rafmacalaba/data-use-mentions-extended huggingface dataset:
constraint|40|46|48|56|
||Percentage of firms identifying<br>tax administration as major<br>constraint|21|24|32|17|
||Percentage of firms expected to<br>pay informal payments<br>to public officials (to get things<br>done)|17|23|38|41|
||Percentage of firms expected to<br>give gifts to get an operating<br>license|13|30|26|39|
|Corruption|Percentage of firms expected to<br>give gifts at meetings with tax<br>officials|13|25|39|33|
||Percentage of firms expected to<br>give gifts to secure a government<br>contract|26|55|56|31|
||Percentage of firms identifying<br>corruption as a major constraint|34|44|59|38|

_Source:_ BEEPS data, various years.

11
There were also at least two waves of explicit bailouts of local governments in Spain. The first wave was in 1983 when the central government assumed part of the local government debt, as documented in PedrejaChaparro et al. (2006). The second wave took place in 2012. In response to the crisis and mounting regional government debt, two regional liquidity funds were set up by the central government to provide cheaper loans to regional governments: the Fund for Financing Payments to Suppliers (“FFPP” in Spanish) that lent to local governments to pay for arrears owed to local suppliers; and the Autonomous Liquidity Fund (“FLA” in Spanish) designed to help with regional governments’ difficulties in debt refinancing. In essence, local governments that were facing surging interest rates or even losing access to the financial market can borrow cheap loans from the FFPP and FLA funded by the central government.<sup>46</sup>

# **B. Empirical evidence**

Empirical studies on the causal link between central government transfer and local governments’ debt burden mainly follow two approaches. The first approach uses econometric methods to estimate or test whether higher debt burden (or higher deficits in the past) indeed results in higher transfers. Solé-Ollé and SorribasNavarro (2012) use the municipal level panel data in Catalonia region during 1988-2006 to study how municipal governments respond to shocks to revenue, expenditure and grants. They find that a positive expenditure shock to municipal government is followed by an increase in grants from upper level governments. As stated by the authors, these grants are usually discretionary grants, and they “might have generated a moral-hazard problem, with municipalities having the incentive to undertake excessive expansion since additional capital spending will always be financed (at least in the long run) by higher tiers of government.” Sorribas-Navarro (2011) uses regional fiscal data in Spain during 1986-2006 to document that regions with larger debt burden tend to receive more federal transfers. The potential endogeneity problem is addressed by (1) adding cyclical economic
GDP|2005-2007|WDI|+|
|FDI|Foreign direct investment, net inflows (% of GDP)|2005-2007|WDI|+|
|log(MATTRI)|Log of MA-TTRI (applied tariff incl. prefs.) - All goods|2005-2007|WTI|-|
|log(TTRI)|Log of TTRI (MFN† applied tariff) - All goods|2005-2007|WTI|-|
|log(MAOTRI)|Log of MA-OTRI (applied tariff incl. prefs.+NTMs) - All<br>goods|2005-2007|WTI|-|
|log(OTRI)|Log of OTRI (MFN applied tariff+NTMs) - All goods|2005-2007|WTI|-|
|log(# of FTAs)|No. of FTAs / CUs - goods and services|2006-2007|WTI|+|
|Time to Export|Log of time to export (days) trading across borders|2005-2007|DB|-|
|Entry|Number of procedures required to start a business|2005-2007|DB|-|
|Size of Trade|Size of the trade sector relative to expected|2005-2007|EFW|+|
|Property|Index measuring protection of property rights|2005-2007|EFW|+|
|Infrastructure|Log of overall quality of infrastructure|2008|GCR|+|
|Log(Area)|Log of area in square kilometers|-|CEPII|-|
|Remoteness|Weighted measure of remoteness of the country|2008|Author|-|
|Regulatory Quality|Overallqualityof the regulatorysystem|2005-2007|WGI|+|

Note: WDI: World Bank Development Indicators, WTI: World Trade Indicators, DB: Doing Business, GCR: Global Competitiveness Report, EFW: Economic Freedom of the World, WGI: World Governance Indicators.

