rafmacalaba/datause-encoder-data huggingface dataset:
measured as the ratio of the current account of the balance of payments to GDP which data comes from the IFS.

In this paper we capture developments in domestic financial markets by the formation of booms in credit and asset prices because we argue that these markets may play a role in driving the likelihood of surging foreign capital inflows. In credit markets, we compute the growth rate of the credit to GDP ratio —which takes positive value whenever credit growth exceeds the growth in real economic activity. The data is collected from WDI. Overvalued asset prices are captured by the degree of real exchange rate overvaluation —which is roughly measured as the deviation of the REER index from its (HP-filtered) trend. The index of the real exchange rate comes from IFS and higher (lower) values for this index indicate a real appreciation (depreciation) of the currency.

Countries with greater linkages to the world goods and asset markets are arguably more prone to surges in inflows. Trade openness is measured as the ratio of export and import to GDP as collected from WDI. Financial openness, on the other hand, is computed as the ratio of gross inflows and gross outflows to GDP —with IFS data. We also argue that the likelihood of surges is not only determined by the extent of international integration but also the mode of integration. On the trade side, it is important to include an indicator of commodity trade. We measure _natural resource abundance_ as the trade balance per capita of primary exports (that is, agricultural raw materials, food, fuel and mineral ores and metals). Regarding financial openness, we distinguish between equity-driven gross inflows and debt-driven gross inflows that are expressed as a ratio to GDP.

_Push Factors_ . This group of determinants are external factors —more specifically, indicators capture trade-related external factors (say, growth in external demand), finance-related external factors (say, international interest rate and global risk aversion), and financial contagion.

_Trade-related_ external shocks are proxied by the growth rate of the foreign country. These are computed by the weighted average of the GDP growth of the main trading partners of the corresponding country. The weights are estimated using the bilateral trade between the corresponding country and each partner.

_Finance-related external shocks_ are proxied by indicators of performance and volatility in financial markets in advanced countries. The world real interest rate is proxied by the money market rate of the monetary anchor country —as suggested by Di Giovanni and Shambaugh (2008), and the data is gathered from IFS. Global risk aversion is proxied by the VXO index — as a measure of implied volatility computed using 30-day S&P 100 index at the money options. Higher values of the VXO indicate rising global risk aversion. Performance in stock markets of advanced economies is proxied by (average annual) returns to the US S&P 500 index while their volatility is computed as standard deviation of (annual) returns. Note that these two indicators are computed using monthly information of the S&P 500 index. We also include the leverage of 17
- 17 -

statistics is estimated at 47.9.<sup>19</sup> All the indications suggest that regardless of the level of competition, auctioneers would exogenously make their decision to adopt the substantial technical evaluation process. With our data, auctioneers tend to use it for civil work contracts and large power generation procurements. It suggests that the quality is of particular importance in large infrastructure projects, and auctioneers cannot easily substitute prices for quality even though competition is limited in such projects.

Regarding the bid equation, high priority attached to technical aspects would have significant impacts on the equilibrium bids. The coefficient is 0.43; this is considered the pure cost of requiring higher quality specifications, exclusive of the indirect impact of limited competition stemming from high technical thresholds.<sup>20</sup> The endogenous impact of the technical evaluation is controlled in the equation of ln _n_ as mentioned above, and the overall impact of competition is estimated at –0.56. That is, the realized competition is still a driving force toward lower procurement costs. The other coefficients are largely consistent with the above two-equation estimation results. Civil works are costly, but more applicants can be expected. Power generator installation is also costly, and fewer enterprises may be able to enter the procurement process. In addition, the technical evaluation is normally required for power generator works. By contrast, more bidders may participate in larger transmission and distribution contracts. And the coefficient of the engineering cost estimate is close to unity, as expected.

> 19 On the other hand, the number of bidders does seem to be exogenous in the equation of _DTE_ . The Hausman test statistics is –35.23, which can be interpreted as strong evidence that the exogeneity hypothesis cannot be rejected.

