rafmacalaba/datause-encoder-data huggingface dataset:
for present and past agreements, respectively, and i is a dummy for the existence of bilateral investment treaty; i , , and , represent respectively ii BBFFTT country-pair industry, reporter industry time and partner industry time fixed effects. kk iikkii iikkii δδ δδ δδ

# b. Baseline results

Table 1 reports the coefficients of total depth, WTO-plus and WTO-extra variables for both OLS and PPML estimators. The first three columns are estimated with OLS and show that the total depth of PTAs matters for investment, whereas a shallow measure of PTAs using only a dummy variable does not have a significant effect. The bilateral investment treaty dummy is not significant, which is also the case in the literature when the effects of BITs and PTAs are estimated concurrently. Moving to the best practices for the gravity estimation, which recommend using PPML, we find that the total depth variable is also significant and positive when using the PPML estimator. One additional discipline increases FDI announcements by 1.4 percent. Splitting the depth of the PTAs between WTO-plus and WTO-extra disciplines as done in columns (4) and (6) of table 1, we find that the disciplines that go beyond the WTO’s mandate drive the positive effects. In table 2, we test whether deep PTAs matter for FDI in extractive activities. The interaction with a dummy for extraction shows that deeper agreements do not promote FDI in extraction activities as opposed to services and manufacturing activities. This is consistent with the work of (Laget, Osnago, Rocha, & Ruta, 2018) who show that deep PTAs do not matter for value added trade in resource sectors. For the rest of our analysis we focus on manufacturing- and service-related activities only.

In the next section we investigate the effects of specific disciplines.

15
# **Table B1: Data sources and summary statistics**

|Variable|Descriptions|Country<br>N|Obs. No.|Mean|S.D.|Min|Max|
|---|---|---|---|---|---|---|---|
|**National poverty rate (1**|**979–2019) (percent)**|o.||||||
|_Source: The World Bank_ (|https://datacatalog.worldbank.org/home)|||||||
|Poverty rate $1.90|Poverty Headcount Ratio at US$ 1.90 a day|134|464|7.288|14.814|0.000|78.841|
|Poverty rate $3.20|Poverty Headcount Ratio at US$ 3.20 a day|134|464|15.399|23.975|0.000|91.518|
|Poverty rate $5.50|Poverty Headcount Ratio at US$ 5.50 a day|134|464|26.593|32.088|0.051|97.485|
|**Subnational poverty rat**|**e (Global Subnational Atlas of Poverty – GSAP)**|||||||
|**(percent)**||||||||
|_Source: The World Bank_ (|https://datacatalog.worldbank.org/home)|||||||
|Poverty rate $1.90|Poverty Headcount Ratio at US$ 1.90 a day|134|4,972|10.061|19.389|0.000|98.010|
|Poverty rate $3.20|Poverty Headcount Ratio at US$ 3.20 a day|134|4,972|20.327|28.482|0.000|99.724|
|Poverty rate $5.50|Poverty Headcount Ratio at US$ 5.50 a day|134|4,972|34.009|34.700|0.000|100.000|
|**Subnational poverty rat**|**e**_(Source: Kalkuhl and Wenz, 2020)_|||||||
|Poverty at $1.90|Poverty rate using average gross daily income being<br>below US$ 1.90 a day|77|3,394|20.443|37.990|0.000|100.000|
|Poverty at $3.20|Poverty rate using average gross daily income being<br>below US$ 3.20 a day|77|3,394|34.075|44.185|0.000|100.000|
|Poverty at $5.50|Poverty rate using if average gross daily income<br>being below US$ 5.50 a day|77|3,394|57.450|46.434|0.000|100.000|
|**Subnational poverty rat**|**e**_(Source: Kummu et al., 2018)_|||||||
||Poverty rate using average gross daily income being|||||||
|Poverty at $1.90|<br>below US$ 1.90 a day|82|1,811,394|24.245|42.857|0.000|100.000|
|Poverty at $3.20|Poverty rate using average gross daily income being<br>below US$ 3.20 a day|82|1,811,394|32.000|46.648|0.000|100.000|
|Poverty at $5.50|Poverty rate using average gross daily income being<br>below US$ 5.50 a day|82|1,811,394|55.000|49.749|0.000|100.000|
|**National inequality**||||||||
|_WDI(Source: The World_|_Bank -_https://datacatalog.worldbank.org/home_)_|||||||

