rafmacalaba/datause-encoder-data huggingface dataset:
- Beck, T., F. K. Rioja, N. T. Valev, and B. Buyukkarabacak. 2012. Who Gets the Credit? And Does It Matter? Househols vs. Firm Lending across Countries.” _The B.E. Journal of Macroeconomics 12_ , 2.

- Berger, A., I. Hasan, and L. Klapper. 2004. “Further Evidence on the Link between Finance and Growth: An International Analysis of Community Banking and Economic Performance.” _Journal of Financial Services Research 25_ (2), 169–202.

- Bolton, P., T. Santos, and J. Scheinkman. 2011. “Cream Skimming in Financial Markets.” Working Paper 16804, National Bureau of Economic Research.

- Brunnermeier, M. K., and S. Nagel. 2004. “Hedge Funds and the Technology Bubble.” _Journal of Finance 59_ (5), 2013–40.

- Cecchetti, S. G., and E. Kharroubi. 2012. “Reassessing the Impact of Finance on Growth.” Working paper 381, Bank for International Settlements.

- Cecchetti, S. G., and E. Kharroubi. 2013. “Why Does Financial Sector Growth Crowd out Real Economic Growth?” Technical report, Bank for International Settlements.

- Cetorelli, N., and P. E. Strahan. 2006. “Finance as a Barrier to Entry: Bank Competition and Industry Structure in Local U.S. Markets.” _Journal of Finance 61_ (1), 437–61.

- Chipman, H. 1996. “Bayesian Variable Selection with Related Predictors.” _Canadian Journal of Statistics 24_ , 17–36.

- Christopoulos, D. K., and E. G. Tsionas. 2004. “Financial Development and Economic Growth: Evidence from Panel Unit Root and Cointegration Tests.” _Journal of Development Economics 73_ (1), 55–74.

- Ciccone, A., and M. Jarocinski. 2010. “Determinants of Economic Growth: Will Data Tell?” _American Economic Journal: Macroeconomics 2_ (4), 222–46.

- Cihak, M., A. Demirguc-Kunt, E. Feyen, and R. Levine. 2013. “Financial Development in 205 Economies, 1960 to 2010.” Working Paper 18946, NBER.

- Claessens, S., and L. Laeven. 2005. “Financial Dependence, Banking Sector Competition, and Economic Growth.” _Journal of the European Economic Association 3_ , 179–207.

- Cuaresma, J. C., G. Doppelhofer, and M. Feldkircher. 2014. “The Determinants of Economics Growth in European Regions.” _Regional Studies 48_ , 44–67.

- Doppelhofer, G., and M. Weeks. 2009. “Jointness of Growth Determinants.”

47
# **Figure 4.1 Public and private investment growth**

_In AEs, public investment growth has moved broadly counter‐cyclically to private investment growth since 2008. In EMDEs, the counter‐cyclical public investment boost of 2008‐09 offset a sharp slowdown in private investment growth, but was followed by a period of slowing public and private investment growth. Private investment weakness was most pronounced in BRICS and commodity‐exporting EMDEs._

## **A. Public investment growth**

<!-- Start of picture text --> Percent 1990-2008 average<br>2003-08 average<br>20<br>15<br>10<br>5<br>0<br>-5<br>-10<br>AEs EMDEs<br>2008 2009 2010 2011 2012 2013 2014 2015 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->

## **C. Contributions to investment growth**

<!-- Start of picture text --> Percentage points<br>12<br>10 Public Private<br>8<br>6<br>4<br>2<br>0<br>-2<br>World AEs EMDEs<br>2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->

## **B. Private investment growth**

<!-- Start of picture text --> Percent<br>1990-2008 average<br>20 2003-08 average<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>AEs EMDEs<br>D. Contributions to investment growth<br>Percentage points<br>16 Public Private<br>12<br>8<br>4<br>0<br>-4<br>Commodity Commodity BRICS<br>exporters excl. importers excl.<br>BRICS BRICS<br>2008 2009 2010 2011 2012 2013 2014 2015 2008 2009 2010 2011 2012 2013 2014 2015<br>2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> Sources: Eurostat, General Statistics Office of Vietnam, Haver Analytics, International Monetary Fund, Ministry of National Economy of the Republic of Kazakhstan, OECD, Reserve Bank of India, Sri Lanka Ministry of Finance, World Bank. Note: Public and private investment growth rates are weighted average of gross fixed capital formation growth rates in the public and private sectors, respectively, in constant 2005 U.S. dollars. The sample includes 20 advanced economies and 99 EMDEs for 1990 to 2015.

