rafmacalaba/datause-encoder-data huggingface dataset:
2

# **I. INTRODUCTION**

**1. Just before the crisis hit the global financial system in 2007–08, policymakers were concerned about the rapid growth of bank credit, particularly in Europe; now, the worry is about a massive credit crunch with damaging spillover effects to the rest of the world.**<sup>**1**</sup> Preceding the global crisis, massive amounts of easy credit flowed from domestic and international sources to doubtful parts of the private sector, such as risky mortgages and real estate projects, triggering credit booms and asset bubbles across the world (figures 1a and 1b). Many financial institutions were operating with small amounts of high quality capital buffers (that is, high leverage), and relied heavily on an unsustainable wholesale funding model to support the rapid growth of credit (figure 2). At the same time, growing volumes of capital flows across borders (figure 3), particularly those from European banks, created increasingly complex and interconnected financial systems, while national supervisors missed deficiencies in banks‘ ability to manage risks taken. Furthermore, effective mechanisms for cross-border supervisory coordination and burden sharing were largely absent to address the risks and failures of crossborder banking groups.

**Figure 1. Global Credit Growth**

<!-- Start of picture text --> a.Nominal Private Credit Growth in Emerging b. Nominal Private Credit Growth in Europe<br>Markets and Developing Economies and Other Advanced Economies<br>(Median) (Median)<br>40 60<br>Jan 2006–Dec 2011 Jan 2006–Dec 2011<br>35 50<br>30<br>40<br>25<br>30<br>20<br>20<br>15<br>10<br>10<br>5 0<br>0 -10<br>AFR China EAP LCR MNA ECA Developed Europe Other Developed<br>%, year-on-year %, year-on-year<br>Jan-06 May-06 Sep-06 Jan-07 May-07 Sep-07 Jan-08 May-08 Sep-08 Jan-09 May-09 Sep-09 Jan-10 May-10 Sep-10 Jan-11 May-11 Sep-11 Jan-06 May-06 Sep-06 Jan-07 May-07 Sep-07 Jan-08 May-08 Sep-08 Jan-09 May-09 Sep-09 Jan-10 May-10 Sep-10 Jan-11 May-11 Sep-11<br><!-- End of picture text --> Source: IMF, International Financial Statistics.

Note: AFR: Africa; EAP: East Asia and Pacific; LCR: Latin America and the Caribbean; MNA: Middle East and North Africa; ECA: Emerging Europe and Central Asia.

# **2. Regulators around the world responded to the crisis by launching extensive reforms to create more resilient financial systems. Banks responded by repairing their balance**

> 1 For an in-depth analysis of the global financial crisis, see, for example, Claessens, Dell‘Ariccia,  Igan, and Laeven (2010), Brunnermeier (2009), Diamond, Douglas, and Rajan (2009), and Adrias and Shin (2008).
ing, our data set includes all DPLs approved by the Board from 2004 to the first months of 2016, representing 690 operations and approximately $130 billion worth of commitments. We consider all DPLs, irrespective of the sectors they specifically claim to target. As a matter of fact, basically all DPLs include elements that either directly or indirectly can improve the business environment, and choosing between them is not straightforward. However, as we believe that a well-functioning public sector administration is an essential prerequisite to foster business, we also present results only for those DPLs targeting reforms in the public administration area.<sup>5</sup> Unlike the more traditional investment project financing, DPLs are approved under the condition that the government in the recipient country implement a set of policy reforms, called ”prior actions”. We can thus reasonably expect that the effect of a DPL on the _Doing Business_ score, if any, occurs concurrently with its Board approval. Furthermore, following Easterly (2005) and Smets and Knack (2014), we focus on the cumulative number of policy loans, to assess the impact of repeated lending to the same country. We are also interested in investigating whether the size of these loans matters. We therefore compute the cumulative value (in current dollars) committed to each country.

As control variables, we include GDP per capita and total official aid received by each country in each year from all donors. These variables, as well as the population, which we use to normalize some explanatory variables and in our instrumental variables estimation, are taken from the _World Development Indicators_ database. We also create a dummy variable capturing whether the country is under an IMF assistance program for the year that the ease of doing business is assessed. This information is taken from the IMF website.

