rafmacalaba/datause-encoder-data huggingface dataset:
individuals or organizations (such as researchers, and the media) have access to this information, there is some chance that others who are interested in key variables will<sup>beableto obtainthe</sup> necessary information. Despite this fact, the measure of availability used in this paper almost surely overstates how much information on common economic data is<sup>easilyavailablein</sup> practice.

The index, thus constructed is used to assess the importance of information/transparency on institutional quality or governance. It is likely that countries that produce timely data on these indicators are more likely, on average, to produce more timely economic data of<sup>other kinds.</sup> The transparency index indicates how much information governments are<sup>willingto</sup> disclose - but the FOI law gives access to more than just economic data.

# A) Access to Information Index

Even when governments publish simple economic data, the people they<sup>governmaynot</sup> have sufficient information to judge outcomes and monitor performance. Information<sup>on a wide</sup> variety of activities that is not immediately encapsulated in the type of economic<sup>datadiscussed</sup> above, can be very important in ensuring accountability of government.<sup>Informationonhow</sup> decisions are made, as well as the immediate inputs into, and outcomes<sup>of,thesedecisionsare</sup> critical for monitoring purposes. A key question is how does society<sup>getinformation on what it</sup> wants and needs to know about its government? In many countries there are<sup>clearrulesorlaws</sup> which define the rights of individuals and private entities - often defined<sup>ingeneralterms in the</sup> constitution and defined in more detail in Freedom of Information (FOI)<sup>laws.The adoptionof</sup> FOI laws is quite recent in the case of most countries. As citizens around the world have become progressively more aware of their rights and have learned the value of<sup>adoptingsuchlaws from</sup> their neighbours, they have adopted FOIs. FOI laws may vary in both content and scope from 11
35 complete and there is a strong need to continue the adjustments. The authors calculate their own relative cost of living index comparing prices of the same consumption basket in a couple of Eastern European countries including Poland and Austria. The results are striking: the overall index for Poland in percent of the Austrian index equals 60.4%, the food index- 67.3% and the index for utilities - just 22.6% ! 37 While the calculations were made using data from March 1994, the size of undervaluation of utilities as compared to other CPI categories should not differ significantly as the relative prices of Polish utilities have hardly risen since 1994 (see Fig 6 and Fig. 7). Pujol and Griffiths also calculate the coefficient of variation that captures the extent to which individual ratios of commodity prices in both countries deviate from the overall index. The relatively high value of that coefficient for Poland suggests that undervaluation varies enormously for different goods. In particular, authors note that much of this variation comes from relatively high undervaluation of utilities. They conclude that while the low value of the overall cost-of-living index points to significant undervaluation of the Polish currency, the desired appreciation" will not eliminate the existing disparities within the index. Therefore, it is important to continue corrective adjustments of prices of utilities (which seem to be most undervalued) to bring the relative price structure closer to world levels. Upward adjustments in these prices are also important if the sectors are to be provided with full cost recovery as well as funds for necessary investments. Finally, bringing these prices up to the real levels would induce their proper economical use and discourage overconsumption.

The need for further adjustments entails the question of the optimal path of the process. Putting aside the implications of such adjustments in the sphere of political economy, what is the least distorting way of establishing higher relative prices ? Is the big-bang approach of large onetime increases more desirable than continuos gradual adjustments that take longer to achieve the same effect ? The results of the model estimated in Section 5 suggested that gradual adjustment is clearly preferred on the grounds that it does not contribute to wide and skewed relative price

> 38 Real appreciation has been indeed taking place systematically during the entire period 1989-1997, in particular since 1994 (Pujol and Griffiths' data) up to date.

37 The utilities index equals 73.6 % in Slovenia, 35% in Hungary, 29.6% in the Czech Rep. and 10.9% in Slovakia
<!-- Start of picture text --> 6% 55%<br>5%<br>4% 50% q<br>a<br>3% 9<br>2% 45% :<br>1% et<br>0% 40% pi<br>-1% %<br>-2% 35% 732<br>“vv<br>-3% fe)<br>30% =<br>-4% 7<br>5% 25%<br>-6%<br>-7% 20%<br>OC A & OD OD db db _@® MO O Aw OD AD wy AD 0D a 0 WO A<br>ONSWP SfSPRRSyNS eoSP WeRPFPSWPASss\WP SsEeAP PPP SsAS SsOeAP sREAP PP SoAP SPPWP SsEEWP SPPWP SsPPOP KS)GS” OPSY3PPP Ke KS oh cSOeSY AGM EenS” WPK'snO” LW cSnO” nO”<br>mm Main budget balance as a share of GDP (%) - left axis<br>== Real GDP growth (%) - left axis Lar ieee" saa<br>to downward-phase<br>«== Gross debt as a share of GDP (%) - right axis of the business cycle<br><!-- End of picture text -->
# Comparison across different amounts of disaster management investment