15
000 species. This includes 510,090 species that have not yet been evaluated for risk by the International Union for Conservation of Nature (IUCN) (Dasgupta et al. 2024b).<sup>6</sup>

# **Geographic Boundaries for Conservation Analysis**

In addition to generating species occurrence maps, we overlaid various geographic boundaries to examine the influence of governance and territorial issues on biodiversity conservation. These boundaries included areas such as non-determined legal status areas, Fragile and ConflictAffected Situations (FCSs), joint regimes of Exclusive Economic Zones (EEZs), and transboundary river basins, all of which present unique challenges for effective conservation. The boundaries for non-determined legal status areas and fragile/conflict states (FCSs) were obtained from the World Bank. The boundaries of joint EEZ regimes were sourced from the Flanders Marine Institute (2019), and the transboundary river basin boundaries were taken from McCracken and Wolf (2019), version 2022-03-07.

The inclusion of non-determined legal status territories is critical because these regions often face unclear or disputed governance, which can hinder effective conservation efforts. These

> 6 <u>Global Biodiversity Species Extncton Risks Data</u>

8
**The Dependent Variable**

There is a lively debate regarding how to measure corruption and rent extraction (see for example Tresiman, 2007, Olken 2009, and Kauffman et al 2007). However, despite continued controversies over (1) the validity of subjective measures of rent extraction (such as Transparency International’s (2011) Corruption Perceptions Index or the World Bank’s (2011) Good Governance Indicators) and (2) the external validity of more narrow objective measures of rent extraction (such as the UN’s (2011) data on bribery) there is now a growing consensus that, as long as standard errors are taken into account most of these major measures of rent extraction are valid and reliable and measuring the same underling variable (Kauffman et al 2007). Given that Transparency International’s Corruption Perception Index (CPI) is most substantively focused on politically sensitive corruption vis-à-vis other indicators (Hamilton, 2012; Persson and Tabellini, 2003) this measure of rent extraction (see Appendix A for details) is used as the dependent variable of interest (it is in fact inverted so that lower scores denote less corruption). However, substituting this depended variable for other measures of rent extraction (see next Section and Hamilton 2012) does not alter the robustness of the findings.

Because the CPI scores is constrained (cannot exceed or be less than a given value; 0-10) the CPI is not, strictly speaking, drawn from normal distributions (which must, by definition, vary from negative to positive infinity). While many researchers simply use OLS to run regressions on such variables, this is potentially problematic because the nature of these variables means that the residuals of such models may only approximate that of a normal distribution. If the residuals are not normally distributed, then the results of such analyses will be biased either over- or under-estimating the influence of the independent variable of interest on the dependent variable.

> 13 Australia, Austria, Belgium, Canada, Denmark, Finland,
industry tend to be more productive than other plants. This finding is by now very well established—e.g., Clerides, Lach and Tybout (1998), Bernard and Jensen (1999a) and Aw, Chung and Roberts (2000). One reason is that there are generally large sunk costs associated with contesting an export market (see Roberts and Tybout, 1996; Bernard and Jensen, 1999b). Hallward-Driemeier, Iarossi and Sokoloff (2002) find that in a sample of East Asian countries, both firms with foreign ownership and firms that export are significantly more productive, and the productivity gap is larger the less developed is the local market. Using a firm-level dataset to explore the sources of exporting firms’ greater productivity, they argue that 19
escalated, there was a 21 percentage point increase in the share of the population that was experiencing acute food insecurity or worse relative to 2014 (IPC 2015). Furthermore, the Emergency Food Security and Nutrition Assessment estimated that 65 percent of Yemenis had poor or borderline food consumption in 2016, a 57 percentage point increase relative to 2014 (FAO 2017; World Bank 2017). Since 2015, the severity of the food emergency has varied along with variation in the shocks to income and prices described above. And food insecurity worsened each year after 2015, teetering on the edge of famine at the end of 2018 (e.g., IPC 2017; IPC 2018; OCHA 2019a).

