rafmacalaba/datause-encoder-data huggingface dataset:
**PROVING INCENTIVES FOR LONG-TERM INVESTMENT BY PENSION FUNDS –THE USE OF OUTCOME-BASED BENCHMARKS**

# **Fiona Stewart**<sup>**1**</sup>

### **<mark>JEL Classification:</mark>**

- <mark>G15: Financial Economics / General Financial Markets / International Financial Markets</mark>

- <mark>G18: Financial Economics / General Financial Markets / Government Policy and Regulation</mark>

- <mark>G23: Financial Economics / Financial Institutions and Services / Non-bank Financial Institutions; Financial Instruments; Institutional Investors</mark>

- <mark>G28: Financial Economics / Financial Institutions and Services / Government Policy and Regulation</mark>

- <mark>J26: Labor and Demographic Economics / Demand and Supply of Labor / Retirement; Retirement Policies</mark>

## **_Sector Board: Financial Sector (FSE)_**

- 1 This paper was written by Fiona Stewart, Senior Financial Sector Specialist, Global Capital Markets and NonBank Financial Institutions Group. fstewart1@wordlbank.org

- The paper draws heavily on previous work by Heinz Rudolph and benefited from input from Peter Holtzer. Comments were gratefully received from Will Price, Zoran Anusic, Csaba Feher as well as Dariusz Stanko and his IOPS colleagues.
11

**Figure 10. European Bank Equities and Profitability**

<!-- Start of picture text --> a. Stock Market Indexes b. Return on Equity<br>120 12<br>Jan 2011– May 2012 2007–10<br>10.0 9.9<br>110 10<br>Euro Stoxx 50<br>8<br>100 Euro Stoxx 600 Banks 6.6<br>6<br>90 3.9<br>4<br>1.9<br>80 2<br>1.0<br>70 0<br>-2<br>60<br>-2.8<br>-4<br>50<br>-6<br>-5.4<br>2007 2008 2009 2010<br>Index Date = All Banks Large Banks<br>Percentage<br><!-- End of picture text --> Source: Panel a, Bloomberg; Panel b, ECB Consolidated Banking Data.

# **III. THE SCOPE OF DELEVERAGING AND ASSOCIATED RISKS**

**15. Information as of early 2012 suggests that many banks have been selling or scaling down their businesses within or across borders** . In particular, banks have been adjusting their business strategies to focus on ―core‖ markets of strategic importance, and abandoning activities and locations that require higher capital or that do not offer profitable opportunities going forward: for example, because of adverse regulatory regimes. Appendix table A.3 lists recent actions undertaken by many banks.

**16. Data also point to a marked deterioration in credit conditions across the Euro Area in late 2011, with some turnaround in the first quarter of 2012** . Driven by more prudent risk management practices, European banks have been tightening lending standards since fall 2011 (figure 11). The ECB‘s lending surveys (ECB 2012a, 2012b) show that credit conditions worsened sharply across the Euro Area in the fourth quarter of 2011, but, while remaining tight, the net tightening of credit standards fell substantially in the first quarter of 2012, both for loans to nonfinancial corporations and to households. Key drivers of tightening conditions in 2011 were a deteriorating economic outlook, limited access to market financing, and tight liquidity conditions. The easing of tighter lending conditions in 2012:Q1 mainly reflects milder pressures from cost of funds and balance sheet constraints, in particular regarding banks‘ access to funding and their liquidity position. Banks report a sizeable fall in net demand for loans to corporations in 2012:Q1, but expect higher demand in 2012:Q2 and a further decline in the tightening of credit conditions.
**Table 11.F. Model comparison: Diebold and Mariano (1995) test for tin prices**

