WASH and infection prevention and control (IPC). Geneva: World Health Organization and the United Nations Children’s Fund (UNICEF), 2022. Licence: CC BY-NC-SA 3.0 IGO Yallew WW, Kumie A, Yehuala FM (2016). Point prevalence of hospital-acquired infections in two teaching hospitals of Amhara region in Ethiopia. Drug Healthc Patient Saf. 8: 71.

Yallew WW (2017). Hospital acquired infections and infection prevention practice in teaching hospitals in the Amhara Regional State, Ethiopia. PhD Thesis. Addis Ababa University, Ethiopia.

Zaidi AK, Huskins WC, Thaver D, Bhutta ZA et al (2005). Hospital-acquired neonatal infections in developing countries. Lancet 365(9465): 1175-88.

28
calibrate our constant price elasticity demand and supply curves, we use elasticities found in the literature as well as the elasticities used for similar developing countries in the FAPRI model. We use Hamilton’s (2009) estimate of fuel demand elasticity of -0.26.<sup>15</sup> The demand for soybean oil for human consumption, demand for soybean meal for feed, and supply of soybeans, has price elasticities -0.15, -0.35, and 0.34, respectively.<sup>16</sup>

# _The Persistence of the Observed Production and Trade Patterns_

The objective of this section is to provide rough bounds on the key market prices and parameters for which one might expect the observed production, consumption, and trade patterns to be preserved. The observed patterns suggest that the price regime 4 in Table 1 prevailed in Zambia in 2010. This price regime implies that in order for domestic biodiesel production to begin, Zambia’s biodiesel producers need to be compensated for the difference between the market price of soybean oil and their willingness to pay for the feedstock to break even. The determination of this compensatory payment is crucial for modeling the effects of alternative biodiesel policies.

Sinkala (2011) reports a processing cost of soybean-based biodiesel in Zambia of $0.22/liter. Because the local market price of biodiesel in Zambia is $1.31/liter, using equation (5) we estimate the Zambian biodiesel producers’ willingness to pay for soybean oil to be $1,180/mt. The difference between the market price of soybean, $1,284/mt, and the willingness to pay then determines the compensatory payment which is equivalent to $0.10/liter of biodiesel.

In addition to the constellation of world commodity prices relative to domestic prices (Table 1), the stability of the observed production and trade patterns also depends on the processing cost for soybean oil and biodiesel. The maximum processing cost of soybean oil that leaves the observed Zambia’s production/consumption pattern unchanged, for given market prices, can be obtained from equation (3) as

> 15 This value is close to the average long-run elasticity obtained reported in a meta-analysis by Havránek et al. (2012).

> 16 The FAPRI elasticities database does not provide elasticities specifically for Zambia. We therefore use the elasticities reported for Brazil as both countries are developing countries.

11
dummy variable, _fincrisis_ , if any of those crises is reported.<sup>10</sup>

# 3.2.3 Trade and FDI variables

_Trade openness_ is conventionally captured as the sum of exports and imports over GDP. To account for the higher trade levels of smaller countries, irrespective of trade policies, we calculate _trade_resid_ as the residual of a regression of exports and imports over GDP on log of exporter population and global GDP, which acts as a demand shifter. We then take the log of the residual, which first requires transformation to positive values. This is done as: _ltraderesid_ = ln(trade_residmin(trade_resid) + 0.01). All the respective variables are taken from the PWT 10.0.

For _export diversification_ , we rely on the International Monetary Fund’s export diversification index (Papageorgiou, Spatafora, and Wang 2015), which is a Theil index. A higher value hence indicates lower diversification.<sup>11</sup> The variable is logged, _lEDI_ipol_ . Moreover, we take the square of the variable because a nonlinear or U- shaped relationship is expected (see Imbs and Wazciarg 2003; Papageorgiou, Spatafora, and Wang 2015, figure 3).

To account for positive and negative shocks due to changes in international price levels, we include _terms of trade changes_ , _d.ltot_ , calculated as changes in the log of the export price level relative to the import price level ( _pl_x/pl_m_ ), both taken from the PWT 10.0.<sup>12</sup> _Foreign direct investment_ acts as a proxy for the activity of multinational corporations, their investment and productivity spillovers. We take inward FDI stock as a percentage of GDP from the United Nations Conference on Trade and Development (UNCTAD).<sup>13</sup> The variable is logged, _lFDIstock_ip_ .

# 3.2.4 Infrastructure

_Infrastructure_ is strongly correlated with income levels, but it is difficult to measure, particularly over a longer time period (for surveys, see Välilä 2020; Timilsina, Hochman, and Song 2020). The empirical literature hence often uses a composite

> 10 Since the variable is constructed on annual data, it ranges from 0 to 0.6 for five-year periods, the latter indicating three years of crisis in a five-year period (3/5 = 0.6).

