rafmacalaba/datause-encoder-data huggingface dataset:
and health, the subjects of SDG 3 and SDG 4) and develop strategies to reduce those barriers.

- _Tools and modeling work._ The WBG has developed an SDG benchmarking and projection framework that has been applied to well over 16 countries (Gable, Lofgren, and OsorioRodarte 2015). This framework has been updated to benchmark countries in the areas of data, finance, and attainment of the SDGs (Amin-Salem et al. 2018) and has recently been complemented by a framework that allows for additional prioritization using network analysis (El-Maghrabi et al. 2018).<sup>17</sup> For particular SDGs, different tools are continuing to be developed to assist country programs. For instance, Climate and Disaster Risks Screening Tools and Climate Metrics have been developed to assist the operational work of the WBG. Global models were also developed to assess the impact of education demographics on global inequality and inequality within countries (Ahmed et al. 2017; World Bank 2018f).

## **D. Operational alignment**

To encourage compatibility of WBG operations with the SDGs, the WBG has also mapped corporate and IDA indicators to the SDG agenda. The WBG’s Operations Policy and Country Services (OPCS) has assessed the mapping of the SDGs to the indicators in the Corporate Scorecard and the IDA18 Results Measurement System (RMS) and, broadly, found strong consistency with the SDGs. The exercise shows that 16 of the 17 SDGs are covered by at least one corporate indicator, and that nine of the indicators in RMS are taken directly from the SDGs. For some projects, the WBG uses SDG indicators as project indicators.

## **E. Advocacy**

The WBG staff actively engage stakeholders towards achieving the 2030 Agenda for Sustainable Development. Advocacy activities, coordinated through offices in Washington, DC, New York, and Geneva, include building interagency and intergovernmental partnerships, presenting SDG progress in international fora, and representing the WBG in decision-making processes related to the SDGs.

# **IV. Advancing the Agenda Related to SDG Costing**

_Beyond costing exercises, the WBG has advanced the SDG agenda through multiple other channels. These include shifting the financing debate from simply more spending to ensuring spending efficiency, as well as building recognition for the vital role of policy reforms and crosssectoral synergies in the development of strategy to achieve the SDGs. The WBG is also deeply involved in the global efforts to expand the sources of financing for SDG-related spending—in particular, to the private sector. The WBG supports this work through its country-level engagements._

> 17 Recent modeling work related to the SDGs follows a record of modeling work on the MDGs, including through the Maquette for MDG Simulations (MAMS; Bourguignon, Diaz-Bonilla, and Lofgren 2008).

13
previously registered in various source lists maintained by different agencies (United Nations 2011). Such increases in efficiency entail a transparent process. Countries that have better access to procurement information online, complemented by an overall better accessibility to key information for suppliers (e.g. outcomes of consultation with the private sector, bidders’ questions, etc.), lead to the engagement of higher quality contractors that would manifest itself in the production of higher road quality.

The positive and significant correlation of the _Bid and Contract Management_ scores also supports previous findings in the literature. For example, mandatory minimum time to prepare and submit bids and the possibility of submission of bids through electronic means not only reduce transaction costs for bidders but also ensure higher participation, as preparing a bid can require hiring consultants, preparing plans, producing samples and performing other time-consuming tasks. This is the case in Georgia, where suppliers can submit their bids using the electronic procurement portal and bids are always opened electronically. This process has led to a rise in the number of tenders from 1,923 to 33,000 when the e-procurement platform reform was launched between 2010 and 2011. Similarly, in the United States procurement information such as the notice of calls for tender and tender documents all the way to notice of awards must be published online. Korea is yet another example where the digitalization of procurement has yielded benefits and reduction in costs. In addition to being able to access needed documents online, bidders in Korea may submit their bids and attend the bid opening session electronically from anywhere. In fact, the country has registered savings of US$4.5 billion with transaction costs on a total volume of US$44 billion in 2006. Furthermore, the electronic system in Korea has contributed to increasing efficiency in the public administration as well as preventing illegal practices and collusive acts 11
10 However, the regressions "explain" between 30 and 58<sup>per cent of quarterly</sup> variations in inflation leaving considerable deviations to be explained by other factors.