> 20 Formally, the Hausman exogeneity test cannot be rejected in the bid equation. For auction participants, both _n_ and _D_<sup>_TE_</sup> seem to be exogenously given at the time of bidding.
Table 7: Long versus Short Bonds, Developed versus Developing countries - OLS and Poisson

|Dependentvariables:|log(Longbonds)—|_log(Shortbonds)|Longbonds|Shortbonds|
|---|---|---|---|---|
||<br>(1)|<br>(2)|<br>(3)|<br>(4)|
|logMigration;;|0.152*|0.050|0.134***|0.142**|
||(0.078)|(0.109)|(0.028)|(0.072)|
|logMigration,;«(DevelopingCountry);|0.047|0.057|0.177***|0.010|
||(0.072)|(0.124)|(0.033)|(0.054)|
|logDistance;;|—0.463***|—0.790***|—0.332***|—0.144|
||(0.110)|(0.153)|(0.087)|(0.095)|
|Obs.|541|541|1,517|1,517|
|Obs. with Developed Borrowing Countries|395|395|643|643|
|Obs. with Developing Borrowing Countries|146|146|874|874|
|No. of Lending Countries|31|31|32|32|
|No. of Borrowing Countries|85|85|92|92|
|Estimator|OLS|OLS|Poisson|Poisson|

This table estimates the effect of migration on investment in long-term bonds and short-term bonds, with interaction with developing countries. N, L and B denote number of observations, number of lending (investing) countries and number of borrowing (issuing) countries, respectively. Regressions are estimated by OLS and Poisson. Standard errors are clustered at the borrowing country level. All columns include country fixed effects, colonial link, language, legal origin and common border as additional controls. The data is described in section II.
# **Figure 9: Labor force participation, interacted with time since arrival in the US. Males.**

<!-- Start of picture text --> 0.02<br>0<br>-0.02<br>-0.04<br>-0.06<br>-0.08<br>-0.1<br>-0.12<br>-0.14<br>Whites out > 20Whites out 1020Whites out < 10Blacks inBlacks out > 20Blacks out 1020Blacks out < 10Asians inAsians out > 20Asians out 1020Asians out < 10Hispanics inHispanics out > 20Hispanics out 1020Hispanics out < 10<br><!-- End of picture text -->

# **Figure 10: Labor force participation, interacted with time since arrival in the US. Females.**

<!-- Start of picture text --> 0.1<br>0.05<br>0<br>-0.05<br>-0.1<br>-0.15<br>-0.2<br>-0.25<br>Whites out > 20Whites out 1020Whites out < 10Blacks inBlacks out > 20Blacks out 1020Blacks out < 10Asians inAsians out > 20Asians out 1020Asians out < 10Hispanics inHispanics out > 20Hispanics out 1020Hispanics out < 10<br><!-- End of picture text --> Data from the 2000 Census IPUMS data (5% sample), population aged 25-65. The figures display the marginal coefficients and their 95% confidence intervals on interaction terms for racial groups, foreign born status (“in” for natives, “out” for foreign born; “<20” for arrived more than 20 years ago, “1020” for arrived between 10 and 20 years ago, “<10” for arrived less than 10 years ago) and time since arrival in the US in probit regressions with labor force participation as the dependent variable. “White natives” is the omitted dummy. The regression results are further detailed in table A4.