> 11 Since this index stops in 2014, we interpolate the missing years with the Herfindahl-Hirschman Product Index from UNCTAD. The correlation between both series in the sample is high, 0.90.

> 12 We consider terms of trade changes as shocks to income levels and hence include them in changes in our equation in levels (see the section on estimation and equation (2) below).

> 13 Since no stock data are reported before 1980, we fill the missing decade of data with an interpolation from FDI flow data (as a percentage of gross fixed capital formation). The sample correlation of both variables is high, at 0.87.

-9-
Table 5: Impacts on Networks

||(1)<br>Fam|(2)<br>ily|(3)<br>Frie|(4)<br>nds|(5)<br>Social|(6)<br>Media|(7)<br>School|(8)<br>mates|(9)<br>Train|(10)<br>mates|
|---|---|---|---|---|---|---|---|---|---|---|
|Offered Training|0.07<sup>_∗∗∗_</sup>|0.03|0.06<sup>_∗∗∗_</sup>|0.06<sup>_∗∗_</sup>|0.11<sup>_∗∗∗_</sup>|0.11<sup>_∗∗∗_</sup>|0.10<sup>_∗∗∗_</sup>|0.10<sup>_∗∗∗_</sup>|0.30<sup>_∗∗∗_</sup>|0.39<sup>_∗∗∗_</sup>|
||(0.02)|(0.03)|(0.02)|(0.03)|(0.02)|(0.03)|(0.02)|(0.03)|(0.02)|(0.03)|
|Offered * Round 2||0.07<sup>_∗_</sup>||0.04||-0.02||0.01||-0.15<sup>_∗∗∗_</sup>|
|||(0.04)||(0.04)||(0.04)||(0.04)||(0.04)|
|Offered * Round 3||0.06<br>(0.04)||-0.05<br>(0.04)||0.01<br>(0.04)||0.00<br>(0.04)||-0.13<sup>_∗∗∗_</sup><br>(0.04)|
|Control Mean|0.42||0.71||0.44||0.52||0.17||
|Control Mean R1||0.43||0.72||0.47||0.51||0.17|
|Control Mean R2||0.40||0.66||0.43||0.51||0.15|
|Control Mean R3||0.44||0.76||0.42||0.54||0.19|
|Observations|2,597|2,597|2,597|2,597|2,597|2,597|2,597|2,597|2,597|2,597|
|p-value Round 2||0.01||0.00||0.01||0.00||0.00|
|p-value Round 3||0.01||0.78||0.00||0.00||0.00|

Notes: Offered Training is the coefficient from a pooled OLS regression of the outcome (including three rounds of follow-up data, 4, 10, and 15 months after the end of the program) on an indicator for being offered the training. The dependent variable measures whether the participant discussed job opportunities with each of the respective groups. We control for the baseline value of the dependent variable, dummies for stratification and survey wave dummies. Standard errors are clustered at the respondent level.

21
The analysis, however, could go further. It could suggest how important agents (national governments, multinational organizations, multinational corporations, military and security establishments, and universities) are likely to react to their perception of these problems and then analyze whether the reaction functions and feedback loops tend to ameliorate or exacerbate the problems and whether they could lead to desirable transformative change.

The discussions and findings of megatrend-type studies should make everyone, particularly political and economic leaders, think about two important broad lessons. Although it is impossible to predict specific events with precision (particularly over a long period of time), it is possible to construct multiple scenarios around ongoing demographic and economic trends and assign judgmental probabilities to each. Less uncertain but much more difficult to envision and model are the needed (or implied) institutional changes at both the national and international level to deal with a vastly different world of multipolar growth and decentralized empowerment compared with the current set-up.

Even the most well-specified and sophisticated models will never be able to predict anything with accuracy over the long term. For complex systems, it is impossible to make accurate predictions beyond the short run, mainly because the endogenous variables (for example, economic growth or per capita income) are very sensitive to the determining variables, which are changing constantly, based on their own dynamics or on (unpredictable) policy changes. Moreover, major “exogenous” shocks (wars, revolutions, major terrorist acts, and so forth), including black swan–type events, could seriously affect (or even reverse) some or all ongoing major economic and demographic trends. Box 3 provides an example of this phenomenon.

As a result of the geopolitical shocks of the early 20th century, economic development followed a very different path from the trend (or that predicted by forecasters). By 1949, GDP of the G-5 was almost 300 billion in 1990 international dollars below the lower-bound prediction—an amount equivalent to 13 percent of actual output—highlighting the substantial and persistent effects of large, adverse geopolitical events.

