rafmacalaba/data-use-mentions-extended huggingface dataset:
Index|insign||insign||
|KR rating|Korean country rating index|+|+|insign.|+|
|ASIA|Emerging debt returns Asia|||||
|EUROPE|Emerging debt returns Europe|||||
|LAT|Emerging debt returns Latin Am.|||||
|MEA|Emerging debt returns Middle East|||||

Exhibit 4. Explanatory Variables

# **_S._** _Empirical Analysis of Interacting Risks: Breach-of-Contract Factors_

In the sequel, we investigate how risk factors are transmitted through the nexus of contracts represented by the take-off agreement and bond covenant to bondholders. Our analysis relies on daily data covering the period from January 1997 to March 2000 that is drawn from various sources, mainly Bloomberg, IDC and Baseline. Whenever we found missing observations we cross checked the time series with other news sources and filled in the missing data or, if this was not possible, deleted the observation. Depending on the included variables we have 725 to 750 observations before taking lags. Our dependent variable is the spread of the 17 year Ras Gas bond'<sup>3</sup> yield over the 10 year US Treasury yield. The explanatory variables are the above mentioned risk factors affecting the contractual relationships at the heart of the Ras Gas project.

> '3 The bond is highly liquid and widely traded contrary to the 10 year one and, therefore, constitutes a much better measure of investor and market assessment of the project's prospects.
financial constraints remains large, negative and statistically significant at the 5 percent level in one specification and 1 percent level in the remaining specifications.

# 4.4 _Corruption-finance nexus and institutional quality_

The premise for the results above is that having better access to finance can relieve some of the problems due to corruption. That is, firms can pay their way through corruption provided they have the money to do so. As discussed above, this is more likely when there is weak implementation of the laws, allowing public officials to favor those who can pay the bribe. Thus, if our results are indeed picking up the effect of corruption and finance nexus on employment growth then we can expect them to be stronger when law enforcement is weaker. However, if our results are spuriously driven, there is no reason to expect the corruption-finance relationship to vary with the quality of law enforcement.

For the quality of law enforcement, we use the question in the ES on the severity of the lack of proper functioning courts as an obstacle to firms’ operations. As with corruption and financial constraint variables, we use CI cell averages of the responses of firms to the question on courts functioning, where the average is taken over all firms in the cell excluding the firm in question ( _Courts: Obstacle Severity_ ). The null hypothesis is that the triple interaction term between corruption, financial constraint and the severity level of courts as an obstacle is negative, implying that the corruption-finance interaction term which we found to be negative above becomes even more negative when the lack of proper functioning of the courts is a more serious obstacle to the firms.

Regression results are provided in Table 4. For brevity, only some of the specifications are shown. Results for the full set of specifications are provided in Tables A14 and A15 in the online 24
urban or rural areas, households with low-educated heads are by far the poorest. These four household categories (low-educated salaried and self-employed households in rural and urban areas) combine to encompass more than 60 percent of the total population of the Philippines and the bulk of the poor.<sup>2</sup>

# **2. Model Specification**

## **2.1  Basic Structure**

The model of the Philippine economy used in this study has 35 production sectors, with 13 sectors for agriculture, fishing and forestry, 19 for industry, and three for service sectors, including government service. In the agricultural sector, the model distinguishes capital, land and four types of labor inputs: skilled (high education = high school diploma) and unskilled (low education) agricultural labor, and skilled and unskilled production workers.<sup>3</sup> Agricultural workers are employed only in agriculture, while the small number of production workers employed in agriculture are mobile between the farm and non-farm sectors. Non-agricultural sectors, except government service, use capital as well as skilled and unskilled production worker inputs. Capital is sector-specific in this short-run closure. The demand for intermediate inputs and value-added represents fixed proportions of total output, whereas the components of value added are aggregated using a Cobb-Douglas function.

A CET function transforms local products into exports. Indirect taxes are added to the local price to determine domestic prices, which, together with the import price, will determine the composite price of domestically consumed goods via a CES price aggregator. All prices

> 2 Additional background information and data on trade and poverty in the Philippines is available in this paper. 3 In this work, we have retained the sectoral labor disaggregation in the Labor Force Survey where some production workers are also employed in agriculture. One restrictive assumption we imposed was that only those production workers employed in agriculture can move to the other sectors given a change in the relative factor price.

