rafmacalaba/data-use-mentions-extended huggingface dataset:
# **V.   CONCLUSIONS**

The global financial crisis has led to widespread calls to reform bank regulation and supervision. Changes in bank capital regulation have been at the heart of the resulting policy discussions led by global banking regulators. In redesigning prudential standards to incorporate lessons from the recent turmoil, the Basel committee of supervisors has grappled with two important questions in particular: what type of capital should banks hold to ensure that they can better withstand periods of economic and financial stress? And should a simple leverage ratio be introduced to reduce regulatory arbitrage and improve transparency?

Our paper sheds light on both of these questions by investigating whether banks’ stock returns were affected differently depending on whether banks entered the financial crisis with a better capital position. Specifically, we use a quarterly panel of bank data for 12 countries for 20062009 to study the impact of bank capital and its different definitions and components on changes in market valuation of banks. Using the crisis period that started in August 2007 as an unexpected negative shock, we explore whether market participants perceive different capital definitions to be effective measures of banks’ ability to withstand stress.

We find that before the crisis, differences in initial capital – whether risk-adjusted or not, however defined – did not consistently affect subsequent bank stock returns. The effect becomes evident only during the crisis period, and even then it is significant and robust just for the largest banks in our sample. This is consistent with the implication that a negative relationship between risk and capital should be stronger for undercapitalized banks, which is the case for larger banks in our sample. Our results also suggest that during the crisis stock returns of large and undercapitalized banks were much more sensitive to leverage ratios as opposed to risk-adjusted capital ratios. This may be because market participants viewed risk- adjusted ratios as much less informative given they were more easily subject to manipulation and therefore less transparent. Finally, the positive association with subsequent stock returns is stronger for higher quality capital (Tier 1 leverage and common equity), but these
<!-- Start of picture text --> 7<br>Old-age pensions as transfers Old-age pensions as deferred income<br>6<br>5.8<br>5.3<br>5<br>4<br>3.5<br>3<br>2.7<br>2<br>1<br>0<br>Market Income (M.I.) Disposable Income Consumable Income<br>Poverty gap (%)<br><!-- End of picture text --> Source: Own estimates based on Turkey 2016 HBS. Note: The figure shows the poverty rate and the poverty gap for income concepts described in Figure 1.

The combined impact of direct taxes and direct transfers reduces the incidence of poverty. The poverty headcount ratio decreases from 11.1 to 10.3 percent, when pensions are treated as deferred income. Similarly, the poverty gap is strongly reduced by the combined action of direct taxes and transfers.

Indirect taxes, however, lead to substantial increases in poverty. While all individuals are affected by indirect taxes along the income distribution, the less well-off suffer a relatively higher impact (as shown in the inequality results of Figure 3). As such, poverty rises by more than 5 percentage points from Disposable to Consumable income. The poverty gap increases by almost 50 percent, from 2.7 to 5.3 percent.

In comparative terms, the overall impact of taxes and transfers on poverty in Turkey and peer countries is mixed (Figure 5). Poverty decreases in 4 countries, increases in 4 others, and stays constant in one of them. A common pattern across countries is that indirect taxes are poverty increasing, since low-income households allocate all their income to consumption. Turkey’s relative performance is below-median, driven by the relatively stronger poverty-increasing impact of indirect taxes, and the comparatively smaller poverty-decreasing impact of transfers and direct taxes. While the poverty rate for Consumable
. Although in principle student reported information might not accurately reflect actual labor activities, we do find a strong and statically significant negative association between results in the standardized test and number of working days reported in the _Enlace de contexto_ (Appendix Figure A1). The impacts of PAL are reported in columns 6 and 7 in Table 10. We do observe increases in the number of working days among students from both in-kind and cash localities, with the effect being particularly large for the latter although the differential treatment is not statically significant at conventional levels. Similarly we do observe a large increase in the share of children who report helping with household work but the effects are not statistically significant, arguably for the limited sample size.