58
**Table 5. Prediction results**

||**BMApredictions**|**Best modelpredictions**|
|---|---|---|
|Value of loss function|0.303|0.329|
|Cut‐off probability threshold|0.362|0.357|
|Correct alarms as % of total alarms|0.800|0.725|
|Correct non‐alarms as % ofquiet moments|0.595|0.617|

Results based on out‐of‐sample predictions for the period 1995‐2004 (337 observations).

15
Dague, DeLeire, and Leininger (2014) presented evidence on a significant reduction in employment and work hours as a result of the introduction of the food stamp and Medicaid programs. Several other papers, on the other hand, including Blau (2003), Currie (2003), Gruber (2003), and Olsen (2003), found no evidence of work disincentives for programs such as food stamps and nutrition benefits, childcare subsidies, or various in-kind programs.

The evidence from a number of recent studies on other countries is also inconclusive. On the one hand, Lemieux and Milligan (2007) examined the effects of a new policy in Quebec, Canada that tripled the benefits of a certain demographic group and found that the more generous benefits reduced employment significantly. The decline in the employment rate was found to be between 3 and 5 percent. Similarly, Fernandez and Saldarriaga (2014) found that the recipients of a conditional cash transfer (CCT) program (Juntos) in Peru reduced their involvement in the labor force by six to 10 hours per week, and Sahn and Alderman (1995) showed that a food subsidy program in Sri Lanka reduced the labor supply by 10 percent on average. Finally, Dabalen, Kiloic, and Wane (2008), using Albanian household-level panel data, demonstrated that the negative effect of social transfers on the supply of labor was sizable.

On the other hand, a number of other studies, including Skoufias and Di Maro (2008), Skoufias, Unar, and Gonzalez-Cossio (2008), and Freije, Bando, and Arce (2006), found no evidence of reduced work efforts in their evaluation of the CCT programs (Oportunidades-Progresa) and in-kind programs in Mexico. At the same time, these programs were found to be successful in reducing poverty and child labor. Alzua, Cruces, and Ripani (2012) examined the welfare programs of Mexico, Nicaragua, and Honduras and found small and insignificant effects on the labor supply, and Abdulai, Barrett, and Hoddinott (2005) examined Ethiopian household-level data and concluded that the food aid program had no disincentive effects.

On the interaction between social protection and informal employment, the empirical literature is almost silent except for a few recent studies in Latin American countries. For example, Bosch and Campos-Vasquez (2014) and Aterido, Hallward-Driemeier, and Pages (2011) found that the public health insurance program in Mexico (Seguro Popular) had significantly reallocated labor toward the 4
22 scaled by the size of the corresponding economies (i.e., by their gross domestic product), the largest (deepest) financial system is still some 110 times the smallest (least deep) one. And even if the top and bottom 5 percent of this distribution are taken out, the ratio of the largest to the smallest is about 28 – a large degree of disparity, considering that these are not raw figures but ratios relative to the size of economy. Similar orders of magnitude are obtained for the other characteristics of financial systems.<sup>14</sup> In other words, when one examines country-level data, there are vast differences in financial sector depth, as well as in the other characteristics. The cross-country differentiation along the key characteristics of financial systems can be seen from the scatter plots in Figure 4 as well as from cartograms such as the one shown for illustration in Figure 6. The scatter plots and the cartogram underscore the large cross-country differences. The measurement framework underscores that financial sectors in jurisdictions such as the United States and Korea exhibit a relatively great financial market depth, as one would expect. United States have less deep financial institutions, reflecting a less bank-centric (and more market-based) nature of the U.S. financial system. Several European countries exhibit relatively great financial depth.

Financial systems have changed substantially in the 2000s in the run-up to and during the crisis. As illustrated in Figures 7 and 8, the most visible change is the observed declines in the stability index, which in turn reflects the increased volatility in returns by financial institutions in some countries and in most financial markets. But the charts also illustrates that stability has not been the only dimension in decline and that to some extent it has been accompanied also by difficulties along other characteristics, such as reduced depth and access to finance and in some cases also reductions in efficiency, particularly in financial markets.