62
and Alvarez 2015). In Jordan, where our study takes place 32 percent of respondents gave religious reasons for not seeking conventional loans, and in Syria a survey found that 43 percent of respondents cite religious reasons for not obtaining microcredit (Karim, Tarazi, and Reille 2008, reporting on draft IFC reports). Hence, no matter what the economics are behind the products, there is a large reported preference for sharia-compliant products among Muslims.

It is important to note that the above referenced surveys are not able to explicitly test whether individuals are actually turning down access to conventional financial products for religious reasons, or if they are simply claiming that religion is the reason why they are turning down these products alongside other considerations like price. Since many sharia-compliant products require additional layers in order to be religiously permissible (such as providing a loan in-kind), this can increase the cost, and subsequent price, of the loan. It may be the case that borrowers have access to sharia-compliant products but are unwilling to pay for them, and claim religious reasons as it may feel like a more acceptable answer for them to provide to surveyors.

Further evidence regarding the importance of religion in utilization of financial services comes from a crude cross-country comparison, reported in Appendix Table 1<sup>8</sup> . The table includes five different measures of financial inclusion across all countries in the developing world,<sup>9</sup> and regresses financial inclusion on an indicator variable equal to one if the country is majority Muslim, including a control for a third order polynomial of GDP per capita. People in Muslim majority countries are 3 percentage points less likely to have borrowed from a bank in the past year (a 24 percent decrease compared to countries that are not Muslim majority), and 1 percentage point less likely to borrow from an informal lender. On the other hand they are more likely to buy items on credit from a store and to borrow from family. The latter two behaviors are usually seen as more religiously acceptable forms of borrowing. The table also reports an 11 percentage point decrease in the likelihood of having any formal account with a bank (a 29 percent decrease). While cross-country regressions do not allow us to interpret these differences causally, and much may be omitted from our sparse specification, they provide suggestive evidence that religion contributes to reduced utilization of formal financial services in equilibrium.

> 8 Material in the supplementary appendix is available with this article at _The World Bank Economics Review_

website.

> 9 We use the International Monetary Fund’s definition of developing countries.

7
Policy Research Working Paper 8465

# **Abstract**

The recovery from the global crisis that erupted in 2007 shows that the decoupling between real and financial variables during the business cycle can lead to negative and long-lasting consequences for the economy. A key feature of the past global crisis in many countries is that the recovery in aggregate output has not been accompanied by a contemporary pick-up in lending flows to the private sector, rendering the recovery _credit-less_ . This paper uses data on output and credit to study the relative roles of demand and supply drivers of credit growth during economic recoveries on a sample of advanced and emerging countries between 1980 and 2014. Using a simple endowment economy model, the paper shows that credit-less recoveries are correlated with liquidity shocks in real and financial markets and with the pace of private sector deleveraging. The empirical analysis shows that during these episodes demand-side frictions played a relatively larger role in predicting the occurrence of the episodes, reflecting weak demand for liquidity by the private sector in the aftermath of the crisis.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at irossi1@worldbank.org and luisa.corrado@uniroma2.it.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|Trade Facilitation and ProductivityImprovement Technical Assistan|cOscar Avalle|
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|Trade Facilitation and ProductivityImprovement Technical Assistan|cOscar Avalle|
|Trade Facilitation and ProductivityImprovement Technical Assistan|cOscar Avalle|
|Trade Facilitation and ProductivityImprovement Technical Assistan|cOscar Avalle|
|Trade Facilitation and ProductivityImprovement Technical Assistan|cOscar Avalle|
|Trade Facilitation and ProductivityImprovement Technical Assistan|cOscar Avalle|
|Trade Facilitation and ProductivityImprovement Technical Assistan|cOscar Avalle|
|Trade Facilitation and ProductivityImprovement Technical Assistan|cOscar Avalle|
|Trade Facilitation and ProductivityImprovement Technical Assistan|cOscar Avalle|
|Education Sector Development Support Credit|Amit Dar|
|StrengtheningPublic Expenditure Management|Antonio S. Franco|
|PRIZMA technical assistancegrant|Syed Mesbahuddin Hashemi|
|PRIZMA technical assistancegrant|Syed Mesbahuddin Hashemi|
|PRIZMA technical assistancegrant|Syed Mesbahuddin Hashemi|
|PRIZMA technical assistancegrant|Syed Mesbahuddin Hashemi|
|PRIZMA technical assistancegrant|Syed Mesbahuddin Hashemi|
|PRIZMA technical assistancegrant|Syed Mesbahuddin Hashemi|
|Broad-Based Access to Financial Services|Patricia Caraballo|
|Broad-Based Access to Financial Services|Patricia Caraballo|
|Broad-Based Access to Financial Services|Patricia Caraballo|
|Broad-Based Access to Financial Services|Patricia Caraballo|
|Broad-Based Access to Financial Services|Patricia Caraballo|
|Broad-Based Access to Financial Services|Patricia Caraballo|
|Broad-Based Access to Financial Services|Patricia Caraballo|
|Broad-Based Access to Financial Services|Patricia Caraballo|
|Broad-Based Access to Financial Services|Patricia Caraballo|
|Broad-Based Access to Financial Services|Patricia Caraballo|
|Broad-Based Access to Financial Services|Patricia Caraballo|
|Broad-Based Access to Financial Services|Patricia Caraballo|
|Broad-Based Access to Financial Services|Patricia Caraballo|
|Broad-Based Access to Financial Services|Patricia Caraballo|
|Broad-Based Access to Financial Services|Patricia Caraballo|
|Broad-Based Access to Financial Services|Patricia Caraballo|
|Broad-Based Access to Financial Services|Patricia Caraballo|
|NWFP Structural Adjustment Credit Project|Paul Wade|
|Lucknow-Muzaffarpur National HighwayProject|Rajesh Rohatgi|
|Lucknow-Muzaffarpur National HighwayProject|Rajesh Rohatgi|
|Lucknow-Muzaffarpur National HighwayProject|Rajesh Rohatgi|
|Lucknow-Muzaffarpur National HighwayProject|Rajesh Rohatgi|
|Lucknow-Muzaffarpur National HighwayProject|Rajesh Rohatgi|
|Lucknow-Muzaffarpur National HighwayProject|Rajesh Rohatgi|
|Lucknow-Muzaffarpur National HighwayProject|Rajesh Rohatgi|
|Lucknow-Muzaffarpur National HighwayProject|Rajesh Rohatgi|
|Lucknow-Muzaffarpur National HighwayProject|Rajesh Rohatgi|
|Lucknow-Muzaffarpur National HighwayProject|Rajesh Rohatgi|
|Lucknow-Muzaffarpur National HighwayProject|Rajesh Rohatgi|
|Lucknow-Muzaffarpur National HighwayProject|Rajesh Rohatgi|
In other words, if apparel manufacturing is female-intensive, and the price of apparel decreases, the long run effect is a real decrease in the relative wages of women (in every industry).