Basu et al. (2010) suggest that, in the presence of multiple factor market failures, the introduction or expansion of a productive asset could result in increases in child labor. Information from the baseline household survey shows that the probability that a child age 12-13 exclusively attends school declines with the total number of animals owned by her family while

> 21We test whether characteristics are balanced across the three groups for the students we find in the _ENLACE de Contexto_ . Table A7 reports pre-intervention sample means by treatment group. Overall, characteristics of students observed in _ENLACE de Contexto_ are balanced with very few exceptions.

19
background, data and methodology**

# **3.1 The bank**

The data we use for the empirical analysis was generated using the Management Information System (MIS) of AccessBank Azerbaijan.<sup>**9**</sup> The mission of AccessBank is to provide financial services at European standards to micro and small businesses and low and medium income families while also offering products for larger enterprises.<sup>10</sup> AccessBank was founded by international financial institutions, which are still shareholders today, in October 2002 under the name Micro Finance Bank of Azerbaijan (MFBA). Until today,

> 9 To get a better understanding of the bank’s business, the different loan types and borrower groups as well as the crisis events, we conducted informal interviews with the bank’s management.

> 10 See http://www.accessbank.az/en/index.html or AccessBank (2008) for detailed information on the bank and its business activity.

8
�1 < a1 for individuals with more self-commitment problems proxied by zi.

# 3 The Experiment

## 3.1 The Sample

We purposively chose urban Ghana as the setting for this study. The choice of Ghana was motivated by the desire to provide evidence in an African context, in a country known for a history of involvement of women in business which provides a setting that is conducive to female business success. Women in Ghana have similar labor force participation rates to men, and are more likely to be self-employed. Evidence of this is seen in data from the 2000 Ghanaian Census: the labor-force participation rates for 15-60 year olds are 69.6 percent for females and 73.9 percent for males, and in urban areas 45 percent of females are non-agricultural own-account workers, compared to 33 percent of males. This contrasts sharply with Sri Lanka, the setting for the experiment in de Mel et al. (2009a), where only 7.8 percent of prime age females are self-employed, compared to 29.7 percent of prime age males.

Within Ghana we chose Accra, the capital and largest city, and the nearby industrial city of Tema. A sample of microenterprises (which we term the Ghana Microenterprise Survey) was then constructed as follows. First, enumeration areas (EAs) were selected with probability proportional to the number of households in these EAs according to the 2000 census. We randomly selected 70 EAs in Accra and 30 in Tema. Then, to reduce the costs of listing, we subdivided EAs into equal areas, such that each area would contain approximately 70 to 80 households. This typically required dividing an EA into half or thirds. One of these areas was then randomly selected from each EA. Enumerators went door to door in this area to carry out a screening survey of each household. Households were screened to identify those with an individual age 20 to 55 who was self-employed and working 30 or more hours per week in a business with no paid employees and no motorized vehicle. These
**<mark>Table A.3 Definition of variables used from weekly/triweekly interviews</mark>**

|Variable|Definition|
|---|---|
|Phone weekly|Dummy variable = 1 if respondent was assigned to weekly phone<br>interview treatment|
|In person weekly|Dummy variable = 1 if respondent was assigned to weekly in person<br>interview treatment|
|Phone triweekly|Dummy variable = 1 if respondent was assigned to triweekly phone<br>interview treatment|
|Working|Dummy variable = 1 if respondent completed work over the reference<br>period|
|Days|Days worked per week|
|Days conditional on work|Days worked per week conditional on having reported any work that<br>week|
|Hours|Hours worked over the reference period (e.g. in case the reference<br>period is a week it is number of days worked per week X number of<br>hours worked per day; in case the reference period is a day it is the<br>number of hours worked that day)|
|Hours conditional on work|Hours worked per week over the reference period (if the reference<br>period is a week the number of hours conditional on work is calculated<br>by multiplying number of days worked per week times number of<br>hours worked per day; if it is per day it is simply the number of hours)<br>conditional on havingreported anywork duringthe referenceperiod|