Overall, the data from the Global Financial Development Database suggest that the key disparities among countries in terms of the nature of their financial systems have somewhat subsided in the aftermath of the crisis, as financial sectors in many medium- and low- income countries were relatively more isolated from the global turmoil, and therefore less affected by the global liquidity shocks. In addition, financial institutions on average rebounded faster than markets, showing improvements in depth and efficiency after the crisis. This seems to have been the case so far for example for Brazil and other Latin American countries (de la Torre, Ize, and Schmukler 2011), China, and many Sub-Saharan African countries (see, for example, World Bank 2012). The medium-term effect of the crisis on financial systems still remains to be seen.

> 14 To put this in a more anthropomorphic perspective, the tallest adult person on Earth is less than 5 times taller than the smallest person (www.guinessworldrecords.com).
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to ensure that all material outsourcing arrangements are covered by robust and enforceable agreements.

Licensees must have procedures to select and monitor the performance of service providers. Termination and default provisions must allow for cancellation of a contract without prejudicing the interests of fund members. In addition, the terms of service agreements with related entities, including employer sponsors, must be on arm’s length terms. Service agreements must also provide that APRA may have access to documents and the premises of the service provider, and may request an audit of the provider.

# **2.5 Trustee resources standard**

This standard specifies that trustees must have adequate resources – financial, technical and human – to manage members’ funds prudently. It recognizes that benchmarks for adequacy will vary from fund to fund, and will depend importantly on the extent of outsourcing.

Where trustees’ expenses are met from fees or from earnings, they must prepare operating budgets to demonstrate that they will remain solvent and have sufficient liquidity to meet payments. Where expenses are met by an employer, APRA would take into account the likelihood of the employer’s continuing support. Demonstrating sufficiency of resources will also require appropriate disaster recovery and business continuity plans. Where functions are outsourced, trustees will have to satisfy themselves about a service provider’s resources, including for disaster recovery and business continuity.

# **2.6 Net tangible assets standard**

A trustee with a public offer license must have $A5 million in net tangible assets or a guarantee (or a combination of both) unless the assets under its trusteeship are held by a custodian who meets these requirements.

The capital requirement has three objectives: a buffer against operational risks; evidence of a trustee’s commitment to its superannuation business; and an incentive for the trustee to manage that business well. It may also provide funds to satisfy member actions against trustees for liability where professional indemnity insurance is inadequate.

# **2.7 Auditors**

The revised regime gives a significant role of the external auditors of funds. Auditors must audit the annual statistical returns that funds provide to APRA. They must also conduct annual audits of risk management strategies and plans, stating an opinion as to whether the trustee has complied with these strategies and plans and whether it has adequate systems in place to ensure future compliance. They must refer any identified weaknesses or contraventions to the trustee regardless of materiality. The auditor’s annual report, augmented by these changes, must be completed and provided to APRA within four months of the end of the financial year.

Statutory whistle-blowing provisions require auditors to inform APRA _at the same time_ as they notify trustees of any material contraventions of legislation that may 12
**Table 7: Impacts on Financial Control, Access to Finance, and Capital Levels**

