rafmacalaba/data-use-mentions-extended huggingface dataset:
to SME performance. For example, Ayyagari _et al._ (2007) find that lower costs of entry and better credit information sharing are associated with a larger size of the SME sector, while higher exit costs are associated with a larger informal economy. The overall banking structure is another important factor. Shen _et al._

> 1 See the Pittsburgh G-20 Summit “Leaders’ Statement” at http://www.pittsburghsummit.gov.

> 2 Enterprise Analysis survey data at http://www.enterprisesurveys.org/; IFC (2009); Beck _et al_ (2008b).

> 3 For earlier research, see Hartwell (1947) who reports a negative correlation between loan size and interest rates, and Murphy (1983) who argues that small loans to small firms are more costly for the lenders.

> 4 Studies that focus on a specific country report similar results. Stephanou and Rodriguez (2008) find that access to credit is one of the biggest constraints for SMEs in Colombia; Binks and Ennew (1997) find that the main constraints to growth of SMEs in the UK include management, labor skills, regulation and lack of access to finance; Hutchinson (2006) compares the cases of a leading transition country, Slovenia and an established market economy, Belgium, and finds that the SME sector in Slovenia remains underdeveloped, mainly due to the inability to raise external finance. Studies by Anderson and Kegels (1997), Budina _et al._ (2000), Gros and Suhrcke (2000), Konings _et al._ (2003) all indicate that this appears to be the case in most of Central and Eastern Europe.

3
Citi (2021). Global Asset Allocation: How Much Crypto? Citi Research Viewpoint.

CryptoCompare (2021). Exchange Review October 2021.

Demirgüç-Kunt, A., Klapper, L., Singer, D., Ansar, S., and Hess, J. (2017). The Global Findex Database 2017. World Bank Group.

Dixon, C. (2018). Why Decentralization Matters. Medium.

Feyen, E., Frost, J., Natarajan, H., and Rice, T. (2021). What Does Digital Money Mean for Emerging Market and Developing Economies? In the Palgrave Handbook of Technological Finance (pp. 217-241). Palgrave Macmillan, Cham.

Financial Action Task Force (FATF) (2020). Financial Action Task Force - Annual Report 20192020.

Financial Action Task Force (FATF) (2021). Updated Guidance for a Risk-Based Approach to Virtual Assets and Virtual Asset Service Providers.

Financial Stability Board (FSB) (2018a). Crypto-asset Markets: Potential Channels for Future Financial Stability Implications.

Financial Stability Board (FSB) (2018b). Crypto-assets: Report to the G20 on the Work of the FSB and Standard-setting Bodies.

Financial Stability Board (FSB) (2020). Regulation, Supervision and Oversight of “Global Stablecoin” Arrangements.

G7 Working Group on Stablecoins (2019). Investigating the Impact of Global Stablecoins.

G20 Finance Ministers & Central Bank Governors (2018). Buenos Aires Summit Communiqué. Gensler, G. (2021). U.S. SEC Chair Remarks Before the Aspen Security Forum. August 3.

Global Financial Markets Association (GFMA), Financial Services Forum (FSF), Futures Industry Association (FIA), Institute of International Finance (IIF), International Swaps and Derivatives Association (ISDA) and Chamber of Digital Commerce (CDC) (2021). Comments in Response to the Consultative Document on the Prudential Treatment of Crypto-Asset Exposures.

Graf von Luckner, C., C. Reinhart, and K.
.055|0.455|3.055|0.417|1.620|0.571|
|2009|3.055|0.545|3.055|0.667|1.620|0.714|
|2010|3.055|0.545|3.055|0.583|1.620|0.571|
|2011|0.535|0.909|0.535|0.917|−4.520|0.429|
|2012|1.590|0.818|1.590|0.917|−5.210|0.429|
|2013|1.275|0.545|1.275|0.750|−5.900|0.286|
|2014|0.960|0.909|0.960|0.750|−6.590|0.286|
|2015|0.960|0.909|0.960|0.750|−6.590|0.286|

_Source_ : Authors’ analysis of the Polity IV index. Additional data come from the Polity IV dataset, Freedom House, the Database of Political Institutions (DPI), and the JuriGlobe database. _Note_ : Estimated treatment effects on Jordan’s Polity IV score and corresponding permutation test _p_ -values that indicate the fraction of estimated treatment effects that are larger than the estimated effect on Jordan’s Polity IV score following the 1990 refugee surge. OIC denotes the full Organisation of Islamic Cooperation donor pool; OIC minus PAK denotes the OIC donor without Pakistan; and OIC SAL denotes the donor pool limited to OIC countries that received Structural Adjustment Loans (SALs) from either the World Bank or International Monetary Fund (IMF).