|**Approach**|**Horizon**|**Consensus**|**Futures**|**Bivariate**<br>**correlations**|**BVAR**|**OEM**|**Machine**<br>**learning**|
|---|---|---|---|---|---|---|---|
|Consensus|3|...|||†|||
|Futures|3|*|...||***|||
|Bivariate correlations|3|*||...|***|||
|BVAR<br>|3<br>||||...<br>|||
|OEM|3||||†|...||
|Machine learning|3|*|||*||...|
|Consensus|6|...||||||
|Futures|6|*|...||†|||
|Bivariate correlations|6|*||...|*|||
|BVAR<br>|6<br>||||...|||
|OEM|6|†||||...||
|Machine learning|6||||†||...|
|Consensus|9|...||||||
|Futures<br>|9<br>|†|...|†|†||†|
|Bivariate correlations|9|||...||||
|BVAR|9||||...|||
|OEM|9|†||||...|†|
|Machine learning|9||||||...|
|Consensus|12|...||||||
|Futures<br>|12<br>|†|...|†||†|*|
|Bivariate correlations|12|||...||||
|BVAR|12||||...|||
|OEM|12|†||†|†|...|*|
|Machine learning|12||||||...|
|Consensus|15|...|...|...|†|||
|Futures|15|...|...|...|...|...|...|
|Bivariate correlations|15||...|...||||
|BVAR|15||...|...|...|||
|OEM|15|*|...|...|†|...|**|
|Machine learning|15||...|...|||...|
|Consensus|18|...|...|...|†||*|
|Futures|18|...|...|...|...|...|...|
|Bivariate correlations|18||...|...||||
|BVAR|18||...|...|...|||
|OEM|18|*|...|...|*|...|***|
|Machine learning|18||...|...|||...|
|Consensus|21|...|...|...|*||***|
|Futures|21|...|...|...|...|...|...|
|Bivariate correlations|21||...|...||||
|BVAR|21||...|...|...|||
|OEM|21||...|...||...||
|Machine learning|21||...|...||***|...|
|Consensus|24|...|...|...|*||***|
|Futures|24|...|...|...|...|...|...|
|Bivariate correlations|24||...|...||||
|BVAR|24||...|...|...|||
|OEM|24|***|...|...||...||
|Machine learning|24||...|...|||...|

_Source_ : World Bank.

_Note_ : Diebold Mariano statistic tests whether the forecasts in the model indicated in the row is more accurate than the forecast of the model indicated in the column. *** indicates statistically significantly more accurate model in the row at the 0.1 percent significance level, ** at the 1 percent level, * at the 5 percent level, and † at the 10 percent level.

57
11 countries with variables that might be considered extremely high or low also does not alter the results.<sup>1 0</sup> Based on Levine and Renelt [19921, we also conduct extreme bounds analyses (EBA) of the results in Table V. The EBA involves altering the right-hand-side variables and observing whether the results on the variables of primary interest - the four financial indicators - are robust or fragile to these alterations. Using the "base" regression that always includes LYO and LSEC, we allow the EBA procedure to choose various combinations of up to three right-hand-side variables from the list of "other" variables used in Levine and Renelt (1992), and we then examine whether the coefficient and significance of the coefficient on the financial development indicators remain stable while altering the conditioning information set. (The "other" variables are the number of revolutions and coups (REVC), GOV, PI, TRD, the standard deviation of inflation (STPI), the growth rate of domestic credit (GDC), and the standard deviation of the growth rate of domestic credit (STDC).) The results in table V are robust; small alterations in the conditioning information set do not alter the inferences on the financial indicator.<sup>11</sup> These robust results on financial development indicators contrast strongly with the Levine and Renelt (1992] findings that most other economic indicators have only very fragile associations with long-run growth.

10 For example, LLY is greater than one in Japan, Malta, and Switzerland, while TRD is greater than 1.5 in Hong Kong, Luxembourg, and Malta.

I1 Table VIIe in the Appendix presents these results. Levine and Renelt [19921 run two sets of regressions for every variable of interest. When GYP is the dependent variable, the regression always includes a constant, initial income (YO), the initial secondary school enrollment rate (SEC), population growth (GPO), INV, and the variable of interest. By including INV as a regressor, this is an alternative way of defining the economic efficiency. Also, Levine and Renelt (19921 use INV as the dependent variable. In these regressions, only a constant and the variable of interest are always included. When we use this exact procedure for the four financial indicators, all four are robustly correlated with INV, but only LLY is robustly correlated with GYP. This implies that while measures of financial development are robustly linked to growth through investment, the relationship between financial development and efficiency may be sensitive to the empirical definition of efficiency.
29. Today, rules and regulations governing Romania’s product market policies are less restrictive than most other middle income countries. Even relative to its closest comparators, Bulgaria in 2006 and the OECD countries of Central and Eastern Europe in 2003, Romania appears to be among the less restrictive countries, positioning itself slightly above the OECD and EU averages (Figure 4).