|**Table A.4 Definition of variables used from**<br>Variable|**endline survey**<br>Definition|
|---|---|
|Self-employed|Dummy variable = 1 if primary work activity is self-employment, 0 if<br>primary work activity is wage employment, out of labor force, or<br>unemployed|
|Wage-employed|Dummy variable = 1 if primary work activity
capital goods are taxed. Because the capital account is exogenous, rates of return across countries can differ over time and across simulations. The model solves only for relative prices. The numeraire, or price anchor, in the model is given by the export price index of manufactured exports from high-income countries. This price is fixed at unit value in the base year and throughout time.

The new version of the LINKAGE model, Version 6, is based on the latest release of the GTAP dataset, Release 6.0.<sup>6</sup> Compared with Version 5 of the GTAP dataset, Version 6 has a 2001 base year instead of 1997, updated national and trade data, and, importantly, a new source of the protection data. The new protection data come from a joint CEPII (Paris)–ITC(Geneva) project. The product of this joint effort, known as MAcMap, is an HS-6 detailed database on bilateral protection that integrates trade preference, specific tariffs, and TRQs.<sup>7</sup> In summary, the new GTAP database has lower tariffs than the previous database because of the reform efforts between 1997 and 2001—for example, China’s progress toward WTO accession and continued implementation of the Uruguay Round Agreement—and the inclusion of bilateral trade preferences.

The version of LINKAGE used for this study comprises a 27-region, 25-sector aggregation of the GTAP dataset.<sup>8</sup> There is a heavy emphasis on agriculture and food, which make up 13 of the 25 sectors, and a focus on the largest commodity exporters and importers.

> 6. GTAP is an international consortium of trade researchers from universities, research institutions, and national and international agencies. It is based at Purdue University. The GTAP Center provides four key resources to the trade community. First is an integrated and consistent international database for trade policy analysis. The current version is composed of 87 country and region groupings and 57 economic sectors. The second is
# **4 Data and statistics summary**

In order to estimate equations (1), (2) and (3), we use data from the Orbis database of Bureau Van Dijk. The Orbis dataset provides retrospective information on company ownership and reports the country of origin of each foreign shareholder, although the share value is often unknown. It also provides information on employment and turnover. We extracted data on foreign ownership on all manufacturing firms (an annual average of about 63,000 companies) for the period 1997 to 2012. The data covers about 58% of Polish firms as reported by Eurostat and coverage varies significantly across regions (Farole et al., 2017).<sup>5</sup> Figure A.4 shows the evolution of the percentage of companies with at least one foreign shareholder from either Germany, French, the United States or the United Kingdom. In the case of German shareholders, sectors are divided into those with the highest pre-determined export share (above median, red line) and those with lower level of specialization (below median, blue line). The figure confirms the sharp increase in German acquisitions of Polish firms (anticipated in Figure 1) right after EU accession, and at a remarkable greater extent for sectors with higher export shares. The percentage of German owned companies peaked in 2007 and 2009 at about 1.8% overall, corresponding to about 18% of total employment (according to our sample). While foreign acquisitions began in 2001 in concomitance with the conclusions of the accession negotiations, it was only after joining the EU that FDI experienced a sharp rise. Foreign acquisitions increased also from other EU countries, such as France and the United Kingdom, and non-EU major trade partners, such as the US, but to a much lower extent. This suggests that German foreign investment played an important role. This pattern is confirmed also when considering an alternative measure of foreign ownership based on the country of origin of the Global Ultimate Owner (GUO
score is positively correlated with the best available measure of post-MNREGA mechanization – the number of combines in a district in 2011. I then divide districts into quartiles based on their mechanization index score and run my difference-in-differences regression from equation 1 on districts in each quartile.<sup>25</sup> I find in the full, nationwide sample, districts with the highest mechanization scores have sub-

> 23For details of each component of the index, as well as heterogeneity results by the individual components, see Appendix 3.8.