25
**Table 44. Importance of IPA services: Pre-investment Information (e.g. location guide, sector and project profiles, regulatory procedures)**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.0140|-0.0131|||-0.0611|-0.0649|||
||(0.0533)|(0.0518)|||(0.111)|(0.115)|||
|Export % of sales|0.000274|-0.000183|0.000405|-1.14e-05|-0.000315|-0.000897|5.68e-05|-0.000448|
||(0.000680)|(0.000653)|(0.000682)|(0.000666)|(0.00140)|(0.00142)|(0.00144)|(0.00149)|
|Share of sourcing via imports|0.000989|0.000703|0.000813|0.000599|0.00266*|0.00186|0.00243|0.00173|
||(0.000750)|(0.000774)|(0.000767)|(0.000781)|(0.00158)|(0.00170)|(0.00165)|(0.00176)|
|High-income source country|-0.152**|-0.0820|-0.156**|-0.0832|-0.342**|-0.196|-0.359**|-0.199|
||(0.0768)|(0.0766)|(0.0771)|(0.0772)|(0.169)|(0.171)|(0.173)|(0.176)|
|>250 employees|0.00645|0.0156|-0.00448|0.00779|-0.00922|0.0339|-0.0328|0.0176|
||(0.0685)|(0.0692)|(0.0694)|(0.0705)|(0.143)|(0.148)|(0.146)|(0.153)|
|Cumulative inv. >US$10M|0.0536|0.123*|0.0434|0.115*|0.148|0.260*|0.126|0.247*|
||(0.0643)|(0.0634)|(0.0646)|(0.0637)|(0.137)|(0.141)|(0.141)|(0.144)|
|Years in country|-0.000689|-0.00141|-0.000737|-0.00177|-0.00159|-0.00304|-0.00138|-0.00352|
||(0.00234)|(0.00221)|(0.00237)|(0.00221)|(0.00523)|(0.00500)|(0.00536)|(0.00507)|
|Level of foreign ownership (%)|5.49e-05|0.00134|3.65e-05|0.00132|-1.26e-05|0.00303|-6.79e-06|0.00293|
||(0.00119)|(0.00119)|(0.00118)|(0.00118)|(0.00252)|(0.00263)|(0.00256)|(0.00263)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.006|0.069|0.028|0.092|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How important are the following services offered by Investment Promotion Agencies to your company in this country? Please rate each on a scale from 1 to 4 where 1 is not at all important and 4 is critically important. [Pre-investment Information (e.g. location's guide, sector and project profiles, regulatory procedures)]". Higher numbers denote more greater importance. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

61
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- Robinson, J. A. and R. Torvik (2009): “A Political Economy Theory of the Soft Budget Constraint,” _European Economic Review_ , 53, 786–798.

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- ——— (2005): “Achieving Fiscal Discipline in Federations: Germany and the EMU,” Working paper.

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41
property tax exemptions to widows in general. The State Act of Chhattisgarh provides exemptions to widows of freedom fighters and retired members of the Defense Services. The State Act of Madhya Pradesh also provides exemptions to abandoned females. The Delhi Municipal Corporation Act provides exemptions to war widows.

In addition, seven urban local bodies (ULBs) provide discounts for _all_ female property owners. These ULBs are Pimpri-Chinchwad Municipal Corporation,<sup>10</sup> Pune Municipal Corporation,<sup>11</sup> New Delhi Municipal Council, East Delhi Municipal Corporation, North Delhi Municipal Corporation, South Delhi Municipal Corporation, and Greater Chennai Corporation. This was confirmed through interviews with government officials and from media reports in the _Times of India_ (June 2011) and the _Economic Times_ (June 2011).<sup>12</sup> See Table 4.

Table 4: Summary of the discounts in property tax offered by ULBs

|**ULB Name**|**Nature of property tax discounts offered**|
|---|---|
|Pimpri-Chinchwad Municipal Corporation and Pune<br>Municipal Corporation<sup>13</sup>|10% discount on properties owned by females for<br>properties up to 1,500 square feet|
|New Delhi Municipal Council<sup>14</sup>|25% discount to female property owners for self-<br>occupied properties only|
|East Delhi Municipal Corporation, North Delhi<br>Municipal Corporation, and South Delhi Municipal<br>Corporation<sup>15</sup>|30% discount on early payment of property tax by<br>female property owners on one property only and up<br>to 200 square meters<br>15% discount if property is owned jointly by a male<br>and female (also applicable only to properties with<br>areas less than 200 square meters)|
|Greater Chennai Corporation|Discount on timely payment by female property<br>owners|

Apart from tax discounts and rebates, states also offer other types of incentives. Information on these details is provided in Appendix 3.

> 10. Females interviewed for FGD groups as part of the study report that the discount is not actually provided.

> 11. Government officials interviewed as part of the study indicate that this discount is not actually provided.

> 12. This is not an exhaustive list, as it includes only ULBs approached to provide interviews.