||Ratio of<br>Worse/Curren|Monitors<br>t<br>Sales|Fraction<br>Stock Lost|Received a <br>bulk|Production<br>Cost|Received<br>Loan from|Has<br>Business|Inventory|Capital<br>Stock|
|---|---|---|---|---|---|---|---|---|---|
||Profit|Trends|to spoilage|discount|Change(%)|Bank/MFI|Bank Account|Value|Value|
|**Panel A: Impact of Assignment to Trainin**|**g, Allowing for S**|**pillovers**||||||||
|Assigned to Training*1 Year|0.014|0.112***|‐0.007|0.009|15.536|0.017||2475|‐52|
||(0.009)|(0.023)|(0.014)|(0.023)|(15.561)|(0.017)||(2125)|(882)|
|Assigned to Training* 3 Years|‐0.007|0.141***|‐0.014|0.034|13.954|0.013|0.058**|5889**|3219***|
||(0.011)|(0.022)|(0.021)|(0.023)|(19.084)|(0.019)|(0.022)|(2978)|(1085)|
|Spillover Group * 1 Year|‐0.004|0.025|0.037**|‐0.028|7.825|0.014||‐339|‐828|
||(0.009)|(0.024)|(0.017)|(0.025)|(17.121)|(0.018)||(1966)|(917)|
|Spillover Group * 3 Years|‐0.011|0.000|‐0.005|0.013|‐5.558|‐0.017|‐0.003|3350|672|
||(0.012)|(0.023)|(0.024)|(0.024)|(20.019)|(0.021)|(0.023)|(2829)|(1065)|
|Mean of Pure Control Group|0.450|0.269|0.194|0.423|160.4|0.172|0.354|19918|13410|
|Sample Size|10609|6214|4989|5879|5541|5404|3005|6183|6203|
|P‐value: Training 1 year=3 years|0.136|0.263|0.766|0.447|0.935|0.850||0.107|0.002|
|P‐value: Spillover 1 year=3 years|0.622|0.355|0.102|0.255|0.526|0.139||0.093|0.174|
|P‐value: Training effect zero both years|0.252|0.000|0.767|0.308|0.569|0.583||0.137|0.005|
|**Panel B: Impact of Mentoring Compared**|**to Training Alon**|**e, Allowing**|**for Spillovers**|||||||
|Assigned to Mentoring|‐0.024*|0.194***|‐0.038|0.029|‐8.074|0.004|0.048|15371***|3180**|
||(0.012)|(0.029)|(0.026)|(0.030)|(23.297)|(0.026)|(0.034)|(4523)|(1504)|
|Assigned to Training Alone|‐0.015|0.070***|‐0.009|0.002|‐15.671|0.044*|0.099***|4705*|2561*|
||(0.013)|(0.024)|(0.028)|(0.030)|(25.575)|(0.026)|(0.025)|(2793)|(1330)|
|Spillover Group to Mentoring|‐0.010|‐0.012|0.002|‐0.012|‐3.198|‐0.023|0.010|9707**|628|
||(0.014)|(0.031)|(0.032)|(0.030)|(25.752)|(0.027)|(0.035)|(3770)|(1365)|
|Spillover Group to Training Alone|‐0.029**|‐0.014|‐0.029|0.010|‐41.846|0.006|0.023|2649|‐133|
||(0.014)|(0.032)|(0.034)|(0.032)|(26.112)|(0.028)|(0.025)|(3198)|(1451)|
|Sample Size|4563|2636|2078|2497|2389|2236|2636|2634|2635|
|P‐value: Mentoring= TrainingAlone|0.515|0.000|0.353|0.432|0.791|0.157|0.137|0.021|0.720|

Notes: Robust standard errors in parentheses, clustered at the market level. *, **, and *** denote significance at the 10, 5, and 1 percent levels. All regressions control for randomization strata fixed effects. Mentoring regressions only for three year follow‐up. Business bank account not asked in round 2 follow‐up survey.

34
32 Table 3. Expenditures on subsidized food by expenditure quintile group in urban Egypt, 1997

|Subsidized food|Mean per<br>Lowest<br>Quintile<br>[1]|capita expe<br>Second<br>[21|nditures pe<br>Third<br>[3]|r week (inL<br>Fourth<br>[4]|E)aon subsi<br>Highest<br>Quintile<br>[5]|dized food by:<br>Average|
|---|---|---|---|---|---|---|
|Baladi bread|0.96|0.94|0.95|0.86|0.73*|0.88|
|Baladi wheatflour|0.10|0.13|0.12|0.06|0.09|0.10|
|Edible oil|0.08|0.10|0.12|0.09|0.07|0.09|
|Sugarb|0.08|0.09|0.11|0.10|0.10|0.10|
|Total expenditures on subsidized|||||||
|food byquintile|1.22|1.26|1.30|1.11|0.99|1.17|
|Percent of total expenditures<br>on subsidized food by quintile|20.75|21.43|22.11|18.88|16.83|100.00|
|Percent of total population<br>in quintile|20.00|20.00|20.00|20.00|20.00|100.00|

Source: IFPRI Egypt Integrated Household Survey, 1997.

Notes: N = 1,078. Urban population is divided into quintile groups on the basis of total per capita fitted expenditures.

**a** In 1997,1 Egyptian pound (LE) = US$0.295. All expenditure figures are in nominal terms. **b** Includes expenditures on fully and partially subsidized sugar.