<!-- Start of picture text --> GDP compared to baseline Potential GDP compared to baseline<br>— 0% —— 20% —— 40% —— 0% —— 20% —— 40%<br>4 —— 60% —— 80% —— 100% 4 —— 60% —— 80% —— 100%<br>0 a a ne ne ee See Se 0 SS Se ee een<br>-4 -4<br>8 8<br>-12 -12<br>-16 -16<br>-20 -20<br>2020 2030 2040 2050 2060 2070 2080 2090 2100 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Consumption compared to baseline Debt/GDP<br>— 0% —— 20% —— 40% — 0% —— 20% —— 40%<br>2 —_— 60% —— 80% —— 100% 160 —— 60% —— 80% —— 100%<br>0 140<br>-2 \ era 1\ 120<br>»6 VWAAWYYYRYE R®<br>3 WAGAS 7 ame wo<br>-10 JY 40<br>-12 20<br>2020 2030 2040 2050 2060 2070 2080 2090 2100 2020 2030 2040 2050 2060 2070 2080 2090 2100<br><!-- End of picture text -->
# **1. Introduction**

Numerous empirical studies have investigated whether foreign aid effectively improves development outcomes in recipient countries. This literature has relied mainly on two levels of analysis. One focuses on the aggregate country-level impacts of aid, typically on economic growth or sector outcomes. Another takes a micro-level approach, with development projects as the unit of analysis, most often using donors’ own ratings of project outcomes as a measure of effectiveness.

This study bridges those two strands of research by focusing on the association between donor-financed projects and observable development impact, treating project ratings as intermediating variables. This enables us to ask whether project ratings convey information about those outcomes. We use ratings from projects undertaken in 183 developing countries by eight donors since the 1990s, concentrating on a few service delivery sectors with readily available data on beneficiary-level outcomes. We succeed in replicating previous findings of small positive effects of aid on sector outcomes. However, our results suggest that the project ratings convey little information about impact.

The second and more important contribution of this study is to describe and analyze the correlates of projects’ contributions to improvements in sector outcomes. Focusing on projects undertaken by the World Bank, for which more granular information and extensive text documentation are available, we use state of the art methods to assess what aspects of a project’s production process are associated with stronger outcomes. We first create what are called “text embeddings” of project documents using the latest generation of transformer models,<sup>3</sup> turning texts into numerical representations of their similarity and differences. Then, we train machine learning models to predict projects’ sector outcomes, and probe what features of the projects the model paid most attention to. We find that projects with what appear to be high degrees of tailoring to country context and concentration of funds in fewer sectors are associated with stronger outcomes. In doing so, we use newly available data on project characteristics and draw on methodological advances at the intersection of causal inference and econometrics with machine learning. To our knowledge, this is the first attempt to quantify the importance of project contextualization to development effectiveness.

Our findings have actionable implications for the system through which the World Bank and other development institutions evaluate project performance, by offering a cautionary tale against the over-reliance on project ratings as impact metrics. It also has implications for the design and staffing of these projects.

> 3The same class of models that power all state-of-the-art translation, search engines, AI text generators, as well as most plagiarism detectors.

1
# **4. Empirical Strategy and Identification**

This section discusses the empirical methodology, followed by the identification threats and how I assuage these threats.