**Figure 4. Product Market Regulation - Country Comparison**

<!-- Start of picture text --> 3.0 2.8<br>2.5<br>2.1<br>2.0<br>2.0 1.6 1.7 1.8<br>1.4 1.4 1.5<br>1.5<br>1.0<br>0.5<br>0.0<br>Slovak EU15 OECD Romania Czech Bulgaria Hungary Other Poland<br>Republic 2003 2003 2006 Republic 2006 2003 MICs 2003<br>2003 2003 2003<br><!-- End of picture text --> _Source:_ see Figure 3. _Note_ : Other MICs are Brazil, Mexico, and Turkey. OECD CEE include Czech Republic, Hungary, Poland, and Slovak Republic. The values refer to 2006 for Romania and Bulgaria, 2004 for Brazil, and 2003 for all other countries. Romania’s 2002 score was calculated using a different methodology so is not strictly comparable.

30. We now turn to an analysis of the various components of the PMR indicator, in order to examine in greater detail some of the drivers of this excellent performance, but also to identify remaining sources of restrictiveness of product market regulation. A useful approach in doing so is to decompose the indicator into inward and outward oriented policies. The former include policies and regulations that determine the degree of state control and barriers to entrepreneurship, while the latter reflect policies and regulations that affect barriers to trade and investment. Detailed description of the basic indicators included in the indicators of inward and outward oriented policies follows in the next sections.

31. In general, average performance seems better in all countries for outward- than for inward-oriented policies (Figure 5). This is certainly due to the requirements imposed by international agreements - such as the WTO charters, and, for EU countries, membership of the European Union - which are more binding in matters concerning trade and foreign direct investment. Greater reform challenges, as well as greater crosscountry variation, lie with the regulations that fall under the category of inward-oriented policies. Within this general framework, Romania’s product market regulations appear to be more restrictive of competition than the OECD and EU averages for outward oriented policies and around the OECD and EU averages for inward oriented policies.

11
Policy Research Working Paper 10757

# **Abstract**

This paper examines spillovers in the use of digital technologies from formal to informal businesses by exploring differences in geographic proximity. Using a unique set of geocoded data from the 2019 World Bank Enterprise Surveys in Zambia, the findings indicate that closer geographic proximity to formal firms is associated with a significantly higher likelihood of digital adoption by informal businesses. The finding holds for various types of digital technologies, ranging from computers, tablets, and cell phones to mobile money transactions, and is robust to various measures of geographic proximity and model modifications. The results vary by the owner’s level of education and business age. The results also suggest that the spillovers in information and communications technology use can be explained by competition in the local market and learning through enhanced interactions.

This paper is a product of the Equitable Growth, Finance and Institutions Practice Group and the Global Indicators Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at at fjolevski@worldbank.org, gnayyar@worldbank.org, rpleninger@worldbank.org, and syu2@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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
13 the firms in which AFPs have invested is a little higher (lower leverage, higher coverage ratio, and higher profitability) than that of firms that are eligible for AFP investments but in which they have not invested. However, the dispersion of financial performance is large for both groups of firms (with AFP investments or not). In fact, all groups of firms contain badly performing firms and performing firms in similar proportions. The group of eligible firms in which AFPs have acquired equity contains 17 firms with a score below 5 (i.e. scoring poorly on more than half of the 10 indicators used). By contrast, there are 33 eligible firms in which AFPs have not invested which have a liquid stock and a score of 7 or higher. The existence of such a plethora of performing stocks left aside by institutional investors and the fact that there is little pension fund money going to new market entrants is likely to create a disincentive for firms thinking about issuing securities.