# 3 Credit expansion and monetary equilibrium

If credit expansion, one of the chief tools of monetary<sup>policy, does not have</sup> an important effect on inflation it is important to<sup>assess what its effects</sup> are. The openness of the economies suggest that<sup>the effects of credit policy</sup> on output and employment are also likely to be weak.<sup>Accordingly, one may</sup> suppose that if there is too much credit expansion,<sup>it will simply leak out</sup> into imports or capital movements; if there is too<sup>little, there will be made</sup> up by capital inflows. A visual examination (Figure<sup>4) of the trends in real</sup> money and credit suggests that credit expansion has had<sup>little impact on the</sup> determinants of money demand (with the possible exception of Cameroon<sup>before</sup> 1986). This view appears confirmed by the results<sup>of statistical analysis of</sup> the short- and long-term relation between domestic<sup>credit expansion and</sup> monetary growth in three of four countries examined.

redit and Monev: A Model A simplified monetary balance sheet indicates that the counterparts<sup>of the</sup> money stock are chiefly domestic credit of the banking system<sup>and the</sup> external reserves. We may regard domestic credit as subject<sup>to policy</sup> control while the other counterparts adjust endogenously. If an expansion in domestic credit leads to increased output or interest rates on a sustained basis, then demand for money can be expected to respond.<sup>However, if</sup> domestic credit expansion has no long-term effect on output<sup>(or interest</sup> rates), it will be offset by a reduction in the other counterparts, essentially through a fall in external reserves.

In order to explore this issue in the absence of an adequate database for<sup>a</sup> proper quarterly demand for money estimate, we inquire as<sup>to whether credit</sup> is an empirical proxy for unmeasured determinants of the long-term demand for money. Thus we write the demand for money as: M'-p.yC+6X, where C is domestic credit and X is other measured variables affecting<sup>the</sup> demand for money. The hypothesis to be tested is that the coefficient V is zero. We assume convergence of actual to desired money stock according to: _AM_ = _aAC_ + _t(M!4 - M_J._
**Table 11.A. Model comparison: Diebold and Mariano (1995) test for oil prices**

|||||**Bivariate**|||**Machine**|
|---|---|---|---|---|---|---|---|
|**Approach**|**Horizon**|**Consensus**|**Futures**|**correlations**|**BVAR**|**OEM**|**learning**|
|Consensus|3|...|||*|||
|Futures|3|*|...||*|*||
|Bivariate correlations|3|†||...|*|||
|BVAR|3||||...|||
|OEM|3|||||...||
|Machine learning|3|†|||*|†|...|
|Consensus|6<br>|...<br>|||*<br>|||
|Futures|6|†|...||*|†||
|Bivariate correlations|6|||...|**|||
|BVAR|6||||...|||
|OEM|6||||*|...||
|Machine learning|6||||*||...|
|Consensus|9|...|||†||†|
|Futures|9|*|...||*||*|
|Bivariate correlations|9|||...|*|||
|BVAR|9||||...|||
|OEM|9||||*|...||
|Machine learning|9||||†||...|
|Consensus|12|...|||†||**|
|Futures|12|*|...||†||**|
|Bivariate correlations|12|||...|*||*|
|BVAR|12||||...|||
|OEM|12|||†|*|...|*|
|Machine learning|12||||†||...|
|Consensus|15|...|...|...|†||***|
|Futures|15|...|...|...|...|...|...|
|Bivariate correlations|15||...|...||||
|BVAR|15||...|...|...|||
|OEM|15|†|...|...|**|...|**|
|Machine learning|15||...|...|||...|
|Consensus|18|...|...|...|*||***|
|Futures|18|...|...|...|...|...|...|
|Bivariate correlations|18||...|...||||
|BVAR|18||...|...|...|||
|OEM|18|*|...|...|***|...|***|
|Machine learning|18||...|...|†||...|
|Consensus|21|...|...|...|*||***|
|Futures|21|...|...|...|...|...|...|
|Bivariate correlations|21||...|...||||
|BVAR|21|||||||
|OEM|21|*|...<br>...|...<br>...|...|...|***|
|Machine learning|21||...|...|†||...|
|Consensus|24|...|...|...|**||***|
|Futures|24|...|...|...|...|...|...|
|Bivariate correlations|24||...|...||||
|BVAR|24||...|...|...|||
|OEM|24|*|...|...||...|***|
|Machine learning|24||...|...|*||...|

_Source_ : World Bank.

_Note_ : Diebold Mariano statistic tests whether the forecasts in the model indicated in the row is more accurate than the forecast of the model indicated in the column. *** indicates statistically significantly more accurate model in the row at the 0.1 percent significance level, ** at the 1 percent level, * at the 5 percent level, and † at the 10 percent level.

52
# **1. Introduction**

The link from macroeconomic stability to economic growth has been studied for some time in both academic and policy circles, most recently e.g. by Loayza et al. (2005) and Iradian (2007). The reverse link from better structural, pro-growth policies to enhanced macroeconomic stabilization is emphasized less often but is equally obvious. Stabilization policy is focused on maintaining price stability and unemployment levels close to the natural unemployment rate. These goals are typically outsourced by government to, preferably, an independent agent, the central bank which chooses a (explicit or implicit) nominal anchor, as an intermediate goal and a way to anchor the public’s expectations. The chosen nominal anchor then dominates in a reaction function of the central bank, i.e. the central bank adjusts its monetary policy instrument by putting the highest weight on sticking to the nominal anchor. The typical representations of the central bank’s reaction function, which we also consider in this paper, are due to due to Taylor (1993) for (explicit or implicit) inflation targeting, and Benigno et al. (2007) for exchange rate targeting. Both representations of the reaction function happen to be interest rate rules with a certain degree of discretion. Which of the two reaction functions will be more successful in stabilizing a given economy depends on the constraints that the economy places on achieving the monetary policy objectives. These constraints, in turn, can be significantly alleviated by appropriate structural reforms, which mainly promote the economy’s flexibility and responsiveness to changes in the monetary policy stance.