**Table S1.4.** Goodness of Fit Estimates for Synthetic Jordan, Polity IV Index

||**OIC all**|*
**Figure 1:  Average Disbursement Profiles**

<!-- Start of picture text --> All Creditors<br>0.25<br>0.2<br>All Creditors<br>0.15<br>0.1<br>0.05<br>0<br>0 1 2 3 4 5 6 7 8 9 10<br>Years Since Loan Commitment<br>Bilateral Versus Multilateral Creditors<br>0.35<br>0.3<br>Multilaterals<br>0.25<br>Bilaterals<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>0 1 2 3 4 5 6 7 8 9 10<br>Years Since Loan Commitment<br>Note: This figure reports average disbursement profiles on loans from official creditors to developing country<br>government, defined as the fraction of the initial loan commitment disbursed in the year of loan approval (year<br>zero) and subsequent years (years one through 10), averaging across loans. The top panel reports averages across<br>all 59,184 loans included in my loan-level dataset. The bottom panel reports average disbursement profiles<br>separately for loans from multilateral and bilateral creditors.<br>Fraction of Original<br>Commitment Disbursed<br>Fraction of Original<br>Commitment Disbursed<br><!-- End of picture text --> 32
Figure 3: Aggregate Headcount Poverty Rates – GHS (USD 1.90$ International Poverty Line) a) <!-- Start of picture text --> Aggregate Poverty Rates -GHS (1.90$ International Poverty Line)<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>child 0-4 child 5-9 child 10-14 child 15-17 adult 18-59 adult 60above adult child<br>Wave 1 Wave 2 Wave 3<br><!-- End of picture text --> b)

# **A G G R E G ATE P OV E R TY R A TE S - G HS (1.90$ INTE R NA TIONA L P OV E R TY LINE )**

<!-- Start of picture text --> adult child child north child south child rural child urban<br>W A V E 1 W A VE 2 W A V E 3<br>69%<br>57% 56% 59% 60% 63%<br>45% 48% 50%<br>33% 28% 24% 35% 31% 24% 36% 24%<br>18%<br><!-- End of picture text --> Source: Authors’ estimations based on GHS Wave 1-3. Note: Individuals with missing age were dropped from the analysis.

Figure 4 a): Rural- Urban Headcount Poverty Rates (USD 1.90$ International Poverty Line) Figure 4 b): North-South Headcount Poverty Rates (USD 1.90$ International Poverty Line) <!-- Start of picture text --> Rural-Urban Poverty Rates - GHS North-South Poverty Rates - GHS<br>(1.90$ International Poverty Line) (1.90$ International Poverty Line)<br
0.9|85|1.3|56.8|1520|
|Pakistan|0.9|85|1.3|58.1|420|
|Zambia|0.8|9|0.1|58.2|340|
|Ethiopia|0.8|4|0.1|58.3|100|
|<br>Guinea|0.5|3|0.0|58.3|410|
|Bolivia|0.4|5|0.1|58.4|900|
|Gambia|0.4|0|0.0|58.4|270|
|Ghana|0.3|5|0.1|58.5|270|
|United States|0.3|2121|31.5|90.1|35400|
|Azerbaijan|0.3|6|0.1|90.2|710|
|**WORLD**<br>Source: FAOSTAT|**0.1**<br>database at|**6656**<br>www.fao.o|**100.0**<br>rg<br>,except f|**100.0**<br>or final col|**5510**<br>umn whic|

Source: FAOSTAT database at <u>www.fao.org, except for final column which is from the</u> World Bank’s _World Development Indicators 2004_ for the year 2002.
variables remain significant and with the expected signs. Additionally, we document the robust positive association with LAGEDEP, CONCENTR., PRIVATE, and LPC. However, in these full models, LEGAL_RIGHTS and LCONTRIB_RATE lose statistical significance.