Difference between lowest and highest quintile groups is significant at the 0.05 level.
|Sudan|IDP<br>Profiling<br>Survey<br>(2018)|Stratified cluster<br>sampling|Abu Shouk and El Salam<br>camps, and neighboring and<br>non-neighboring Al-Fashir|IDPs and host<br>communities|17,645<br>(95.2%)|
|---|---|---|---|---|---|
|South<br>Sudan|High<br>Frequency<br>Survey<br>Wave 4<br>(2017)|Stratified two-<br>stage cluster<br>design|Urban areas of seven of the<br>ten pre-war states (Western<br>Equatoria, Central<br>Equatoria, Eastern<br>Equatoria, Northern Bahr-<br>El-Ghazl, Western Bahr-El-<br>Ghazal, Warrap, Lakes<br>state).|IDPs and host<br>communities|4,554<br>(92.8%)|

Data was available for all selected MPI indicators in every country except South Sudan, where the question on access to a bank account was not included in the survey. The MPI for South Sudan is thus constructed without this indicator, and the remaining indicators in the financial security dimension are re-weighted accordingly, receiving 1/6 of the weight each.

# **5. Findings**

## **5.1 Key findings**

The focus of this study is to analyze the level and composition of multidimensional poverty among forcibly displaced communities across countries and compared to host communities, respecting each of the survey designs. Table 3 shows the headline figures for the MPI disaggregated by displacement status. Overall, the highest level of poverty can be found in the Somalia population covered, where over 63% of IDPs and nearly 45% of the host community are identified as multidimensionally poor, while the lowest overall poverty is in the population covered in N.E Nigeria, with 23% of IDPs and 17% of non-IDPs suffering from poverty. As expected, many countries show large inequalities, with displaced persons being poorer across all five countries according to MPI and incidence of multidimensional poverty, with statistically significant result for all countries but Nigeria and South Sudan. In addition, there is large variation in the level of poverty between the two groups across countries. For instance, the difference in the incidence between displaced and host communities ranges from 6 percentage points in Nigeria (not significant) to 15 and 19 percentage points in South Sudan and Somalia, and to over 30 percentage points difference in Ethiopia and Sudan, where 46% of refugees and 44% of IDPs are poor compared with 13% and 9% of the host community, respectively. Interestingly, we find that the intensity of poverty (average share of weighted deprivations experienced by the poor) is similar across displaced and non-displaced populations in all countries, with the only significant difference between IDPs and host communities appearing in Nigeria.

_Table 3: Main results disaggregated by displacement status_

|**Country**|**Displacement status**|**MPI**|**Confidence**|**Incidence**|**Intensity**|**Population**|
|---|---|---|---|---|---|---|
||||**interval**|**(H)**|**(A)**|**share (sample)**|
|**Ethiopia**|Host Community|0.073|(0.057, 0.090)|12.8%|57.3%|12.0%|
||Refugee|0.270|(0.233, 0.307)|45.5%|59.4%|88.0%|

12
23

**Figure 14. Income inequality** **<u>worsened more in countries that experienced greater economic slowdown</u>**

<!-- Start of picture text -->
-10 -5 0 5 10<br>pp change in GDP growth 2012 vs 2007<br>East Asia and Pacific Central-Eastern Europe Western Europe Latin America/Caribbean<br>Middle East and North Africa South Asia Africa North America<br>Source : Authors’ computations based on World Bank’s World Development Indicators and OECD database.<br>Figure 15. Uneven impact of the crisis on income inequality within the advanced world<br>60<br>Reduced income inequality  Increased income inequality<br>50<br>40<br>30<br>20<br>10<br>0<br>Last value for 2003-2007 Last value for 2008-2011<br>Source : Authors’ computations based on World Bank World Development Indicators and OECD Database.<br>15<br>10<br>5<br>0<br>-5<br>-10<br>change in inequality (GINI index) post-crisis vs pre-crisis<br>Romania Hungary Estonia Norway Portugal Slovak Republic Ireland Czech Republic Belgium Greece Italy Slovenia Latvia Poland Mexico Israel Chile Austria New Zealand Spain United States France Turkey Finland Canada Japan Korea, Rep. Germany Netherlands United Kingdom Lithuania Australia Denmark Switzerland Sweden Luxembourg Croatia Iceland<br><!-- End of picture text -->