11
across countries and over time. However, occupations relatively more intensive in routine cognitive and non-routine manual tasks are not necessarily the same according to O*NET and STEP. This implies that the estimated trends in the task content of jobs will depend on whether the O*NET or STEP measures are used. In fact, while according to the O*NET indicators only one developing country in our sample shows evidence of job de-routinization, all the countries with sufficiently long-time coverage experience such phenomenon according to the STEP indicators.

> 1 We also construct indexes comparable to those that Autor and Handel (2013) created for the US using the PDII survey. Since the results are very similar to those that mimic the O*NET, we do not report them, but they are available from the authors upon request.

2
price change is rather permanent and/or if the income gain creates sufficient liquidity, it might give farmers an incentive to invest in production assets. On the other hand, if they perceive that the price shock is transitory, they will increase their savings for the future price fall, leaving their investment unaffected (Paxson, 1992; Rosenzweig and Wolpin, 1993; Kazianga and Udry, 2006). Which of the two effects dominates is ambiguous.

Did the positive food price shock increase investments in production capitals, creating a dynamic positive impact on agricultural productivity? Or, did farmers increase their savings (e.g., to cope with future negative income shocks) or production inputs (e.g., fertilizer and labor) to temporarily increase their outputs?

This question is related to “induced innovation” hypothesis which suggests that agricultural development is directed by the change in the conditions of factor and product markets (Hayami and Ruttan, 1985). In our context, if the food price spike increases real wage of hired labors, it might induce farmers to invest in productive assets to save the labor cost. Farmers may also substitute labors with fertilizer as the fertilizer price fell relative to agricultural real wages. As the output price of food items increases, farmers may also have stronger incentive to reallocate their resources from non-farming activities to farm investments in response to the price spike.<sup>6</sup> In this paper, we aim to answer the questions of (a) whether farmers increased farm investments to expand the production frontier and (b) whether any constraints bind farmers’ optimal strategies.

> 6 Using household panel data from Indonesia, Yamauchi (2012b) showed that (i) large farmers tend to acquire more lands by renting in land when non-agricultural real wages increase, and (ii) they install machines if agricultural real wages increase.  In contrast, small farmers seemed not to change their behavior. Since land size is significantly larger in non-Java islands than Java, the above observations imply
</sup>|_0.096_|-0.050<sup>*</sup>|_0.026_-0.055<sup>*</sup>|_0.028_|0.036<sup>*</sup> _0.020_|0.040<sup>*</sup>|_0.022_|_0.029_|_0.026_|0.029|_0.026_|
|Share of informal output, % of GDP<br>(DGE)|-0.007<sup>***</sup>|0.002|-0.011|_0.061_|0.045|_0.10_|-0.039|_0.033_-0.016|<br>_0.037_|0.030<br>_0.028_|0.012|_0.029_|0.025|_0.022_|0.025|_0.033_|
|_Instrumental variable_|||||||||||||||||
|Months elapsed since last general<br>election (March 2020)|-0.003<sup>***</sup>|0.001|||||||||||||||
|Cragg-Donald Wald F statistic|6.0|28|||6.0|28||6|.028||6.0|28|||6.02|8|
|Number of countries<br>|1|26|12|6|12|6|12|6<br>|126|126|12|6|126||12|6|
|R<sup>2</sup>|0.|47|0.1|4|0.|04|0.1|4<br>0|.03|0.
match on street name and number, with a very similar activity (e.g. “bar” and “comercio varejista de bebidas”); an exact match on street name and number, with no name or activity data in the listing; or a match on phone number, street name, and activity, with a different street number.

# _Matching ALF registrations_

The ALF administrative data also do not contain a fantasy name, so the same criteria were used as for matching MEIs.

32
household size reflects problems with the 1995 sampling design or administration of the questions regarding household membership.

**Table 19 Mean Household Size by Region**

|||_Hous_|_ehold size_||
|---|---|---|---|---|
|_Region_|_1985_|_1988_|_1993_|_1995_|
|Abidjan|6.18|5.88|5.98|5.82|
|Other Cities|6.41|6.33|6.19|5.27|
|East Forest|6.60|6.46|6.14|5.86|
|West Forest|6.03|5.60|5.68|4.63|
|Savannah|7.24|6.35|5.47|5.57|
|C6te d'Ivoire|6.51|6.16|5.91|5.46|

Source: 1985 and 1988: Demery and Grootaert (1993); 1993 and 1995: Priority Surveys.