In Table 1 we report some descriptive statistics of the main variables contained in our data set for the top 20 DPL recipients (countries that received 10 or more loans). All measures of performance, including the average _Doing Business_ score, its change, and the growth rate of per capita GDP are computed over the period starting in the year the country received its first DPL

> 5 Linkages with the _Doing Business_ score could be thought of as even tighter for those DPLs explicitly targeting private sector development. However, while about 420 DPLs, of the 690 operations in our data set, have their major sector classified as ”General Public Administration”, ”Central Government Administration”, and ”Sub-national Government Administration”, the theme ”Infrastructure services for private sector development” appears for only 68 operations. Of these, 30 are in Africa, 15 in East Asia and Pacific, 8 in Europe and Central Asia, 6 in South Asia, 5 in Latin America and Carribean and 4 in Middle-East and North Africa. In all regions the size of these DPLs varies significantly, from a few million to hundreds of millions.

6
**Table 4: Indicators of Indian Agricultural Trade**

# **4.A: Difference in UR final bound rates and MIFN** **_tariff_ rates: Number of Lines by different range groups**

|UR-TR >= 75|401|
|---|---|
|_50_=<UR-TR <75|_155_|
|25 =< UR-TR < 50|29|
|IO =< UR-TR <25|39|
|O =< UR-TR <10|4 1|
|**UJR-TR**<br>**<0**|**8**|
|**TOTAL**|**673**|

TR =MFN Tariff Rate (BCD) as announced in G.O.I., Budget 1999/2000. UR =Uruguay Round final bound rates.

- **_Notes:_ 1)** Tariff Lines at 6-digit HS or sub-groups of 6-digit HS. 2) Includes only agricultural products.

# **4.1B: Aggregate Measure of Support to Indian Agriculture (selected crops)**

||**Produc~t**<br>Spcifiq|NonwPrducdt**SIP:fiw**|**TotalAMS,**|
|---|---|---|---|
|1986|-34.29|2.25|-32.04|
|198|<br>~~-3208|326|2888|
|<br>1991|<br>.<br>-6.336-58.63|.|-.|
|1992|-69.31|3.46|_-65.85_|
|1---f9-93|<br>-54.75|3.14|-51.61|
|1994|-43.27|3.4|-39.87|
|1995|<br>~~-44.09|3.9<br>-40.|.19|
|1996|-45.8|3.62|-42.22|
|1997|-32.16|4.12|-80|
|1998|<br>~~-41.89|3.49|-38.4|

**_Source:_** Gulati (1999)

34
**<u>Figure 2.4:  Gini Index of Income Equality, 1991 and 2000</u>**

<!-- Start of picture text --> 0.680<br>0.660<br>1991 2000<br>0.640<br>0.620<br>0.600<br>0.580<br>0.560<br>0.540<br>0.520<br>Source: Census 1991 and 2000.<br>North Northeast Rio Grande do Norte Pernambuco Alagoas Sergipe Bahia Southeast South Center-West Brazil<br><!-- End of picture text -->

# **_Poverty Profile_**

Comparing average levels of poverty for different categories is useful for learning about which population groups are falling behind or catching up in terms of poverty. This is useful for the design of policies: we would like to know not only whether, for example, more- or less-educated people are more likely to be poor in Paraíba, but how the relative odds of being poor have evolved for these groups. This section traces the evolution of the P0 for various population groups during the 1990s. Appendix B presents the full profile for Brazil, the Northeast, and Paraíba during 1981-99.

The structure of poverty is clear in Paraíba: (a) blacks and mulattos are poorer than whites, (b) young households/household heads are poorer than older households/household heads, (c) the poor tend to work more in the informal sector, and (d) the poor are more engaged in agriculture than in industry and services. Furthermore, the deepest poverty is in rural areas, among the illiterate or poorly educated and young households with children. In fact, the P0 is actually rising for these groups. Under current conditions, their plight is likely to worsen because they lack opportunities and assets.

More blacks and mulattos are poorer than whites. The poverty profile for 1999 reveals a large difference in the levels of well being among different groups. The P0 reveals that about 45 percent of households headed by blacks and mulattos are extremely poor compared to 3 percent of households headed by whites. During 1988–99, poverty fell 21.9 percentage points among households headed by whites and about 16 points among those headed by blacks and mulattos.<sup>5</sup> Brazil’s racial heterogeneity does not create equal opportunities for everybody for example in education and employment. In fact,

> 5 For a detailed description of the poverty profiles for Brazil and the Northeast, including Paraíba during 1981–99, see Fiess and Verner (2001).