**Figure 13:  Performance of Firms With and Without AFP Investments**

<!-- Start of picture text --> Financial performance of eligible Financial performance of eligible<br>companies with no AFP investment companies with AFP equity investment<br>Financial performance index Financial performance index<br>20 16<br>2015 14 12 16 15 16 13 15 12 11 13<br>8 9 10 7 7 9<br>10<br>5 2 1 1 5 0 0 3 4<br>0 0<br>0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10<br>Financial score Financial score<br>Source: Author’s calculations based on data from the FECUs and the SAFP 6<br>Number of firms<br>Number of firms<br><!-- End of picture text --> The closed family owned structure of many firms may also be at the root of decisions not to go public. Many Chilean firms have a closed family-based structure, and many others belong to family-owned conglomerates. In fact, from a universe of about 6,500 large and mega large firms, only about 250 are listed. In addition, many listed firms have only a fraction of shares in the hands of the public (the free float of about 30 percent of all listed stocks is lower than 10 percent, and for nearly half of all listed stocks, the free float is lower than 30 percent). As a result, while there are 156 firms non eligible for AFP investments

- Net margin > 5%

- RoE > 8%

- RoA > 1.5%

6 Superintencia de AFPs
(Haggard et al. 2009). One possible explanation for the different findings is that we focus on PIPE investments by PE firms whereas Haggard et al. (2009) consider all investors. As mentioned earlier, Brophy et al. (2009) document significant positive announcement returns for PIPE investments issued to non-hedge fund investors such as the PE investors in our sample but insignificant returns to PIPEs issued to hedge fund investors in the US. Similarly, Barclay et al. (2007) find that the positive short-run abnormal stock returns for US private equity placements seem to be driven largely by a relatively small fraction (about 15%) of the transactions and that these are generally the very transactions in which the buyer becomes publicly involved in the operations of the firm. For the private placement transactions where the investors are passive (about 85% of the sample), the stock market reaction is not significant.

# **4.5** **_Does firm performance improve after PIPEs issues to PE funds?_**

The event study results of Table 7 around the issue date and Table 4 in the run-up to the issue date suggest a strong certification and monitoring role for PE investors. Here, we consider the post-investment performance of our sample to examine whether the perceived value PE investors bring to firms actually materializes. In the US the post-issue performance of both PIPE and SEO issuers has been shown to suffer significant declines (see Krishnamurthy et al. 2005 and Marciukaityte et al. 2005, Dai 2007 and Chaplinsky and Haushalter, 2010 for PIPES and Eckbo et al. 2007 for SEOs). Further, Brophy et al. (2009) find that returns vary by investor type for PIPEs and buy-and-hold abnormal returns for non-hedge fund investors while negative are insignificant.

We test whether the post-issue performance of firms raising money from PE investors via PIPEs differs significantly from the firms that raise equity via SEO issues in two different ways.

22
Table 7. Effect of a 1 Percentage Point Increase in the Income Share Held by the 3<sup>rd</sup> Quintile on Share of Population With Tertiary Education

||Level of Economic Development:<br>1970 GDP per capita|Level of Economic Development:<br>2010 GDP per capita|
|---|---|---|
||(1)|(2)|
|Brunei|2.12|3.31|
|Cambodia|-0.90|0.86|
|Indonesia|-1.26|1.29|
|Laos|-1.33|1.03|
|Malaysia|-0.41|2.18|
|Philippines|-0.45|1.15|
|Singapore|0.54|3.28|
|Thailand|-0.43|1.87|
|Vietnam|-1.34|1.06|
|ASEAN Average|-0.38|1.78|
|ASEAN Median|-0.45|1.15|

Note: The table reports effects on the share of population with tertiary education (in percent) of a 1 percentage point increase in the income share held by the 3<sup>rd</sup> quintile. For column (1) the predicted effects are generated by combining estimates reported in Brueckner and Lederman (2015) with the values of countries' GDP per capita in 1970. For column (2) the predicted effects are generated by combining estimates reported in Brueckner and Lederman (2015) with the values of countries' GDP per capita in 2010. Specifically, the estimated relationship for country _c_ and period _t_ is (Share of Population Tertiary Education)ct = -5.0*Q3ct +0.8*Q3ct*ln(initial GDP per capita)c.