**Table 20 Mean Size of Farm Households**

|||_House_<br>|_hold size_<br>||
|---|---|---|---|---|
|_Region_|_1985_|_1988_|_1993_|_1995_|
|East Forest|||||
|Export crop farmer|||7.38|6.23|
|Food crop farmer|||5.51|6.09|
|West Forest|||||
|Export crop farmer|||6.34|5.30|
|Food crop farmer|||4.77|4.15|
|Savannah|||||
|Export crop farm|||6.06|5.75|
|Food crop farmer|||5.50|6.10|
|C6te d
# MTEFs and Fiscal Performance:

# Panel Data Evidence<sup>*</sup>

Razvan Vlaicu<sup>¶</sup> Francesco Grigoli<sup>†</sup> Zachary Mills<sup>‡</sup> Marijn Verhoeven<sup>§</sup>

**Abstract**

In the last two decades more than 120 countries have adopted a version of a MediumTerm Expenditure Framework (MTEF). These are budget institutions whose rationale it is to enable the central government to make credible multi-year fiscal commitments. This paper analyzes a newly-collected dataset of worldwide MTEF adoptions during 19902008. It exploits within-country variation in MTEF adoption in a dynamic panel framework to estimate their impacts. The analysis finds that MTEFs strongly improve fiscal discipline, with more advanced MTEF phases having a larger impact. Higher-phase MTEFs also improve allocative efficiency. Only top-phase MTEFs have a significantly positive effect on technical efficiency.

## **JEL Classification:** E62, H51, H62.

**Keywords:** budget institutions, MTEF, fiscal discipline, allocative efficiency, technical efficiency, dynamic panel data analysis.

* We would like to thank Abdul Abiad, Nataliya Biletska, Jim Brumby, Stefania Fabrizio, Richard Hemming, Malcolm Holmes, Sam Ouliaris, Philip Keefer, Steve Knack, Ajay Tandon, and participants at the PREM Knowledge & Learning Forum and the World Bank MTEF Workshop. Haishan Yuan provided excellent research assistance. The views expressed in this paper are those of the authors and should not be attributed to the World Bank or its affiliated institutions, the IMF, its Executive Board, or its management.

† IMF, Fiscal Affairs Department, 1900 Pennsylvania Ave. NW, Washington, DC 20431, USA. E-mail: fgrigoli@imf.org.

‡ The World Bank, Europe and Central Asia Department, 1818 H Street NW, Washington, DC 20043, USA. E-mail: zmills@worldbank.org.

§ The World Bank, Poverty Reduction and Economic Management Vice Presidency, 1818 H Street
22.<br>countries)<br>.|22<br>5|22<br>17|5<br>.<br>0|

Note: This replicates Table 3, Stepan and Skach (1993), using variables from the Database on Political Institutions (DPI). Countries are “democratic” if they score more than 5 on the Legislative Index of Electoral Competitiveness. Regime type scores are from the SYSTEM variable, where 0 = Presidential, 1 ~indirectly elected and 2 = Parliamentary.
# Concentration of Wealth

Wealth is even more unequally distributed than income. Saez and Zucman (2016) measure wealth inequality in the United States and find that in 2012 the top 10% of households owned a staggering 87.7% of the total wealth. In LMICs, wealth is also highly concentrated. In South Africa the top 10% owned 85% of total wealth (Chatterjee et al, 2022), while in Latin America, the World Inequality report estimated that the top 10% owned 77% of total household wealth (Chancel et al, 2022). The top 10% owned 67% of wealth in China (Piketty et al., 2019) and 63% in India (Anand and Thampi, 2016). The extreme inequality of wealth in many parts of the world means that the same challenges researchers face when measuring the top of the wealth distribution using household surveys are even more of a concern. For example, unit and item non-response and sparseness are much more severe problems when wealth is concentrated in the hands of fewer people than incomes.