# _Dynamic panel regression results_

Most of the factors we analyzed are arguably exogenous, thus reducing the problem of determining the direction of causality. However, two factors in particular could be endogenous: PC and CARS. Therefore, we adopt an instrumental variable approach á la Arrelano and Bover (1995) using the same sample of annual country-level data for the period 2000-08. This method controls for country-fixed effects which capture time invariant characteristics of the country such as the average level of economic development. It also uses internal and external instruments for PC and CARS to address causality. Note that since the models include a 1-period lag of the insurance variables, the interpretation of the coefficients is slightly different because they effectively capture the effect on insurance activity _growth_ .

Following previous sections, in each of our regressions, we include the baseline variables LPOP, LPOP_DENSITY, LINFLATION. For consistency with previous regressions, we show results with and without LGDPPC. However, we find that it is difficult to fully disentangle the contemporaneous effects of LGDPPC and our drivers, particularly in the presence of countryfixed effects.

As exogenous (internal) instruments, we use the following set: LPOP, LPOP_DENSITY, (French and British) legal origin and religion dummies. In our specifications, we consider LGDPPC and INFLATION to be (possibly) endogenous. We include 2- to 4-period lags as internal instruments for the endogenous variables. Depending on the specification, the regressions pertain to 71 up to 82 countries.

Table 9 presents the results. Models (1) and (2) confirm the causal effect of LPC on LLIFE, although the inclusion of LGDPPC produces a p-value for LPC of 0.145. Model (1) also finds consistent results for LPOP and LPOP_DENSITY and shows the causal effect of LGDPPC 24
C7a_startyear1 What year did you start this MAIN job?

[YYYY]

**[If don’t know then put “Don’t Know”=888]** **<u>[If refused then put “Refused”=999]</u>**

C7b_startmonth1 In which month of the year did you start this MAIN job?

1. Jan

2. Feb 3. Mar 4. Apr 5. May 6. Jun 7. Jul 8. Aug 9. Sep 10. Oct 11. Nov 12. Dec 888. Don’t know 999. Refused C8_howfind1 How did you find this MAIN job?

# **[DO NOT READ CHOICES BELOW. LISTEN TO SPONTANEOUS RESPONSE AND SELECT APPROPRIATE CODE FROM LIST.]**

1. By starting my own business

2. Contacted employer directly 3. Through a public employment agency 4. Through a private employment agency 5. Through friends or relatives 6. Through school/university employment center 7. By responding to a vacancy 8. Through a union/professional register 9. Through job training programs/courses 10. Other (specify) 888. Don’t know 999. Refused C9_numemp1 About how many persons (including yourself) work in the enterprise or household business where you are employed in this MAIN job?

1. 1 2. 2-5 3. 6-9 4. 10-25 5. 26-50 6. 51-100 7. 101-249 8. More than 250 888. Don’t know 999. Refused

3
or investors which in turn started to compete with the incumbents and other entrants. Because there is no reliable measure of the degree of competition for a large sample of countries, the existence of private capital investment in telecoms may be the best proxy to document the commitment of the government to increase competition in the sector. It relies on the minimum volume of information and yet it gives a reasonable sense of the commitment to competition for the largest possible sample of countries. It is clearly not perfect since the opening to the private sector is necessary but not sufficient to increase competition. However, the experience suggests that the existence of private capital and the existence of competition are highly correlated when both are measured correctly.<sup>6</sup> According to the data from the International Telecommunications Union (ITU) on whether a country has at least some private ownership in their telephone companies, the

> 4 As shown later in a summary table, on average, developing countries introduced their major reforms around 1998 while developed countries did it around 1997. 5 See Wallsten (1999).

> 6 Note that the data on competition actually raises more issues. Besides the coverage problem; international databases on the existence of competition often refer to the “legal” status but not to the “de facto” situation.
South Asia (SA), with GEGIs of around 40%-50%. The lowest GEGIs are in Europe and Central Asia (ECA) with GEGIs of around 10%.

A key motivation for this paper is to calculate the economic effects of closing gender employment gaps in Pacific Island nations, as almost all of these countries are excluded from other studies. As documented in Section 7, the GEGIs average 22% across 11 Pacific Islands indicating that GDP per capita would be 22% higher in the long run if gender employment gaps were to be closed. However, this simple average masks substantial heterogeneity across islands, with GEGIs ranging from less than 10% to more than 35%, depending on the country considered.