70
as a proxy for the willingness of respondents to engage in policy dialogue with the World Bank. We also use the product of ( _freq_ _~~c~~ omm_ ) and ( _demand_ _~~r~~ eform_ _~~a~~ dvice_ ) as a more direct measure of the respondent’s demand for reform advice from the World Bank. Tables A6 and A7 demonstrate that our core finding related to the effect of World Bank ESW is robust to the inclusion of these additional control variables.

Third, we consider the possibility that our results are biased by a selection effect. Participants in the _2014 Reform Efforts Survey_ first identified the bilateral and multilateral development institutions with whom they directly engaged during their period of public service, and then evaluated the policy influence of only these institutions. We therefore only observe responses about the level of the World Bank’s policy influence from those survey participants who worked directly with the World Bank, which implies that our results could be driven by self-selection bias (if respondents who are more inclined to work with the World Bank are also more inclined to assess its policy influence favorably). The Heckman model accounts for self-selection bias—i.e., the presence of potential confounders that may have affected the propensity of participants to interact with the World Bank (Heckman 1979). In the first (selection) stage of the analysis, we estimate a Probit model (using the full sample) to explain the probability of respondent participation in the outcome stage. Then, in the second (outcome) stage of the analysis, we estimate a standard linear OLS model to predict the World Bank’s level of agenda-setting influence. To employ the Heckman model, a variable should be identified that only affects respondents’ propensity to interact with the World Bank (included in the selection stage) and does not affect their evaluation of the influence of the World Bank (excluded from the outcome stage) (Sartori 2003). For this purpose, we use a variable that measures the number of bilateral and multilateral development institutions with whom a participant interacted during his or her period of public service ( _number_ _~~o~~ rg_ ). This variable captures the general propensity of survey respondents to interact with aid agencies and development banks, including the World Bank, but we do not have any strong “too little reform,” or “about the right amount” within their respective areas of policy expertise and between their start dates and end dates in specific public sector positions.

25
# **Making Informed Investment Decisions in an Uncertain World: A Short Demonstration**

Laura Bonzanigo<sup>1</sup> and Nidhi Kalra<sup>2</sup>

**JEL** : C44, D81, H54, O21, O22

**Keywords** : decision making under uncertainty, long-lived infrastructure investments, economic analysis, robust decision making

> 1 Sustainable Development Network, Office of the Chief Economist, The World Bank; Department of Economics, Ca’ Foscari University, Venice

> 2 Sustainable Development Network, Office of the Chief Economist, The World Bank
**Table 7: Treatment in the Framework of EC Regulation 1408/71 (Medically Necessary Treatment Abroad and Regular Cross-Border Commuters), 2005**

|**Case**|**Number of cases**|**Euro**|**Euro/case**|
|---|---|---|---|
|CT (computer tomography)/ MRI (magnetic<br>resonanceimaging)|197|16,051.2|81.47|
|Disposableinstruments|3|1,626.3|542.10|
|Expensive benefitsin kind|489|366,275|749.02|
|In-patient treatment|2,237|14,979.8|6.69|
|Dentaltreatment|156|6,183.01|39.63|
|Spa treatment|606|219,887|362.84|
|Medical Aid|23,453|17,052.7|0.72|
|Pharmaceuticals|196|385.378|1.96|
|Treatment at generalpractitioner|15,170|89,888.3|5.92|
|Care athome|3|229.791|76597|
|Total|42,510|732,558|17.23|

Source: Data provided by courtesy of the Hungarian NHIF, assembled by the author.

Outside the structures provided by the EC Regulations 1408/71 and 574/72, 3,510 Austrians purchased health care in Hungary for the total amount of 262,370 Euro in 2005 (see Table 8). This number provides only an account of the reported cases from the innetwork physicians and hospitals to the NHIF. Out-of-network actors do not have to report their cases, the overall number therefore being unknown.