In this paper we consider two transition economies – Macedonia and the Slovak Republic – which happen to apply exchange rate targeting and inflation targeting regimes, respectively. We assume that the economies’ constraints to achieving the monetary policy objective of low inflation and output variability are represented by the consumption behavior of households, pricing behavior of firms, and interactions of the domestic economy with the external sector. We describe the above constraints using a small open-economy model with rational expectations where the model parameters are estimated using quarterly data for Macedonia and the Slovak Republic over 1995-2007. In the assumed model, the key elements of the constraints on monetary policy are real and nominal rigidities, and domestic and external structural shocks. We benchmark the differences in the estimated model coefficients and provide some explanations for the 2
Several quality control measures are taken. Riders take a photograph of their check-in and check-out station. The app geo-tags and time-stamps each observation when a sub-task is started. Riders take a photo and record the car number on which they ride. The app also included checks against riders changing the time settings on their phone. The different spaces are internally connected and riders are allowed to switch spaces after reporting on the space they boarded.<sup>13</sup> Riders are paid for each ride shortly after completion, and can choose to discontinue participation at any time. As a result, some riders only experimented with the application for a few rides. Table A1, panel A, shows the number of riders that progress through each of the study stages. Appendix D presents robustness checks to attrition in two forms: early attrition from the experiment and attrition (non-response) as a function of treatment assignment; we conclude that reporting through the app is unrelated to treatment assignment and, therefore, that attrition is unlikely to bias our estimates across all phases of the study.

# **Survey questions**

We administer two short surveys through the smartphone application. An initial demographic survey includes standard questions on age, employment, education, marital status, self-assessed socioeconomic status, home location and commuting patterns (timing, lines and frequency of riding SuperVia). Once a woman rider finishes her pipeline of ride tasks, she is invited to take an exit survey, which includes questions on topics that were not included in previous interactions to avoid priming effects. It includes a set of questions on riders’ stated preference as well as stated willingness to pay for the reserved space. Finally, we ask about the perceived risk of harassment across spaces, as well as the stated attitudes toward commuters using either space.<sup>14</sup>

> 13Upon checking out, riders are asked whether they switched spaces; they report doing so on 4-6 percent of rides across phases. We revisit this as a margin of adjustment in the results section.

> 14The complete questionnaires can be found online through this link.

11
<!-- Start of picture text --> Concentration<br>0.016 0.16<br>0.014 0.14<br>0.012 0.12<br>Gini<br>0.01 (right axis) 0.1<br>0.008 0.08<br>Theil<br>0.006 0.06<br>0.004 0.04<br>0.002 Herfindahl 0.02<br>0 0<br>1972 1976 1980 1984 1988 1992 1996 2000 2004 2008<br>Source: Authors' calculations<br><!-- End of picture text --> Figure 2: Concentration measures for growth polarity shares of top 15 economies, 1972–2008, smoothed by taking rolling five-year averages. Polarity shares are calculated from the real GDP growth rate. All three measures have trended downward since the mid-1970s Thus, the world has steadily become increasingly multipolar. This rising multipolarity has occurred in concert with the expansion of globalization. Indeed, history tells us that successive waves of economic globalization typically have wrought periods of greater economic multipolarity, along with concomitant frictions due to changes in the global configuration of geopolitical power (Findlay & O’Rourke 2007).

Concurrent with this rising multipolarity has been a shift away from the G-7 economies as global growth drivers, toward the economies of the developing world. This shift partly explains why the postfinancial crisis global environment has been marked by a sharp rise in international economic tensions, with heightened protectionist sentiment, accusations of exchange rate manipulation, and talk of trade collapse and currency wars.

Yet a deeper examination of the changes in the growth polarity indexes underlying Figure 2 suggests that the dynamics of what is captured in the figure are due not so much to a decline of developed economies (although some absolute decline, especially in the early 1970s, indeed occurred), but rather to a rise in the growth polarities of developing economies. Moreover, while structural changes in both the advanced and emerging world possibly could alter this dynamic, the overall trend toward a more multipolar world seems unlikely to change.

by the second in 1979). This negative shock was felt worldwide by all countries (apart from oil exporters), but the slowdown was more severe for the industrial world, which had relatively larger economies at the time. This resulted in a significant reduction in their respective growth polarities, and hence a corresponding decrease in the concentration indices. A secondary reason is that data coverage in the earlier years was not as comprehensive, and to the extent that higher polarity countries are omitted, the polarity share _P_<sup>_∗_</sup> used in calculations of (6)–(8) would have been affected. An examination of the distribution of the polarity index during this time suggests, however, that this latter concern is likely to be less of an issue, because the decline in the Herfindahl-Hirschman appears to be driven more by a significant reduction in the polarity value for the major economies of the euro area and the United States, rather than the introduction of high-polarity economies as the sample coverage improved.