The GEGIs use simple neoclassical growth theory to show that a specific measure of gender employment gaps in fact equals the GDP per capita gains from exogenously closing those same gender employment gaps. There are two mechanisms behind the relationship, relating to labor (direct) and capital (indirect). First, when more women work, they will directly produce more output. The basic GEGI is the percentage increase in employment from closing gender gaps. This channel involves an exogenous change in female employment, keeping other factors constant (like total factor productivity, human capital, etc.). Note that in a cross-section of countries when female labor changes _endogenously_ , those other things will not be equal, which obscures causal relationship. Second, an increase in employment will induce an increase in physical capital accumulation (again, other things equal). This is because more female labor creates a relative shortage of capital equipment and structures which raises their marginal productivity and encourages the accumulation of more capital – though this only happens in the long term. Combined, these two mechanisms mean that an exogenous X% increase in total employment also increases long run GDP per capita by X%. A similar capital-adjustment mechanism is at the heart of economic interpretation of the World Bank Human Capital Index (Kraay 2018) and the Utilization-adjusted Human
the empirical results using a Dynamic stochastic general equilibrium (DSGE) model. The model explains the propagation mechanism of a natural disaster shock that affects agricultural productivity. Based on Gallic and Vermandel (2020), it features farmers endowed with land with timevarying productivity subject to economic and weather conditions. The model is extended with a more detailed government sector, which allows considering the fiscal implications. Last but not least, counterfactual simulations highlight the role of structural resilience in limiting the impact of disasters.

# **2 Related Literature**

Empirical literature remains inconclusive on the macro-fiscal effects of natural disasters. When it comes to the impact on economic activity, Rasmussen (2004) suggests that natural disasters cause a 2.2 percent median reduction of the same-year real GDP growth rate. In a similar vein, Loayza et al. (2012) and Fomby et al. (2013) use a large panel of countries to conclude that severe disasters carry much worse effects than moderate ones, especially in developing economies. Klomp and Valckx (2014) conduct a meta-analysis based on 25 2
One of the driving incentives for IE producers—whether in academia or (to a lesser degree) in international organizations—is the opportunity to publish in professional journals. From a researcher’s perspective, there is little incentive to take time out of the preferred activity— measuring benefits—to collect high quality cost data and work through tricky cost estimates and assumptions to achieve a VFM analysis with the same level of rigor as the IE if it is not rewarded.

Evidence from our journal editor survey indicates that demand for VFM from top journals is mixed at best. Perhaps most illustrative of the low esteem held for VFM by journals is the fact that while nearly all of the top economics and economic development journals that we investigated had statements on the standards of estimating benefits (often termed effects, outcomes, results, and so on), none addressed quality control criteria for Cost estimates, much less efficiency. Additionally, 13 of 14 journal editors surveyed reported neither formal nor informal editorial practices governing inclusion of (CEA, CBA, or other VFM analysis) in published impact evaluations. Twelve of the journal editors did not give special consideration to VFM analysis—an IE with VFM is generally no more likely to be published in the top academic journals than is an IE without VFM analysis.

Journal editors mainly thought that Value for Money is so infrequently incorporated into impact evaluations because so few studies can obtain high quality cost data. “While such calculations are helpful in ‘ball parking’ the magnitude of an intervention, and whether it is ‘worth it,’ I suspect most researchers don't believe that the estimated numbers are of sufficient precision or reliability to require people to put dollar figures on interventions.” Several journal editors noted methodological issues and a lack of standardization could cause the analysis to be rejected: “Because careful valuation of Costs and benefits involves difficult decisions on certain parameters, such as shadow prices, discount rates and welfare weights, which may be arguable, and thus expose authors to additional rejection risks
as the share of total expenditures dedicated to health above a certain threshold level (Xu et al.

> 7  We apply the following calculation to assess the impoverishing impact of health spending: Given published poverty rates by TURKSTAT at the national level, we derive poverty lines endogenously for each HBS year, using nominal expenditures (as the publically available household surveys do not include geographic identifiers which would have allowed for a computation of regional poverty lines and the derivation of real consumption expenditures). Those households above the poverty line are marked as non-poor (including health expenditures). We then create a new poverty line that does not include health expenditures and assess the poverty status of each household not including health expenditures.  The impoverishing impact of health spending is the percentage of individuals who were poor without health expenditures (when compared to the poverty line net of health expenditures) but were non-poor when full expenditures are compared to the full poverty line (including health expenditures).