**Table 8: Treatment Outside the Framework of EC Regulation 1408/71, 2005**

|**Treatment**|**Number of**<br>**cases**|**Value in Euro (not actual price,**<br>**according to NHIF provisions)**|**Euro/case**|
|---|---|---|---|
|In-patient|705|249,240|353.53|
|Out-patient|2,805|13,130.9|4.68|
|Total|3,510|262,370|74.74|

Source: Data provided by courtesy of the Hungarian NHIF, assembled by the author. The hospital/physician determines the price in advance. Only in-network hospitals/physicians have the duty to report to the NHIF.

# **5 POTENTIAL FINANCIAL BENEFITS FROM CROSS-BORDER PURCHASING FOR SELECTED HEALTH CARE SERVICES**

The above presented data show that cross-border purchasing of health care is at the moment a minor phenomenon regarding health services covered by the statutory Austrian health insurance system. This section will look at the potential benefits for Austrian insurance institutions and individual patients regarding health services which are not always fully covered: cataract surgeries, dental treatment, and cosmetic surgeries.

In principle, the statutory health insurance in Austria covers all treatments that are medically indicated; only few areas are not covered such as specific cosmetic surgeries or fixed dentures. Each regional or occupational HIF has its own catalogue of benefits that are covered by it. The HIFs fund the public hospitals with lump sums without giving a limit regarding the treatments covered. Each hospital then decides independently whether 18
compared to past measures of subjective poverty. Section 4 turns to our tests of the robustness of regressions for subjective economic welfare. Section 5 concludes, also noting some caveats.

# **2. Survey data on subjective economic welfare**

We study subjective economic welfare, as measured by survey responses to the following question: _“Imagine a 6-step ladder where on the bottom, the first step, stand the poorest people, and the highest step, the sixth, stand the rich. On which step are you today?”_ Concerns about scale heterogeneity also arise with questions related to broader welfare concepts such as “happiness” or “satisfaction with life.” Our methods may well be adapted to these broader concepts, although it is likely to be harder to devise credible and practical vignettes for such questions, given that so many variables could be deemed relevant.

Respondents were asked to place themselves on the subjective welfare ladder described above. Later in the questionnaire they were asked to place four vignettes, each describing a hypothetical household, on the same ladder, and finally to (again) place their own household on the ladder, after scoring the vignettes. In asking the own-welfare questions both before and after the questions about the vignettes, we are able to test whether the vignettes alter the respondents’ perceptions of their welfare. The vignette questions may focus the respondent to think about, and possibly revise, the scale they have in mind in reporting their subjective welfare (similarly, see Hopkins and King, 2010).

For all three countries, we developed the vignettes in consultation with local counterparts. The actual vignettes from the questionnaires (translated into English) are given in the Appendix. The vignettes were designed to capture representative snapshots of various levels of welfare in each country. The first vignette was designed to present a scene that almost anyone in the country concerned would deem to be one of poverty. This was not intended to be the poorest imaginable destitute household, but rather an undeniably poor family with a sustainable livelihood. The second vignette indicated a family with improved conditions, though a family that some would still consider poor in that setting. The third was intended to represent a family from the middle class and the fourth an affluent family. The characteristics incorporated in the vignettes varied across countries and included land holdings, education, diet, clothing, and the ability to heat the home during the winter. The vignettes were developed in a clear expected hierarchy of dominance with respect to economic welfare, with all aspects of socio-economic 6
Policy Research Working Paper 5485

# **Abstract**

Firms in developing countries face numerous and serious constraints on their growth, ranging from corruption to lack of infrastructure to inability to access finance. Countries lack the resources to remove all the constraints at once and so would be better off removing the most binding one first.

This paper uses data from World Bank Enterprise Surveys in 2006 –10 to identify the most binding constraints on firm operations in developing countries. While each country faces a different set of constraints, these constraints also vary by firm characteristics, especially firm size. Across all countries, access to finance is among the most binding constraints; other obstacles appear to matter much less. This result is robust for all regions.

Smaller firms must rely more on their own funds to invest and would grow significantly faster if they had greater access to external funds. As a result, a low level of financial development skews the firm size distribution by increasing the relative share of small firms. The results suggest that financing constraints play a significant part in explaining the “missing middle”—the failure of small firms in developing countries to grow into medium-size or large firms.