9
Randomized Regulation: The Impact of Minimum Quality Standards on Health Markets<sup>∗</sup> Guadalupe Bedoya<sup>†</sup> Jishnu Das<sup>‡</sup> Amy Dolinger<sup>†</sup> _JEL Codes_ : H75, I11, I18, 017 _Keywords_ : Health Care Market; Public Health; Regulatory Enforcement; Informal Sector; Kenya

> ∗Bedoya: e-mail: gbedoya@worldbank.org; Das: e-mail: jishnu.das@georgetown.edu; Dolinger: e-mail: adolinger@worldbank.org. Extended members of the Kenya Patient Safety Impact Evaluation (KePSIE) project team include Jorge Coarasa, Ana Goicoechea, Njeri Mwara, Khama Rogo, and Frank Wafula from The World Bank; John Kabanya, Charles Kandie, Mary Wangai, and representatives of the Kenya Medical Boards and Councils from the Ministry of Health; and the County Executive Members and Directors for Health of Kakamega, Kilifi, and Meru Counties. We are grateful to Sherlene Chatterji, Benjamin Daniels, Rebecca De Guttry, Thomas Escande, Seungmin Lee, Maria Camila Ayala Guerrero, Garima Sharma, Chex Yu, and Tatiana Zarate for excellent research assistance throughout the project. Rodgers Kegode, Purity Kimuru, Andrew Muriithi, Pheliciah Mwachofi, Salome Omondi, Pamela Kuya, and Leah Adero provided field support. We thank Jay Bhattacharya, Paolo Belli, Mickey Chopra, Daniel Chen, Ana Goicoechea, James Habyarimana, Arianna Legovini, Nikolas Mittag, Edit Velenyi, as well as seminar participants at UCLA, Georgetown University, Warwick University, University of Washington, University of Maryland, World Bank and the 2020 ASSA meeting for providing valuable comments. We also thank the anonymous referees for their useful feedback. Funding was provided by the DIME Impact Evaluation to Development Impact (i2i) fund, the Trade and Competitiveness Impact program (Compel), the Strategic Impact Evaluation Fund (SIEF), the Knowledge for Change Program (KCP), the Korea World Bank Group Partnership Facility (KWPF), the Development Economics Research Support Budget (RSB) and the Development Research Group (DECRG) at The World Bank. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its executive directors, or the countries they represent. The authors declare that they have no relevant or material financial interests that relate to the research described in this paper. This study was pre-registered in the AEA RCT registry with RCT ID AEARCTR-0001001. Ethical clearance was granted by the Ethics and Scientific Review Board at the African Medical and Research Foundation (Approval no. AMREF-ESRC P94/2013), the Kenyan Ministry of Health and authorities at participating facilities. Computational reproducibility verified by DIME Analytics.

> †Development Impact Evaluation (DIME) department, World Bank

> ‡Georgetown University and NBER
Table 3. Militarization and Fearon and Laitin’s measures of fractionalization, 1988–2002

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Ethfrac (Fearon & Laitin)|Military<br>Expend.<br>-2.523***|Military<br>Expend.<br>-2.545***|Military<br>Pers.<br>-0.903**|Military<br>Pers.<br>-0.940**|Arms<br>Imports<br>-3.307**|Arms<br>Imports<br>-3.311**|
||(4.15)|(4.15)|(2.46)|(2.46)|(2.41)|(2.46)|
|Relfrac (Fearon & Laitin)|0.877|0.880|-0.541|-0.559|0.611|0.647|
||(1.20)|(1.21)|(1.31)|(1.34)|(0.40)|(0.43)|
|GNI p.c. (ln)|-0.328|-0.338|0.050|0.035|-0.690*|-0.647*|
||(1.39)|(1.43)|(0.46)|(0.31)|(1.93)|(1.86)|
|Economic growth|1.293**|1.281**|0.093|0.064|-0.208|-0.097|
||(2.08)|(2.09)|(0.14)|(0.10)|(0.08)|(0.04)|
|Polity|-0.040**|-0.039**|-0.012*|-0.012*|-0.105|-0.112*|
||(2.28)|(2.25)|(1.89)|(1.79)|(1.62)|(1.72)|
|Government expenditures|0.158***|0.157***|0.028**|0.028**|0.161**|0.164***|
||(4.52)|(4.56)|(2.17)|(2.15)|(2.52)|(2.69)|
|Population (ln)|-0.034|-0.044|-0.150**|-0.164**|0.375|0.390|
||(0.30)|(0.38)|(2.35)|(2.30)|(1.49)|(1.57)|
|Contiguous militarization|0.130**|0.126**|0.568**|0.557**|0.110|0.118*|
||(2.52)|(2.48)|(2.23)|(2.17)|(1.55)|(1.72)|
|Peace years (civil war)|-0.020***|-0.020***|-0.004*|-0.004**|-0.024*|-0.024*|
||(3.74)|(3.74)|(1.81)|(2.02)|(1.84)|(1.89)|
|Peace years (intern. conflict)|0.006|0.007|-0.009|-0.009|-0.035**|-0.031**|
||(0.72)|(0.74)|(1.56)|(1.53)|(2.30)|(2.20)|
|Civil war|0.639***|0.639***|0.055|0.053|2.375**|2.391***|
||(3.24)|(3.26)|(0.77)|(0.76)|(2.55)|(2.60)|
|International conflict|0.763|0.761|0.608*|0.612*|3.232**|3.160**|
||(1.24)|(1.25)|(1.79)|(1.80)|(2.19)|(2.18)|
|Observations|1587|1587|1383|1383|1396|1396|
|Countries|131|131|138|138|139|139|