However, given the importance of violence to displacement, it is important to note that those who live in close proximity to violence do not have worse well-being on average. There is little correlation between the location of violence and access to food and basic services, and there is little change in well-being in response to a number of sudden increases in violence and violent offensives (e.g., Almoayad et al. 2020; Tandon and Vishwanath 2020; Favari et al. 2022; etc.). One of the reasons for the lack of a relationship is that particular instances of violence have impacts far beyond the location where they take place (e.g, Tandon and Vishwanath 20202), and many of the other shocks described above have much stronger and more robust impacts on the humanitarian situation (e.g., Favari et al. 2021; etc.). Combined, these results suggest that the violence that might be causing households to become displaced does not necessarily result in a worsening humanitarian situation experienced in those locations from which displaced households might be fleeing.

# **Section 3a. Data**

Given the tumultuous environments in which large-scale displacements occur, it is very challenging to find data that assess how well-being responds to shocks in real time. The Republic of Yemen is no exception, where the
-0.005|-0.003|
||(0.017)|(0.002)|(0.009)|(0.006)|
|Green management ECA|-0.182***|-0.035***|0.061***|0.156***|
||(0.004)|(0.001)|(0.002)|(0.003)|
|Green management MENA|-0.263***|0.027***|0.142***|0.093***|
||(0.011)|(0.003)|(0.006)|(0.004)|
|Observations||17,|902||
|Clusters||3,0|22||

Note: This table shows the average marginal effect estimates of credit constraints and green management on the number of green investments, based on ordered logistic regression with the categories based on the number of green investments as a dependent variable. Regression controls for the locality-level credit market controls (log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius); population size class; and region and sector fixed effects. In Panel B, estimated coefficients for all firm-level covariates as well as log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius are allowed to vary by region. Locality-clustered standard errors in parentheses. ***, **, * - statistically significant at 1%, 5%, and 10%, respectively.

Source: Enterprise Surveys and authors’ calculations.

In MENA, green management quality appears to be more important for the firms’ decision to make green investments (and the number of different green investments) than credit constraints; the estimates are consistent with our hypothesis
1 percentage point. Results for tax effort are robust to inclusion in the regression of the Polity democracy index, in column (v) of Table 5.

Regression (vi) includes a standard indicator of financial development, M2 (a measure of money supply) as a share of GDP, which is computed using data from the IMF’s International Financial Statistics database. Because missing data on M2/GDP reduces our sample size by more than one-third, we do not include this indicator of institutional development in our main tests in Table 3. Its coefficient is positive and significant in Table 5, however, suggesting that financial institutions are among the social and economic institutions taken into account in graduation decisions.<sup>22</sup> 20 According to the World Bank’s Articles of Agreement (Article IV, section 10): “The Bank and its officers shall not interfere in the political affairs of any member; nor shall they be influenced in their decisions by the political character of the member or members concerned. Only economic considerations shall be relevant to their decisions...” Of course, political considerations may influence a member government’s own decisions with respect to graduating from the IBRD.

21 Ravallion (2009) constructs an indicator of countries’ capacity to eliminate extreme poverty through income redistribution, measured in terms of the marginal income tax rates on the non-poor that would be required. This indicator is available for only a very few countries with incomes exceeding the IBRD threshold, so we are unable to use it in our tests.

22 A final variation on the credit rating and ICRG measures (not shown here) is to make use of lags. One commenter on this paper suggested that the credit ratings and ICRG scores could reflect reverse causality: a country’s graduation could cause raters to increase scores in the year of graduation. For countries as developed and integrated into global markets as the typical new graduate, it seems unlikely that IBRD graduation is taken as providing much new information on
8 of 21 The rules were applied to all available images during the defined temporal windows and imposed initial preconditions for class membership. The KBES inference mechanism analyzed the set of rules, resolved potential redundancies and inconsistencies, and made decisions about class membership using a majority rule criterion (Srinivasan and Richards 1990) (figure 3). This approach reduced the potential impact of individual aerosol-contaminated images in the mapping process.

An uncertainty flag was raised when the number of available observations during the temporal window was below a preestablished number due to cloud coverage (n=5). This was often the case for rain-fed agriculture based on observations during the rainy season. In these cases, we used an alternative approach and applied the rules of the knowledge base to an NDVI maximum value composite from all images available during the temporal window.