> 24Proposition 2 of my model indicates the impact of MNREGA on agricultural fires will vary across districts based on the median farm size, _AD_ . Specifically, given a distribution of _A_ that is single-peaked and where the threshold farm size for adoption of mechanization ( _A_<sup>ˆ</sup> ) is above _AD_ districts with initially higher _AD_ will see a larger increase in fires after the implementation of MNREGA. Figure A2 indicates that the average distribution of farm sizes across India does appear to be single-peaked. Further, Foster and Rosenzweig (2011) report that in the pre-MNREGA period (the late 1990s) fewer than 10% of farms were mechanized, suggesting a level of _A_<sup>ˆ</sup> substantially above _AD_ .

> 25Li (2017) suggests that areas with higher land concentrations, an important piece of my mechanization score, had lower pre-MNREGA agricultural wages than other districts, which suggests my mechanization score is not simply capturing districts with high wages prior to MNREGA. Using, imperfect, wage data from ICRISAT I find that the correlation between the Z score of district wages in 2005 and the mechanization index is -0.11 ( _p_ = 0 _._ 71). I directly measure the correlation between the Z score of per capita GDP and my mechanization score and find that it is -0.07 ( _p_ = 0 _._ 45) which suggests that I am also not simply identifying wealthier districts.

18
Our numbers were obtained directly from cash flow data and projections, in contrast to indirect estimates based on the cost of private versus public funds, for instance. Thus, the IRRs we report can be used to evaluate whether the advantages or disadvantages of PPPs compensated for the higher or lower cost of funds when compared with public provision.

Our estimates for the rates of return have a large dispersion, implying a large variance in the results of PPP projects. This explains the pressure for contract renegotiation and underlies the fiscal risks discussed earlier in this paper. The large variance in actual returns indicates that infrastructure investment is intrinsically risky, independent of whether the project is financed by the public purse or as a PPP.

# <u>4.2 Methodology</u>

One of the challenges we faced when calculating the IRRs was the switch from traditional Chilean accounting standards to IFRS standards that took place in the mid-2000s. This meant that the income statements could not be used directly, because several of its components are marked to market under IFRS. This change also implied that fluctuations in interest rates can affect the operational results, which would bias our IRR estimates. For this reason and taking advantage of the fact that PPPs are Single Purpose Vehicles (SPVs), we used cash inflows and outflows –which are not marked to market-- to determine the profitability of projects. These cash flows are somewhat erratic in individual years, with inflows and outflows that are large in magnitude and whose nature is sometimes unspecified. Over longer periods however, this problem tends to cancel out and should not have a large impact on our estimates. Also, some items were excluded for reasons we explain below.

To estimate the IRR of a given PPP, we began by constructing a cash-flow series for years with information on cash flows. We classified inflows and outflows reported in the SPV’s financial statements into three categories: operations, investment, and financing. Next, we used this information to determine net investment cash flows and net operational
Measuring Total Factor Productivity Using the Enterprise Surveys: A Methodological Note<sup>_∗_</sup> David C. Francis Nona Karalashvili Hibret Maemir Jorge Rodriguez Meza<sup>_†_</sup> _Keywords_ : TFP, Enterprise Surveys.

_JEL Classification_ : 012

> _∗_ This note uses the dataset “Firm Level TFP Estimates and Factor Ratios ~~S~~ eptember ~~1~~ 0 ~~2~~ 020.dta” which is published at `www.enterprisesurvyes.org` in the Firm-Level Datasets for Researchers section. The estimation procedure of this note uses the full cross-country ES data, which is continuously being updated as new data becomes available. Every month estimates are updated, and older datasets are archived and available upon request.

> _†_ The authors are from the Enterprise Analysis Unit of the World Bank. We would like to thank Arvind Jain and Rita Ramalho for very helpful comments. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
_differs_ both within and between treining **modes** (Table **1).** Lengthier courses, in augmenting **Individual human capital** stocks, should lead to higher earnings.