The results in equations (3) and (5) can be straightforwardly applied to the empirical estimation through the traditional Mincerian wage equation. The Mincerian equation that is used regresses the log of the wages for worker _k_ at period _t_ on a set of worker characteristics such as years of education ( ), gender (using a dummy variable that equals the value of one if the worker is female); ; ; _j_ industry ( ) and _i_ occupational ( ) dummies, hours ( ) worked; and a remaining match-specific component that is captured in the residual term .

(6) We correctfor the possibility of selection bias that comes from the censoring of female wages by using the two-step Heckman approach. Participation is defined as having positive wage value, and the variables used in the selection correction equation are gender, age, age squared, years of education, and a series of dummies for marital status.

In our case, the effect of a decrease in industry _j_ ’s price would have two effects on wages. In the short run, the decrease in price would affect the industry-specific component of the wage and would show up as a contemporaneous decline in the estimated industry-specific coefficient  _j_ as implied by equation (3). The estimated coefficients on the industry dummy variables are interpreted as “inter-industry wage differentials” following Krueger and Summers (1987).

In the long run, the price decrease would affect the “general” component of wages. In our apparel industry application, as long as industry _j_ is female-intensive, any decrease in the price of industry _j_ will affect  1 , which represents the economy-wide returns to females.

One problem with applying this approach is that how does one determine what timeframe constitutes “the long-run.” Robertson (2004) provides one of the very few estimates of what the relevant timeframe is for the long-run and suggests that the Stolper-Samuelson effects begin to emerge in three to five years.

Working conditions can fit into this model too. The most straightforward way is to redefine the variable _w_ as compensation that includes working conditions such as bonuses, no mandatory overtime or unpaid overtime, or no use of child labor. Working conditions can be either substitutes or complements for wages. A compensating differential approach even suggests that wages and working conditions are substitutes: in this case, firms with poor working conditions need to pay workers higher wages to compensate them for enduring the poor conditions (and, consequently, pay them less for better conditions).

We constructthe country-specific measures of working conditions using the information from the household and labor force surveys. Conditions are considered favorable to the worker if he or she (i) is older than 14 years, (ii) works no more than 40 hours a week (or less than six days a week), (iii) has a low ratio of in-kind compensation over cash work, and (iv) has a high 7