The map was validated against an independent data set of 754 ground points spread over the study region. Validation points were identified by visually interpreting images with very high resolution for the year 2015 (Google Earth) and combining these with known locations in true color Landsat 8 images. The analysis was carried out on a per-scene basis. Outputs from individual scenes were subsequently mosaicked in a final map at 30-meter resolution covering the whole study area. The implementation of the KBES and data processing was carried out in the Google Earth Engine cloud-based platform.
use of country systems by donors is sensitive to their quality, not only for all donors collectively, but also for the vast majority of individual donors. This conclusion does not apply to a few large donors, however, namely the USA, France and the UN system.

# **3. Data and Empirical Strategy**

_Measuring use of country PFM and procurement systems_ We measure use of country PFM systems with data from the OECD DAC’s Paris Declaration Monitoring Survey (PDMS). The PDMS was designed to assist in measuring progress toward the Paris Declaration’s objectives between 2005 and 2010. We measure use of country PFM systems with PD Indicator 5a. This indicator is constructed as a simple average of three sub-indicators from the survey: use of national (i) budget execution procedures, (ii) financial reporting procedures, and (iii) auditing procedures, each as a percentage of aid for the government sector.<sup>2</sup> Detailed criteria for these three dimensions of use-of-PFM-systems are provided in Appendix 1. Correlations among them average .73 (ranging from .54 to .77). Findings presented below change very little if any one of its three components is analyzed instead of Indicator 5a. Mean use of PFM systems among the 2,089 observations appearing in our baseline multivariate regression is 33.5%.<sup>3</sup> Use of country procurement systems is measured using PD Indicator 5b, defined as the share of aid to the government sector that is not subject to additional or special requirements by

> 2 Aid for the government sector accounts for about 83% of total aid disbursements reported in the PDMS.  It excludes aid disbursed to NGOs, parastatals or private companies unless it is provided “in the context of an agreement with officials authorized to act on behalf of central government” (OECD, 2008).  Empirical results reported below are unaffected if government sector aid is replaced with total aid in the denominator of the use-ofPFM-systems indicator.

> 3 Appendix
14 1995). The per-unit political cost of increasing farm incomes by raising the RRA thus decreases as the economy becomes less agrarian. In other words, even though the share of farmers in the voting population declines, less opposition to protecting farmers arises when there are fewer of them. Studies by de Gorter, Nielson, and Rausser (1992) and Swinnen (1994) show that under plausible assumptions, the second of those two effects dominates.

Ample evidence supports these theoretical predictions. Moreover, these empirical findings are consistent across (a) empirical, mostly cross-sectional, studies on agricultural protection in the 1980s and 1990s that use reduced-form econometric models; (b) studies using long-term time-series data and econometric analyses; and (c) recent empirical studies using new data sets.<sup>15</sup>

# _3.2.2. Change in Political Institutions_

Several developing countries have experienced democratization over the past three decades. Theoretical formulations have been advanced to explain how democratization will affect public policies.<sup>16</sup> Models based on the median-voter theorem predict that democracies tend to redistribute from the rich to the poor. This is expected in democracies because the distribution of political power (measured by votes) is typically more equal than the distribution of income and wealth (Alesina and Rodrik 1994; McGuire and Olson 1996; Persson and Tabellini 1994). Similarly, democratic regimes could lead to economic-policy reforms if these reforms created more winners than losers (Giavazzi and Tabellini 2005).

The implications for agricultural policies are not straightforward. The very factors that make it difficult for farmers to organize politically (such as their large geographic dispersion) render them potentially very powerful in electoral settings (Bates and Block 2010; Varshney 1995). Since greater insulation of decision-makers implies that they can follow their personal preferences to a greater extent in selecting policies, their ideologies or other types of preferences are a key variable. However, while it is intuitively obvious that when decisionmakers are more insulated from repercussions, they
(2)), “started log” of household wealth score (column (3)), and a binary measure of household wealth poverty status (column (4)). Household sample weights from ESS2 applied. All columns control for household (HH) FEs, round FEs, and 12 time-varying characteristics. The coefficient estimates for these 12 characteristics are not reported here, but can be available on request. Robust standard errors clustered at woreda level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

51
firms are particularly under-represented. Likewise, Cusolito and Winkler (2015) show that while the share of firms in many sectors covered in RUSLANA is similar to Rosstat, there are important differences. In particular, wholesale and retail trade firms are over-represented in Ruslana, while real estate, education and other services are under-represented. However, given that Ruslana is the only reliable source of firm-level information and that it does a good job at matching the evolution of employment and value added over time (Cusolito and Winkler, 2015), we use Ruslana as the reference population for our work. Moreover, given that we study the impact of managerial capabilities on firm performance in manufacturing, the lower coverage of small firms, over-representativity in certain services does not affect the usefulness of our approach in using Ruslana as the reference population.