> 13. _Times of India,_ June 2011, “10% tax sop for women property owners,”

> https://timesofindia.indiatimes.com/city/pune/10-tax-sop-for-women-property-owners/articleshow/8894807.cms. 14. New Delhi Municipal Council, “Property Taxes,” https://www.ndmc.gov.in/departments/property_tax.aspx. 15. Based on interviews with government officials.

11
endemicity for amphibians, mammals, reptiles and birds. We develop 50 measures that incorporate biome status, species density, endemicity, and extinction risk. We normalize each measure to the range [0,100] and develop a composite biodiversity index for each 500 m cell by selecting the maximum normalized value among the 50 measures for that cell. This approach ensures representation for diverse stakeholder concerns, which may range from preservation of critically-endangered amphibians to conservation of small ecoregions that are endemic to Lao PDR.

Available species range maps may not reflect the latest information on forest clearing, which is best represented by the global Hansen data set. Accordingly, we adjust our biodiversity indicator for each 500 m cell by multiplying its biodiversity indicator value by the Hansen estimate of percent forest cover remaining as of 2016.<sup>13</sup> The result is our summary estimate of current biodiversity risk.

To assess the potential impact of road improvement on biodiversity, we compute an expected loss measure for each cell by multiplying its forest-clearing-adjusted biodiversity index by the predicted increase in forest clearing percent from secondary and tertiary road upgrading. We map the results to identify priority areas with high expected losses. We find highly diverse patterns of expected forest clearing and biodiversity loss that would be difficult to anticipate without this kind of analysis.

We believe that these results highlight several important dimensions of ecological risk assessment and road investment policy in Lao PDR. First, newly-available data on forest clearing, species risks and ecoregions provide an unprecedented opportunity to assess ecological impacts at high spatial resolution. Second, assessment can be founded on a rigorous methodology that uses

> 13 As we explain in the paper, this requires combining the Hansen estimate of forest cover in 2000 with the estimate of forest clearing from 2001 to 2016.

34
## **Figure 1: The components of the REACH framework**

The framework defines key performance indicators (dark green bars) as the difference between the observed outcome (black, solid line) and the benchmark outcome (blue, dashed line). The difference is zero if the issuer did not perform and generated no additional impact. In this example, the issuer performed consistently (except for period 4) and reaches the benchmarked SPT after five periods.

<!-- Start of picture text --> Component Example<br>Benchmark outcome Expected deforestation<br>Observed outcome Observed deforestation<br>= outcome KPI<br>Benchmark – observed Avoided deforestation<br>= benchmarked KPI<br>Sustainability performance Forest preservation goal<br>target (SPT) over five years<br>Note: The benchmark outcome is a counterfactual and<br>is not observed. It needs to be estimated by a<br>statistical benchmark model.<br>0 1 2 3 4 5<br><!-- End of picture text --> Source: Authors.

(e.g. the “market portfolio” in the capital asset pricing model), such as the S&P500 or the MSCI World Index in the equity space. Good performance then means over-performing the benchmark (generating “alpha”), which is very similar to our benchmarking approach. However, while portfolio managers are concerned with risk-adjusted returns, we are interested in additionality and measurable impact.

The remainder of this paper is structured as follows. Section 2 introduces the REACH framework and gives examples for each of its components. It stipulates the three requirements for the benchmarking approach. Section 3 lays the theoretical foundation and formalizes the rationale behind paying for performance instead of outcomes. Section 4 shows an application of the REACH framework to Brazil’s Legal Amazon. Section 5 introduces the feasibility and ambitiousness matrix (FAB) and illustrates how to identify ambitious but feasible targets to curb deforestation. Section 6 discusses how the REACH framework and FAB matrix can be used for other instruments and support the development of SLBs in general.