21
The results of the stress test are reported in the form of the following table.

**Figure 7:  Interest Rate Risk: Interest Bearing Receivables – RG 03**

|**DKr mln.**||**Re**|**d**|**Yel**|**low**|
|---|---|---|---|---|---|
||Market value|Interest<br>fall|Interest<br>increase|Interest<br>fall|Interest<br>increase|
|Nominal bonds||||||
|1. - duration ≤ 1y<br>2. - 1y <duration ≤ 3.6y<br>3. - duration > 3.6y<br>4. Indexed bonds||||||
|5. Mortgage and commercial bonds<br>6. Derivatives||||||
|7. Total||||||

# **2. Equity and Real Estate Risks**

In the Red Test equity prices are assumed to fall by 12 per cent. This is the same rate used under Basel I for banks for calculating capital requirements. In the Yellow Test equity prices fall by 30 per cent, a rate of decrease that has long been used by insurance actuaries in many countries. Real estate properties are measured at market value according to accounting standards. In the Red Test real estate market values are assumed to fall by 8 percent and in the Yellow Test by 12 percent.

**Table 3  Equity and Real Estate Risks**

|Equity Prices|Decline by 12|percent<br>Decline by 30 percent|
|---|---|---|
||Decline by 8|percent<br>Decline by 12 percent|

The results of the stress test on equity values are reported in risk report RG05 and those on real estate values in risk report RG07.

22
may find it advantageous to let emissions increase to establish a high baseline for “later” periods with even higher quota prices. In this sense, rapidly rising offset prices could be particularly harmful for emissions in a transition period to a possible final steady state. Note here that the model can and should be expanded in the time dimension, by including rising values over time for various other parameters including h and b, and to changing values of the local environmental tax rates s1 and s2; and where firms exist for a larger (variable, and endogenous) number of periods.

# **References:**

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Flues, Florens, Michaelowa, Axel, and Michaelowa, Katharina (2009), What Determines UN Approval of Greenhouse Gas emissions Reduction Projects in Developing Countries? An Analysis of Decision Making on the CDM Executive Board. _Public Choice_ , forthcoming (published online October 23, 2009).

Greiner, Sandra and Michaelowa, Axel (2003), Defining Investment Additionality for CDM projects - practical approaches. _Energy Policy_ , 31, 1007-1015.

Hagem, Cathrine (2009), The Clean Development Mechanism Versus International Permit Trading: The Effect of Technological Change. _Resource and Energy Economics_ , 31, 1-12.

Lazarus, Michael, Kartha, Sivan and Bernow, Steve (2000), _Key Issues in Benchmark Baselines for the CDM: Aggregation, Stringency, Cohorts, and Updating_ . Tellus Institute, Stockholm Environment Institute: Boston.

Michaelowa, Axel and Purohit, Pallav (2007), Additionality Determination of Indian CDM Projects. Can Indian CDM Project Developers Outwit the CDM Executive Board? Discussion Paper CDM-1, Climate Strategies, London.

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# **The Power of Information:**

# **The Impact of Mobile Phones on Farmers’ Welfare in the Philippines**<sup>**1**</sup>

Julien Labonne and Robert S. Chase

> 1 We would like to thank Jean-Louis Arcand, Xavier Gine, Jennifer Hunt, Charles Kenny, Ghazala Mansuri, Stephen Mink, Emmanuel Skoufias and Mark Williams for helpful comments. We also wish to thank Bhuvan Bhatnagar, Andrew Parker, Arsenio Balisacan, Rosemarie Edillon, Sharon Piza and all the staff of APPC without whom the field work would have been impossible. Shiel Velarde and Siobhan Murray provided extremely valuable help accessing and using the GIS datasets. Further, we are grateful to the Philippines Department of Social Welfare and Development for allowing us to use the data. Financial support from the Trust Fund for Environmentally and Socially Sustainable Development is gratefully acknowledged. All remaining errors are ours. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors and should not be attributed in any manner to the World Bank, to its affiliated organizations or to members of its Board of Executive Directors or the countries they represent.

1