> 8 The poverty line is adjusted by subtracting the average health expenditures of the poorest three deciles from the poverty line. The poverty line shifts from the 27<sup>th</sup> percentile in 2003 to the 17<sup>th</sup> percentile in 2008, hence we pick the bottom 3 deciles as an approximation for the deciles around the poverty line across this time period.

9
Talvi Robledo, and Dominik J. Wielath** , “Leveraging Satellite Imagery to Assess Road Quality in the Democratic Republic of the Congo,” Master’s thesis, Barcelona School of Economics, Barcelona 2023.

- **Couttenier, Mathieu, Julian Marcoux, Thierry Mayer, and Mathias Thoenig** , “The Gravity of Violence,” _Unpublished Manuscript_ , September 25 2023.

- **CRG** , “Congo, Forgotten: The Numbers Behind Africa’s Longest Humanitarian Crisis,” Technical Paper, Congo Research Group, New York 2019.

- **Damania, Richard, Jason Russ, David Wheeler, and Alvaro Federico Barra** , “The Road to Growth: Measuring the Tradeoffs between Economic Growth and Ecological Destruction,” _World Development_ , 2018, _101_ , 351–376.

- **DFID** , “Memorandum of Understanding Between The Department for International Development (DFID), United Kingdom of Great Britain and Northern Ireland and UNOPS - Roads in the East of the Democratic Republic of Congo(DRC) Phase 2, ARIES number 203164,” Official Document, DFID, London 2011.

- **Eck, Kristine** , “In Data We Trust? A Comparison of UCDP GED and ACLED Conflict Events Datasets,” _Cooperation and Conflict_ , 2012, _47_ (1), 124–141.

30
Books countering those tendencies (e.g, by Bhagwati 2004, Wolf 2004) have since helped. So too has the recognition that loan conditionality failed to ensure developing country governments stuck with agreed trade and other reform plans (World Bank 1998) such that aid and lending institutions are now targeting their funds selectively, rewarding more those countries with good economic governance (Dollar and Levin 2004). Nonetheless, continual vigilance by economists is required to scrutinize and respond to new calls for intervention. More than that, economists need to continue to press for unilateral liberalization and for the phasing out of important exceptions to the WTO rules aimed at encouraging liberalization (e.g., a ban on export subsidies, enforcing the requirement that reciprocal preferential trade agreements involve substantially all trade, the replacement of special and differential treatment for developing countries and non-reciprocal preferences for the least-developed with first-best ways of promoting their sustainable development).

# **_Better quantitative analysis_**

Notwithstanding the enormous progress that has been made in quantitative modelling in the past two decades, much scope for improvement remains. Theoretical developments have been running well ahead of empirical modelling, as the Feenstra (1995) survey and the final six chapters of Francois and Reinert (1997) make clear. Data developments and parameter estimation have been relatively slow (an international public good problem), as have efforts to specify well the policy instruments being modelled in both the base and reform scenarios. We begin with the latter, since it has been a major source of criticism of Uruguay Round modelling.<sup>25</sup>

# _a. Improving the specification of existing and alternative policy measures_

Several mis-specifications of policy measures are clear from the ex post and especially the ex ante modelling of Uruguay Round (UR) reforms. First, UR (as with past GATT and future WTO) commitments relate to reductions in bound tariff rates, not applied rates, and bound rates can be higher (in agriculture’s case often several times higher) than applied rates.
index are: a population-adjusted trade ratio, country credit rating by Institutional Investor, Foreign Direct Investment as a share of GDP (in PPP terms), and the share of manufacturing that is exported.

_Central Bank Independence:_ Cukierman, Webb and Neyapti (1992). This index is compiled from examination of 16 statutory aspects of central bank operations including thce terms of office for the chief executive officier, the formal structure of policy foimulation, the bank's objectives (as stated in its charter), and limitations on lending to the government.

_Debt to GDP Index:_ Compiled from IMF (1995) and IFS (1995) using data from most recent year available.

21