This paper—a product of the Operations and Strategy Unit, Development Economics—is part of a larger effort in the department to develop new practical insights as to how African countries can significantly improve their production of light manufacturing products. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at HDinh@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Environmental Performance Index (Wolf _et al._ , 2022), the OECD Better Life Index (Durand, 2015), the United Nations Human Development Index (UN, 2022), the World Bank Human Capital Index (Kraay, 2019), and the World Press Freedom Index (Reporters without Borders, 2023). The SPI has strongest correlation with three out of the six indexes, followed by the ODB (two indexes), and the ODB (one index). However, most of these correlation coefficients are not statistically significantly different from each other as shown by statistical tests and further multivariate regression results (Appendix A, Table S7 and Tables S10 to S21), suggesting the tools having rather similar relationships with these six development indexes.

# **Discussion**

We offer the first rigorous assessment of the various tools that are commonly used to assess country statistical capacity. These include the Global Data Barometer index (GDB), the Open Data Inventory index (ODIN), the Statistical Performance Indicators and Index (SPI), their predecessors (the ODB and the SCI), and some regional and self-assessment tools (including the IIAG). Each tool follows its own guiding principles in collecting data and generating its metrics. Compared to the SPI framework, the ODIN and the ODB focus more on data services and products, the IIAG and the SCI focus more on data products and infrastructure, and the GDB and all the three selfassessment tools focus more on data infrastructure. While the SPI (and SCI) obtain most of the data from curated international databases, the ODIN exclusively from NSO websites, the GDB (and ODB) mostly from expert surveys, the IIAG from a mix of different sources, and the three self-assessment tools from expert self-assessment tools. These different methods could offer their own strengths and weaknesses.

Except for the IIAG that focuses on Africa, all the remaining tools are global. The ODIN covers the most countries, to be followed by the SPI (and the SCI), the GDB (and the ODB), and the IIAG (54 countries during 2010-2019). Regarding measuring recent country progress over time, the ODIN and SPI are the two available choices, with the SPI showing somewhat less volatility.

Since strong statistical capacity is crucial for monitoring SDG progress and other key development outcomes, we examine their relationships with all the tools. For the overall SDG index, the SPI shows the strongest correlation. The SPI also exhibits stronger correlations with other popular development indexes such as the Economics Complexity Index, the OECD Better 12
The focus of this paper is on restructuring and growth, however, and so we are particularly interested in how this is enabled, or not, by financial institutions. We therefore focus on the structure of financing of firms.

Table V.1 presents a snapshot of the sources of finance for fixed investment by firms in the BEEPS surveys in 2004-05. By far the largest source of financing is retained earnings, in all regions. This is a standard finding in the literature (see e.g., Mayer (1988, 1990) for an early discussion). More significant are the cross-country differences. West Germany (I) stands out from the cohesion countries (II) as country where dependence on internal financing is somewhat lower, though the differences are not huge: roughly 50% of total financing in Germany is via retained earnings, vs. 60% in the cohesion countries. The difference is due to formal capital markets: not just bank financing (23% vs. 20%) but equity offers (10% vs. 3%). The EU8 members (III) resemble the cohesion countries in both the reliance on retained earnings and on formal capital markets (though division between equity offers and bank financing puts more weight on the former). Reliance on retained earnings and on capital markets is clearly related to level of income and economic development: the former increases, and the latter decreases, as we move from the richest to the poorest country groups. Interestingly, the role of state bank financing is small and if anything, smaller in the transition economies than in the developed market economies. State banks do not appear to be a major conduit for the soft budget constraint, at least for most firms.

More surprising is the time trend in the TEs. Table V.2 shows that the transition economies have _not_ been converging to levels of reliance on retained earnings that are observed in the mature EU economies; on the country, in all groups of transition countries, firms were on average _more_ reliant on retained earnings in 2005 than in 1999. The data in the table do not suggest that formal capital markets have been shrinking in TEs; as the simple means in the table show, reliance on bank and equity financing has been fairly stable over this period. (Bank financing from state banks cannot be separately identified in 1999, but the figures for 2002 are small and slightly below those reported in Table V.1 for 2005.) The biggest change has apparently been a move away from 30
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