Notes: Absolute t- and z-statistics in brackets. Constant and year-specific time-dummies included, but coefficients not reported. *, **, *** significant at .1, .05 and .01 level, respectively.

23
21 production and consumption share weights when estimating the PDI and CDI, respectively.

Kee, Nicita and Olarreaga (2009) provide a methodology for estimating elasticities that could be adapted to the Agricultural Distortions project database.

# **References**

Anderson, J.E. and G. Bannister (1992), “The Trade Restrictiveness Index: An Application to Mexican Agriculture”, Policy Research Working Paper 874, World Bank, Washington DC, March.

Anderson, J.E. and J.P. Neary (2005), _Measuring the Restrictiveness of International Trade Policy_ , Cambridge MA: MIT Press.

Anderson, K. and Associates (2009), _Distortions to Agricultural Incentives: A Global_ _Perspective, 1955-2007,_ London: Palgrave Macmillan and Washington DC: World Bank (forthcoming).

Anderson, K., M. Kurzweil, W. Martin, D. Sandri and E. Valenzuela (2008), “Measuring Distortions to Agricultural Incentives, Revisited”, _World Trade Review_ 7(4): 1-30, October.

Anderson, K. and E. Valenzuela (2008), _Global Estimates of Distortions to Agricultural_ _Incentives, 1955 to 2007_ , database available at www.worldbank.org/agdistortions. Dupuit, J. (1844), “On the Measurement of Utility of Public Works”, _Annals des Ponts et Chaussees_ , 2<sup>nd</sup> series, Vol. 8.
Second, high contribution rates create disincentives for participation. Among local workers, a one percent increase in the labor tax wedge is associated with a 0.14 percent reduction in the probability of participating in the urban worker pension program. The fact that participation of migrants is unaffected at the margin is suggestive of the possibility that other factors, most likely institutional fragmentation and lack of portability, have a larger negative impact on the expected benefits from participating in social insurance programs.

Social insurance programs may be more attractive to workers if both worker and employer contribution rates can be limited, and if pension and social insurance systems can be integrated through higher levels of pooling. The main obstacle to reducing pension contribution rates is the legacy costs associated with retirees from the SOEs and government offices under the centrally planned system. If covering legacy costs can be separated from the pension programs for current workers, costs for workers and employees could be reduced which we would expect to increase participation.

Integrating the social insurance system requires overcoming problems of political economy. Moving toward national, or even provincial, levels of coordination among prefectures and cities at different levels of development is frustrated by the fact that they als0 differ significantly in their capacity to finance social insurance programs. Consolidation of budgetary management, monitoring and management and information systems used to run these programs would both lower operating costs and provide some measure of confidence that a worker will not lose contributions when moving. To this end, developing common procedures that allow workers to maintain accounts, including access to social pooling accounts, when switching employers and even moving geographically, would likely raise the perceived benefit of participating in employer-based pension, health and UI programs.

# **References**

Akay, Alpaslan, Corrado Giulietti, Juan David Robalino, Klaus F. Zimmermann. 2012. Remittances and Well-Being Among Rural-to-Urban Migrants in China, IZA Discussion Paper 6631.

Cahuc, Pierre and Fabien Postel-Vinay. 2002. “Temporary Jobs, Employment Protection and Labor Market Performance,” _Labour Economics_ 9 (2002): 63-91.