## **4.1. Empirical Strategy**

The ESS is a panel household survey, providing room for investigation of the welfare effect of hosting refugees in a household fixed effects specification. In particular, I specify a linear model with fixed effects as follows: with fixed effects as follows: hv h + β β β β _I I v hv hv , (1) jj vv where hvYY is the outcome variable (e.g., one of the welfare outcomes) of a household = αα IIββI II + θθ ′XX + αα + εε h residing in EA during survey round . h stands for household-specific fixed effects. These fixed effects YY absorb any observed and unobserved time-invariant household heterogeneity, which might vv II αα otherwise bias the estimates. β β β I v is an index capturing the refugee intensity in a particular EA during survey round , which varies across EAs and rounds. It is measured as a ββ_I IIββI II weighted sum of the average number of refugees in the two years right before the year the outcome vv II variables are measured over all of the refugee camps or settlement sites in the country, whereby the weights are the inverse geographic distances between a survey EA and each of the refugee II camps or settlement sites , i.e., vv 37 II <u>( )</u> β β β I v , (2) = �� ~~�~~ ss(vv−1) ss vv−2 ββ + ββ ββ_I IIββI II vvss where and stand for the number of refugees in a refugee camp or settlement site ss=1 2DD in the two years (ss(vv−1) ss(vv−2) and , respectively) immediately before the year the outcome ββ ββ II variables are measured during survey round ; 37 stands for the total number of unique refugee (II−1) (II−2) camps or settlement sites, of which 23 are formal camps and 14 are semi-formal settlement sites, II including transit centers and entry points, and informal settlement sites in the country during the period under consideration (2009-2014); and stands for the ellipsoidal distance in kilometers from EA to the location of refugee camp or settlement site vvss . I use two years in the construction DD of refugee intensity, as there are two years between successive surveys. Given that the outcome vv II variables reflect values (almost entirely) in 2011, 2013, and 2015, the information on refugees that I use in the empirical analyses covers the period 2009-2014, during which the total number of 14
0. 8 and. 1 in the_ three cases.· However it is·_ interesting to notice that the other elasticities . seem. to · b.e · very well .estimated around. -::0. 6 ... for the interest semielasticity and: 1 for the scale elasticity. This simulation allows us to conclude that deviations from the assumptions of the estimation along the lines exp.lqred here,. will, bias only the estimate of -y.

The results of this Monte Carlo study implies that for the Mexican case, if the true process of the errors were like the ones examined here, we shoul.d lo.ok at .. an equation with a lower -y than the ML estimator. For instance·, for the processes assumed in.these .experiments, -y=0.7 s~ems tobe. a reasonable estimat;e (equatioI). 5, Table 4). In this case, the interest rate. ~lasticity in Mexico would be -0. 8 and the. consumption elasticity 1. 5, which is greater than the theoretical bounds ..

..; .

# 6. CONCLUSIONS

Attempting to disentangle the shifts in money demand from moyemertts along the money demand sched,ule is worthwhile pursuing. Episodes of velocity shifts are abundant in the empirical literature from inflationary countries. Whether oi; not velocity. shifts permanently after a stabilization plan is importan:t ·to understand. the .events a·fter such a plan takes place. In Mexico we ·e>bserved that velocity stays up after the stabilization of 1987 (Ratchet effect). The same result is observed in Israel after the stabilization of 1986, and in Argentina after the Austral Plan (a result recently challenged by Melnick1 1990). In contrast, in Chile .~fter the .1975 stabilization, a- recovery in velocity is observed, which occurs also with a process of financial deepening.

We . provide a framework which allows us to disentangle those shifts and estimate the other parameters (elasticities) simultaneously. Perhaps the.best 24
Figure A9: Number of Investing Firms into developing countries by source (United States and European Union), 2010-2022 <!-- Start of picture text --> Number of investors, 3-year moving average<br>110<br>EU<br>USA<br>100<br>90<br>80<br>70<br>2010 2012 2014 2016 2018 2020 2022<br><!-- End of picture text --> Note: The analysis relies on greenfield FDI project unique firm identifiers, without any exclusion from the full sample. The European Union group includes the European Union’s 27 members states and the United Kingdom.

Figure A10 <!-- Start of picture text --> Distribution of project capital expenditure<br>Greenfield FDI | Developing countries<br>.4<br>.3<br>.2<br>.1<br>0<br>-10 -5 0 5 10<br>FDI (log)<br>2003 2012 2022<br>Density<br><!-- End of picture text -->

Note: The analysis relies on greenfield FDI capital expenditure, without any exclusion from the full sample. Countries are considered developing if classified as low income, lower-middle income or upper-middle income in 2003 based on the World Bank’s income classification.