24
main experiment, surveyors used the Jordan Microfinance Company name for the conventional loan option, and the Tathmeer name for the sharia-compliant loan option. The primary purpose of this auxiliary experiment is to test whether the “known” entity of Jordan Microfinance Company was trusted, by comparing it to an unknown entity, and keeping all else (including the lack of sharia-compliance) constant. This is not a dispositive test of trust, however, as we discuss in section 5. The unknown name “Tathmeer” led to 2.2 percentage point (se=1.1) lower take up than the control group known name “Jordan Microfinance Company.” This may lead to underestimating the demand for the sharia-compliant loan due to the difference in the brand of the financing entity.

5. Discussion of Results and Alternative Interpretations The results contribute to our understanding of demand for financial products in several ways. In this context there is clear evidence that consumer choice is dependent on more than just the economic fundamentals of the product. The actual economics and contract requirements of the two loan products are very similar for the consumer. This paper shows that low take-up is due not just to standard explanations of desirable economic product characteristics like prices, terms and borrowing requirements, but also whether or not the product is in line with the social and religious considerations of the consumer.

While there is strong evidence that borrowers had greater demand for the sharia-compliant loan, they were not influenced by which entity (if any) was declaring the loan sharia-compliant. In this context Islamic microcredit was still new and there were no certifying entities that would lead consumers to assume that any loan was legitimate. We expect that this result may differ in different contexts. For instance, in places where all loan products are under strict control by the state then certification may not matter. On the other hand, other contexts may follow versions of Islamic law that are more concerned with following the letter of the law which may make consumers more concerned with the certifying entity.

This paper also provides evidence that individuals who are more religious are willing to pay more to adhere to their religious obligations. While there is evidence of demand for microcredit products that adhere to Islamic law, there are not many sharia-compliant products available to individuals, even in Muslim-majority countries. Anecdotal evidence points to many lenders in the financial sector worrying about the added costs associated with shariacompliant products making those products uncompetitive in the marketplace. Our evidence shows that this may not necessarily be the case, and that even when the price of the sharia-compliant product is 16% higher than the conventional product (2.2% vs 1.9% monthly interest) three quarters of the sample still prefer the sharia-compliant 19
example, Lazard (2019) uses a 68% capacity factor for CSP technology that has a very high capital cost, US$7,950/kW, due to its 18-hour storage capacity. Therefore, we use a higher capacity factor for CSP technologies with high capital costs.

Table 1 presents the capital costs we used from these five sources. As can be seen from Table 1, some data are not available (n.a.), and some marked (n.u.) are available, but we did not use them as they were outliers. Some data are not available because the corresponding technology was excluded in the original study (e.g., hydro and biomass in Lazard).

It would be interesting to break down further the categories of technologies. For example, coal could be disaggregated by technologies – pulverized coal, fluidized bed combustion, integrated gasification combined cycle (IGCC) without carbon capture and sequestration (CCS), IGCC with CCS. Hydro could be divided into pumped storage hydro, small hydro, and large hydro. Large hydro could be further divided into run-of-river (ROR) and storage or reservoir type hydro. Nuclear technologies could also be divided between small-scale modular technology vs. largescale plants. Biomass technology could be divided by the type of fuel used, such as bagasse-fired biomass, rice husk-fired biomass, wood-fired biomass, and landfill gas-based biomass. However, having too many technologies is not only challenging from the data availability aspect but also makes the presentation of results possibly confusing. Instead of doing so, we present a wide range for a given source of power generation; the range covers all technologies within the source.

The values for other variables are presented in Table 2. A discount rate of 6% was chosen based on a recommendation in World Bank (2015a). Fixed and variable O&M costs are the average of available data from the five sources from which we took the capital costs (i.e., Lazard, IEA, EIA, IRENA and NREL). For fuel prices, we used international prices of coal and gas in the year 2019 from World Bank (2020). Heat rates are tied to capital costs. Power plants with higher thermal efficiency or low heat rates tend to be expensive. Capacity availability factors are standard values for a given technology in most cases. For concentrated solar power (CSP) and wind (both onshore and offshore), capacity availability factors are also tied with the capital cost. A CSP plant with storage facility costs more than $4000/kW. The storage facility increases its capacity availability factor.

10