# **2 The REACH framework**

The REACH framework consists of five components, as shown in Figure 1: (1) observed outcome, (2) benchmark outcome, (3) benchmark model, (4) benchmarked KPIs, and (5) benchmarked SPTs. This framework is consistent with the International Capital Markets Association (ICMA) guidelines for SLBs and can be seen as a special case of it, where benchmarking takes up a much more central role. In this section, we describe each component and provide examples in the 4. context of deforestation, which sets the stage for the subsequent application in Section

## **2.1 Observed outcome**

The observed outcomes are indicators, variables, or measurements that constitute the basic building blocks of the framework. These indicators are objective measures of the sustainability issue 4
# Comparison of different types of disaster management investments

<!-- Start of picture text --> GDP compared to baseline Potential GDP compared to baseline<br>42<br>}it} ae a Ae“an “SE“Se ae<br>¥\S\em \— { 2 ef sh<br>= wa — 4 j<br>4<br>s —— Only shocks LFA NAN -10___ Only shocks TIANy<br>~12 ——— Shocks and 100% of adaptation yo Shocks and 100% of adaptatib<br>—— shocks and 100% of insurance —— Shocks and 100% of insurance<br>—— Shocks and 100% of contingency fund -14_. shocks and 100% of contingency fund<br>16. Shocks and 100% of fiscal space -167—— Shocks and 100% of fiscal space<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>2 160<br>° 140<br>-2<br>120<br>-4<br>-6 100<br>-8 80<br>-10 ___ Only shocks 60 —— Only shocks<br>-12 —— Shocks and 100% of adaptati —— Shocks and 100% of adaptation<br>-14 —— Shocks and 100% of insurance 40 Shocks and 100% of insurance<br>Shocks and 100% of contingency fund —— Shocks and 100% of contingency fund<br>-16 ——— Shocks and 100% of fiscal space 207 Shocks and 100% of fiscal space<br>2020 2030 2040 2050 2060 2070 2080 2090 2100 2020 2030 2040 2050 2060 2070 2080 2090 2100<br><!-- End of picture text -->
Table 7: Interaction results from baseline specification using technology gap

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐TFP Ack|erberg_et al_. u|sing country|specific IO‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐TF|P Ackerberg|_et al_. using E|U‐wide IO‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|Overallt‐2|‐0.252***||||||‐0.161||||||
||(0.008)||||||(0.272)||||||
|Overallt‐2* Gap|‐0.455***||||||‐0.321||||||
||(0.003)||||||(0.327)||||||
|Entryt‐2||0.112|||0.151|0.161||‐0.030|||0.068|0.090|
|||(0.165)|||(0.125)|(0.108)||(0.852)|||(0.668)|(0.566)|
|Entryt‐2* Gap||‐0.309**|||‐0.786***|‐0.778***||‐0.172|||‐0.831***|‐0.763**|
|||(0.022)|||(0.000)|(0.001)||(0.458)|||(0.005)|(0.011)|
|Conductt‐2|||‐0.134***||‐0.304***|‐0.310***|||‐0.136||‐0.297***|‐0.302***|
||||(0.006)||(0.000)|(0.000)|||(0.122)||(0.003)|(0.003)|
|Conductt‐2* Gap|||‐0.305*||0.536*|0.553*|||0.207||0.924**|0.886*|
||||(0.063)||(0.072)|(0.068)|||(0.587)||(0.049)|(0.059)|
|FDI Overallt‐2||||0.190***||0.011||||0.122||‐0.029|
|||||(0.002)||(0.885)||||(0.270)||(0.795)|
|FDI Overallt‐2* Gap||||‐0.560**||‐0.354||||‐0.841*||‐0.535|
|||||(0.034)||(0.315)||||(0.052)||(0.230)|
|FE firm|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐YES‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|FE c‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐YES‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|FE s‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐YES‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|Observations|4983642|5431331|5064175|5848487|4983642|4983292|4966253|5413942|5046786|5818544|4966253|4965903|
|R2A|0.982|0.981|0.982|0.985|0.982|0.982|0.982|0.981|0.982|0.985|0.982|0.982|
|R2W|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|RMSE|0.130|0.135|0.129|0.135|0.130|0.130|0.130|0.135|0.129|0.135|0.130|0.130|

Note: * p<0.10; ** p<0.05; *** p<0.01. The dependent variable is in log. Robust standard errors clustered at the country‐industry‐year level. Technology gap is constructed following Aghion _et al_ . (2005) and is included as a separate control variable in the regressions.

35