22
their energy portfolios and reduce reliance on imported oil. Through the 1980s and 1990s, concerns declined<sup>4</sup> . Dependency was lower, and so were oil prices.

Taxes have kept prices from dropping for products such as gasoline and diesel in many countries. In some cases this has been in lieu of environmental objectives and road user charges, however equally as important are the general fiscal objectives. In the past couple of decades, a consensus has been building among scientists that consumption of fuel, more specifically the release of anthropogenic carbon dioxide to the atmosphere, has been the primary factor behind the increase in temperature and climate change. Responsible policies required countries to once again reduce consumption of fossil fuel for dual purposes of energy supply security and greenhouse gas emission reductions.

Reducing carbon dioxide emissions and enhancing energy security have been the primary factors driving countries to reduce their oil consumption. Most countries throughout the world (though there are exceptions among oil exporting countries) take some measures to conserve energy through gasoline taxes and/or mandated increases in fuel efficiency standards.

Across the world, there are alternative policies in place to help reduce gasoline consumption (See, among others, Schipper et al, 1999). Policy makers in the United States, the European Union, China, etc. use both mandated fuel efficiency standards as well as fuel taxes<sup>5</sup> . One important argument in favor of fuel efficiency standards is based on the assumption that car buyers, and private individuals in particular, care very little about future savings in fuel consumption. In economists’ terms, people discount the benefit from expected fuel savings at a higher rate than what is socially optimal. If they do, they do not offer car makers enough of a premium for more efficient cars, thus car makers and inventors have suboptimal incentives to build (and invent) more efficient cars. This

> 4 In fact, according to IEA data, in OECD, government research on energy technology almost halved between 1985 and 2000 (Alfsen and Eskeland, 2007).

> 5 We shall use the term fuel taxes, in part because our point is a general one about a variable input with negative external effects, and due to the illustration we make about vehicles and transport, we should think about ‘fuel’ as gasoline and diesel.  The terms ‘car’ and ‘vehicles’ carry the more general meaning of ‘machine’.  The terms fuel economy standards and fuel efficiency standards are used interchangeably.

4
11 up to 1970. All the other industries had either no assistance (maize, oats, rapeseed, soybean, bananas, olives, wool, beef, sheepmeat, pigmeat, chicken meat) or a very small weight in the total gross value of farm production. For the latter category, NRAs are assumed to be: apples 6 percent, oranges 25 percent and potatoes 8 percent for the 1950s and 1960s, and zero for all three crops prior to 1950-51.

To obtain the weighted averages for agriculture as a whole, shown in Table 3, we first assumed the NRA was zero for products not covered by the above estimates and ‘guesstimates’. This is reasonable since those not covered are mostly small horticultural industries which are not subject to market interventions (apart from quarantine restrictions, which are in place purportedly for reasons of health rather than economic protection reasons). As weights we used gross value of production at undistorted prices, calculated by dividing that value at domestic producer prices – or, equivalently, the NRAs – by (1+NRA/100).

The fourth row of Table 3 includes ‘non-product-specific’ assistance (including all assistance via factor and intermediate input markets, even though some of that is in fact product-specific), estimates of which are taken from the Productivity Commission for the period from 1970. We assume them to have been 1.8 percent of the gross value of farm production in 1969, 2 percent in 1960, 0.5 percent in 1946, and linear interpolations between those years.
**<u>Table 7: Estimation results for number of transfers by gender of transferor, 2000-2019</u>**