# Solutions to Challenges of Measuring the Top of the Wealth Distribution

The solutions to challenges to measuring the top of the wealth distribution are similar to those described above for income, so we do not discuss the methods themselves again. Instead, we discuss research that has used these solutions to improve estimates of the wealth distribution in LMICs. We noted above that wealth data is much less common than income data. This means that there is much less research on wealth that attempts to improve measurement of the top of the wealth distribution in LMICs.

Alstadsæter et al. (2018) is an example of using non-traditional data on wealth to supplement administrative data estimates of the share held by the top of the wealth distribution. The authors used the data described in Alstadsæter et al. (2019), discussed above, to improve the estimates of the shares of wealth held by the top 0.01% for 10 countries, although the only LMIC included was
with over-dispersion led us to use the zero-in‡ated negative binomial (ZINB) model for the main estimation speci...cation. Nevertheless, for each HS 6-digit product, in addition to the ZINB, we also run the negative binomial (NB), the zero-in‡ated Poisson (ZIP), the ordinary least squares (OLS) in log and the Poisson models, and use the Vuong test, which is a model speci...cation test, to pick the best-...tting regressions for the AVE estimates.

For ZINB, we assume that the observed trade data is the realization of the mixture of two distinct distributions: one distribution governs the probability of zero trade (participation or extensive margin), and the second distribution governs the realization of positive trade (volume or intensive margin). Let Qnij be the quantity of product n imported by country i from country j; and let h (Qnij; �jX) denote the negative binomial density with mean e (X�), dispersion parameter �, while � includes � and �: Here, X is a vector of variables to explain the positive import quantity, which are standard gravity variables, Zij; as well as tari¤s and NTMs: Therefore, the import quantity density distribution can be modeled as The parameter, pnij;which is used to increase the presence of zeros in the data set, could depend on covariate W and is commonly modeled to follow a logit function to ensure its 15
**Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS2365 Leading Indicator Project: Lithuania|Stephen S. Everhart<br>Robert Duval-Hernandez|June 2000<br>|M. Geller<br>85155|
|WPS2366 Fiscal Constraints, Collection Costs,<br>and Trade Policies|Keiko Kubota|June 2000|L.Tabada<br>36896|
|WPS2367 Gender, Poverty, and Nonfarm<br>Employment in Ghana and Uganda|Constance Newman<br>Sudharshan Canagarajan|June 2000<br>|M. Clarke<br>31752|
|WPS2368 Seeds of Corruption: Do Market<br>Institutions Matter?|Harry G. Broadman<br>Francesca Racanatini|June 2000|S. Craig<br>33160|
|WPS2369 How the Proposed Basel Guidelines<br>On Rating-Agency Assessments<br>Would Affect Developing Countries|Giovanni Ferri<br>Li-Gang Liu<br>Giovanni Majnoni|June 2000|E. Mekhova<br>85984|
|WPS2370 A New Model for Market-Based<br>Regulation of Subnational Borrowing<br>The Mexican Approach|Marcelo Giugale<br>: Adam Korobow<br>Steven Webb|June 2000|M. Geller<br>85155|
|WPS2371 Shock Persistence and the Choice<br>of Foreign Exchange Regime: An<br>Empirical Note from Mexico|Marcelo Giugale<br>Adam Korobow|June 2000|M. Geller<br>85155|
|WPS2372 Financial Openness, Democracy,<br>and Redistributive Policy|Mansoor Dailami|June 2000|W. Nedrow<br>31585|
|WPS2373 Reciprocity across Modes of Supply<br>in the World Trade Organization:<br>ANegotiating Formula|Aaditya Mattoo<br
of the instruments, panel-based Lambda-Pearson statistics (or Fischer statistics) are used.<sup>12</sup> Specifically, the Lambda-Pearson statistic is constructed as 2.0 × ∑lnPi i (where, lnPi is the natural log of the significance level associated with the F-test for significance of the rainfall instrumental variable for country i). Under the null hypothesis of significance of the rainfall instrumental variable for the panel, the Lambda-Pearson panel statistic will have a chi-square distribution with 2xN degrees of freedom, where N is the number of countries. The results show that the instrumental variables are significant at the 5 percent level. (In the case of LICs, they are significant at the 1 percent level.) To help explain the variation of domestic inflation, several potentially important country-specific characteristics were used that may affect a nation’s inflation rate. The characteristics considered are (i) the exchange rate regime by the classification of Shambaugh (2004) (where “1” is assigned to countries that have pegged or fixed exchange rates, and “0” is assigned to those with flexible exchange rates); (ii) an indicator of whether a country has an inflation targeting framework; (iii) Dincer and Eichengreen’s (2014) central bank transparency index (the higher the index is, the more transparent and independent the central bank is); (iv) gross public debt as a percentage of gross domestic product (GDP); (v) trade openness, defined as the sum of exports and imports of goods and services as a share of GDP from the World Bank’s World Development Indicators and the IMF’s World Economic Outlook; (vi) an indicator of the degree of global value chain (GVC) participation (where “1” is assigned to countries that are considered to be well-integrated into GVCs and “0” otherwise); (vii) the Chinn and Ito (2018) index of capital account openness; (viii) an indicator of whether a country is
multipliers—and a lower response of investment—when using the raw disbursements of EU funds instead of the recently released adjusted estimates of the timing of spending. Estimated cumulative multipliers of up to two years after the initial spending are typically insignificant, suggesting that results are not driven by a delayed response of short-term GDP.