**Trainees** completed their courses **in** different calendar years, from 1964-1967 (Table **2).** Year of course completion (COMP) is **added as** a continuous variable in serial form 1-4, corresponding to the years 19641967; the higher the value on the COMP coefficient, the lower would be expented earnings. Year of birth (YB) enters **as** a dummy variable (1947 - 0; 1948 - 1). Information on year of imigration was provided with the earnings data, by the National Insurance Institute. Since the labor market perform.nce of non-immigrants might be superior to that of Immigrants, a dummy variable imigrant status (IMM) was included (Israel born - 0; - Immigrant 1).

Table 2 Year of Course Completion by Training Mode

|Year of<br>Completion<br>1964|Vocational<br>Secondary<br>School<br>17|Apprenticeship<br>I-------I<br>-----<br>157|Industrial<br>School<br>..._____________<br>-----------------------<br>-|Short<br>Teenager<br>Course<br>_____________<br>--------<br>7|All<br>Modes<br>____<br>181|
|---|---|---|---|---|---|
|1965|217|339|17|33|606|
|1966|191|119
# **Additional References**

- Aga, D. A., Noorderhaven, N., & Vallejo, B. (2016). Transformational leadership and project success: The mediating role of team-building. _International Journal of Project Management, 34_ (5), 806–818. <u>https://doi.org/10.1016/j.ijproman.2016.02.012</u>

Ahsan, K., & Gunawan, I. (2010). Analysis of cost and schedule performance of international development projects. _International Journal of Project Management_ , _28_ (1), 68–78. <u>https://doi.org/10.1016/j.ijproman.2009.03.005</u> Biscaye, P. E., Reynolds, T. W., & Anderson, C. L. (2017). Relative Effectiveness of Bilateral and Multilateral Aid on Development Outcomes. _Review of Development Economics_ , _21_ (4), 1425–1447. <u>https://doi.org/10.1111/rode.12303</u>

- Dollar, D., & Levin, V. (2005). _Sowing and Reaping: Institutional Quality and Project Outcomes in Developing Countries_ (Policy Research Working Paper No. 3524). World Bank, Washington, DC. <u>https://doi.org/10.1596/1813-9450-3524</u>

- Dreher, A., Nunnenkamp, P., & Thiele, R. (2008). Does Aid for Education Educate Children? Evidence from Panel Data. _The World Bank Economic Review_ , _22_ (2), 291–314. https://doi.org/10.1093/wber/lhn003

- Dreher, A., Langlotz, S., & Marchesi, S. (2017). Information Transmission and Ownership Consolidation in Aid Programs. _Economic Inquiry_ , _55_ (
>Cutting jobs in the corporate and investment division|
|UniCredit SpA (Italy)| Restructuring its investment banking operations, and reorganizing its CEE<br>businesses; Launching a Є7.5 billion capital increase; Planning to narrow its<br>geographic focus in Eastern Europe, retreating from less profitable markets and<br>focusing on its profitable markets (Poland, Turkey, Russia and Croatia); Cutting its<br>reliance on funding from abroad at their units in CEE countries and focusing more<br>on local funding|
|Volksbank (Austria)| Selling its subsidiaries from its diversified presence in nine CEE countries (except<br>for its Romanian operation) to a Russian bank (Sberbank), reflecting the reduced<br>ability of diversified banks to move resources across their operations in different<br>jurisdictions as national regulators try to prevent outflows of capital and liquidity<br>(through ring-fencingof their subsidiaries)|
|Wells Fargo (USA)| Acquiring loan and securities portfolios being sold off by ailing European lenders<br>Purchased$2.6 billion worth of loans from Irish banks|

Sources: ECB and IIF Survey of Lending Standards; JP Morgan Analyst Reports, various news sources (including the _Financial Times_ , the _Wall Street Journal_ , _The Economist_ , Reuters.