||<br>**Total**|<br>**Male**|<br>**Female**|**Joint**|
|---|---|---|---|---|
|**Panel A: Entire sample**|||||
|Spor. reg. plots pre-2010 (_θ1_)|0.0223***<br>(0.0014)|0.0103***<br>(0.0014)|0.0250***<br>(0.0020)|0.0712***<br>(0.0056)|
|Spor. reg. plots post-2010 (_θ2_)|0.0410***<br>(0.0010)|0.0400***<br>(0.0020)|0.0270***<br>(0.0015)|0.0318***<br>(0.0011)|
|Syst. reg. plots post 2010 (_θ3_)|0.0039***|0.0086***|0.0026***|0.0024***|
||(0.0004)|(0.0013)|(0.0005)|(0.0003)|
|Constant|-0.0388***|-0.0040|-0.0086|-0.0004|
||(0.0123)|(0.0076)|(0.0063)|(0.0061)|
|No. of obs (blocks)|38,600|38,600|38,600|38,600|
|Linear combinations|||||
|_θ2_-_θ1 _|0.0187***|0.0297***|0.002|-0.0393***|
||(0.0020)|(0.0027)|(0.0028)|(0.0060)|
|_θ3_-_θ2 _|-0.0371***|-0.0314***|-0.0245***|-0.0294***|
||(0.0010)|(0.0024)|(0.0015)|(0.0011)|
|_θ3_-_θ1 _|-0.0185***|-0.0017|-0.0225***|-0.0687***|
||(0.0014)|(0.0018)|(0.0021)|(0.0056)|
|**Panel B: Restricted sample**|||||
|Spor. reg. plots pre-2010 (_θ1_)|0.0306***<br>(0.0045)|0.0102***<br>(0.0037)|0.0180**<br>(0.0074)|0.0367***<br>(0.0124)|
|Spor. reg. plots post-2010 (_θ2_)|0.0263***<br>(0.0030)|0.0122**<br>(0.0060)|0.0228***<br>(0.0029)|0.0129***<br>(0.0030)|
|Syst. reg. plots post 2010 (_θ3_)|0.0039***|0.0071***|0.0051***|0.0018***|
||(0.0008)|(0.0026)|(0.0012)|(0.0007)|
|Constant|-0.1033***|-0.0132|-0.0105|-0.0001|
||(0.0298)|(0.0181)|(0.0161)|(0.0100)|
|No. of obs (blocks)|9,100|9,100|9,100|9,100|
|Linear combinations|||||
|_θ2_-_θ1 _|-0.0043|0.0019|0.0048|-0.0239*|
||(0.0061)|(0.0075)|(0.0085)|(0.0131)|
|_θ3_-_θ2 _|-0.0225***|-0.005|-0.0177***|-0.0111***|
||(0.0031)|(0.0067)|(0.0031)|(0.0031)|
|_θ3_-_θ1 _|-0.0268***|-0.0031|-0.0129*|-0.0349***|
||(0.0045)|(0.0044)|(0.0075)|(0.0123)|

_Note_ **:** Standard errors in parentheses: * p<0.10, ** p<0.05, *** p<0.010 27
same social value. The only obvious exception is Punjab, whose general population enjoys comparative affluence and wherein the project will involve much upgrading of existing roads rather than the construction of new ones. In Punjab, it is arguable that private benefits should be discounted somewhat (see below). The same applies to some communities in Himachal Pradesh.

Constructing and maintaining roads in India is a pretty labor-intensive business, so there are good grounds for adjusting the corresponding costs at market prices. Theory and empirical estimates in various developing countries suggest that the socalled conversion factor for such construction work is typically 0.8 to 0.85, with a corresponding reduction in the present value of lifetime costs at any given discount rate, so there will be some help from this quarter where social profitability is concerned.

# **3 The Cost of Construction and Maintenance**

Let the construction of a single feeder road take one year (in _t_ = 0) and cost _K_ at market prices. Routine maintenance thereafter costs _m_ percent of _K_ annually; the black-top must be renewed every ten years, at a cost of _m_ + _m_<sup>_′_</sup> percent of _K_ . Thirty years after completion of the initial construction, let wholesale reconstruction be necessary, albeit with a salvage value that is _κ_ percent of the initial outlay, which represents the rightof-way and any useful residual structures, including the road-bed. We can then confine the calculations to the first 30-year cycle. The present value of the entire stream of expenditures at SPs is where _πK_ denotes the conversion factor for such construction.

We turn to the actual numbers, at least as forecast. The SWAp loan budgets a total expenditure of $3.014 billion to construct 48,736 km. of roads in the participating states of Jharkhand, Himachal Pradesh, Rajasthan, Meghalaya, Uttarakhand, Uttar Pradesh and Punjab (World Bank, PAD, 2010, Tables 2.2 and 3.3), which implies an average cost of $61,820 per km. This addition to the network will connect 14,274 habitations. Taking the habitation as the unit of analysis, we have _K_ = $211 _,_ 000 for a link-length of 3.41 km. Associated estimates are _m_ = 0 _._ 01 _, m_<sup>_′_</sup> = 0 _._ 35 and _κ_ = 0 _._ 25.

8