# _2.4 Sample Stratification_

Taking the reference population from Ruslana, the sample was stratified by sector and federal districts. Within sectors, only manufacturing was considered and the following six aggregate groupings were used in stratification: Food and beverage (ISIC Revision 4 codes: 10 and 11); Textile, apparel and leather (ISIC Revision 4 codes: 13-15); Chemical, rubber and non-metallic products (ISIC Revision 4 codes: 19-23); Basic metals and metal products (ISIC Revision 4 codes: 24 and 25); Electronics and electrical equipment (ISIC Revision 4 codes: 26 and 27); and Machinery and motor vehicles, including transport (ISIC Revision 4 codes: 28-31). The following five Federal districts were used in stratification: Central (excluding Moscow oblast and Moscow city); Northwestern (excluding Leningrad oblast and Saint Petersburg city); Southern; Ural; and Volga. In all sectors and federal districts only small and medium firms (25-250 employees) were 6
0.016**|0.000***|
|one group training at CGAs|[0]|(0.019)|(0.012)|(0.01)||||||
|**PANEL B: Endline survey data**||||||||||
|_Formalization process: (only formal_|_businesse_|_s)_||||||||
|Number of days it took to||||||||||
|formalize<sup>η</sup>|22.9|‐14.6|‐21.6**|‐15.6**|329|0.557|0.929|0.322|0.11|
||[27.3]|(11.4)|(8.6)|(7.5)||||||
|Amount paid for formalization|66,931 <br>[57,036]|‐73,289***<br> (19,449)|‐57,145***<br>(15,343)|‐56,552***<br>(13,302)|332|0.421|0.333|0.957|0.001***|
|Share of business who paid<br>something to formalize|1|‐0.788***|‐0.693***|‐0.773***|332|0.706|0.945|0.563|0.001***|
||[0]|(0.246)|(0.194)|(0.168)||||||
|_Program Knowledge:_||||||||||
|Ever heard of
.

The distributed micro-data analysis was conducted for two separate analytical themes. The first set of analyses gathered data relating to firm demographics, such as entry and exit, jobs flows, size distribution and firm survival. The second theme gathered indicators of movements of firms and resources related to productivity, such as productivity contributions of entry/exit and other measures of resource reallocation. The synthetic indicators used in the analysis for these two themes are discussed in details in Box 1.

The analysis of firm demographics is based on business registers, census, social security databases, or employment-based register containing information on both establishments and firms (see Table 1). Data for the analysis of productivity growth come more frequently from business surveys. Using these data, time-series indicators on firm demographics were generated for

> 5. In addition to the authors of this paper, the researchers involved in the distributed micro-data analysis network for the various projects are: John Baldwin (Canada); Tor Erickson (Denmark); Seppo Laaksonen, Mika Maliranta, and Satu Nurmi (Finland); Bruno Crépon and Richard Duhautois (France); Thorsten Schank (Germany);  Fabiano Schivardi (Italy); Karin Bouwmeester, Ellen Hoogenboom and Robert Sparrow (the Netherlands);  Pedro Portugal Dias (Portugal); Ylva Heden (Sweden); Jonathan Haskel, Matthew Barnes, and Ralf Martin (United Kingdom); Ron Jarmin and Javier Miranda (United States); Gabriel Sánchez (Argentina), Marc Muendler and Adriana Schor (Brazil), Andrea Repetto (Chile), Maurice Kugler (Colombia and Venezuela), David Kaplan (Mexico), John Earle (Hungary and Romania), Mihails Hazans (Latvia), Raul Eamets and Jaan Maaso (Estonia), Mark Roberts (Korea, Indonesia and Taiwan (China)), Milan Vodopivec (Slovenia).

7