We subjected our results to a range of robustness tests to check that they are not caused by outliers, influential individual observations, or omitted variables. First, because growth rates in smaller regions are often very volatile, we winsorize all our growth data—though we also find little robust evidence of large relative multipliers even using non-winsorized data, or when winsorizing at different levels.<sup>9</sup> Second, we carefully check for influential observations by rerunning all regressions dropping countries and years one-byone, and omitting two country-year observations that are extremely influential.<sup>10</sup> Finally, our default approach includes both time and region fixed effects, as well as the one-year lagged growth rates. We also fail to find large multipliers adding other controls in the literature (including institutional indicators).

We also consider some extensions to try to understand the heterogeneity and economic mechanisms behind our multiplier estimates. In contrast with some of the literature, we find little evidence of regional spillovers—spending in a region boosting growth in neighboring regions. Even when we disaggregate our sample beyond CEE vs non-CEE countries—into northern CEE vs southern CEE blocks, for example—we also find little robust evidence of large regional multipliers. The same is true when we split our sample in the time dimension or consider results separately for different EU funds.<sup>11</sup> We also find some suggestive evidence of some negative anticipation effects (one year ahead of EU fund spending) at the subnational level, partially driven by anticipated falls in investment (mostly in non-CEE countries). This could be because some investment is put off until it is eligible for EU funding, though more research
Financial Regulation and Government Revenue calculations highlight that such savings lie between 1.5% and 2.6% of the tax revenue and between 11% and 19% of the primary deficit. Differently from the previous macro-oriented literature, we are able to track the effect of the policy by having access to monthly bank balance sheets and by verifying the bank mechanics behind the policy change. Specifically we observe that banks comply with the policy and start purchasing these new bonds as prescribed. This leads to an increase in safe asset holding, enhancing the banking system safety, and it also slows down year-on-year profit growth from 11% to 2%, in a context where banks’ return on equity lies at 55%.

We believe our results are important and encourage additional research on the linkages between financial regulation and government revenue, especially on a variety of issues that are fundamental in several countries (i.e., optimality, welfare trade-offs, implementation, etc.). Indeed, we strongly believe there is generally insufficient empirical evidence on which policies low-income countries should pursue to boost financial systems and which trade-offs are encountered in their implementation.

# **References**

- Ag ́enor, Pierre-Richard, and Peter J. Montiel. (2008). ”Development macroeconomics”. Princeton University Press.

- Aizenman, Joshua, and Nancy Marion. (2011). ”Using inflation to erode the US public debt.”Journal of Macroeconomics 33, no. 4 : 524-541.

- Bacchetta, Philippe, and Ramon Caminal. (1992). ”Optimal seigniorage and financial liberalization.” Journal of International Money and Finance 11, no. 6: 518-538.

- Borensztein, Eduardo, and Ugo Panizza. (2009). ”The costs of sovereign default.” IMF Staff Papers: 683-741.

- Brock, Philip L. (1984).”Inflationary finance in an open economy.” Journal of Monetary Economics 14, no. 1: 37-53.

- Brock, Philip L. (1989