Cai, Fang, and Dewen Wang. 1999. “The Sustainability of China’s Economic Growth and Labor Contribution,” _Journal of Economic Research_ , No.10.

Cai, Fang, Dewen Wang and Yang Du. 2002. “Regional Disparity and Economic Growth in China: the Impact of Labor Market Distortions,” _China Economic Review_ , 13, pp197-212.

25
<!-- Start of picture text --> 0.12<br>0.10<br>= Full capital cost<br>S 0.08 Limited capital cost<br>a)<br>2 Operating cost<br>un<br>$s<br>2 0.06<br>v<br>><br>2<br>wv<br>i)<br>© 0.04<br>v><br><=<br>0.02<br>0.00<br>Industry and Agriculture Residential and Other Commercial<br>Mining government<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>90%<br>80% y<br>co)<br>= 70%<br>$<br>a 60%<br>a<br>‘5 50%<br>= 40% j<br>fs<br>5 30%<br>a 20%<br>10%<br>0%<br>0% 20% 40% 60% 80% 100%<br>Percent of Consumption<br>600 0.30%<br>500 0.25%<br>Q 400 0.20% &<br>S id<br>2 300 0.15% 2<br>©<br>== ov2<br>= 200 0.10% 2<br>.0 7 7 —0.00%<br>2010 2011 2012 2013 2014 2015 2016<br>lm QFD from collection losses mami QFD from T&D losses<br>Mmmm QFD from underpricing + —@®—Total QFD as% of GDP<br><!-- End of picture text -->
<!-- Start of picture text --> Middle 5<br>Upper East East oi tet High<br>ICT Soutiy Middle SSA and Soutly Asia amnerice . od Pow me wuddle Income World<br>a Africa Asia . & Central Income Income a<br>Income Noth; Pacific Caribbean. Asia. OECD<br>Africa<br>Teledensity 7 7 547 5<br>(total telephone subscribers/1000 people) 408 635 °9 292 2 12 433 4 o4 408 1393 ol<br>Mainlines Teledensity<br>(mainlines subscribers/1000. people) Cd 7 at a vs2! a5 7: o 2 22a 2ss oi 572 2<br>Cellular Teledensity 7. 7 7 2: 5 7 27<br>(cellular subscribers/1000 people) iad sab -s oI as si aa sd Soa 7 Sse<br>Households with Own Telephone 2 2 2 22<br>(% of households) aS ue bs a A 2 a a 5 “ “ os<br>Cost of Local Phone Call 5 5 7 7 7 5<br>(US cents/3 minutes) a “ ao S be ~ sd Z y<br>Cost(US cents/3of Phoneminutes)Call to the US 58 305 «497° «281.336.4803 326 «504 «309 s«d28 338<br>Cost of Cellular Local Call 25 2 52 7 2 57 57<br>(US cents/3 off-peak minutes) ~ = af ~ Z a a ao na ne<br>Commercial Perception of Telephone/Fax Infrastructure 51 56 43 58 43 52 46 53 34 53 6.6 55<br>quality (l=worst 7=best)<br>Commercial Perception i ad of Mobile or Cellular 63 60 57 59 59 57 55 60 50 59 6.7 61<br>Telephone (l=worst 7=best)<br>Commercial Perception of Internet Access in Schools 3.6 38 28 35 37 34 28 3.7 a1 34 54 40<br>(l=worst 7=best)<br>Commercial Perception of Postal Efficiency 33 44.937 #49 33 42 3.1 43 31 39 62 46<br>(l=worst 7=best)<br>Phone Faults 57 2 7 2 25 7<br>(reported faults/100 mainlines) ae ue . = d ae 2 af aS a a<br>Unmet Demand 5 2 7 7<br>(% of main telephone lines in operation) 1 4 so 26 4 13 1 8 4 2 o 71<br><!-- End of picture text --> Note: Data used isfrom the latest observation available between 1997 and 2003.

Source: Estache, Antonio and Ana Goicoechea (2005). “A ‘Research’ Database on Infrastructure Economic Performance,” Policy Research Paper No. 3643, Infrastructure Network, Office ofthe Vice President, June 2005.
infrastructure and social spending and about possible complementarities between public and private capital.<sup>1</sup> The other side is a set of cautionary tales about the absorptive capacity for extensive aid in developing countries, its incentive effects, possible Dutch disease, and macroeconomic instability, as well as serious questions about the effectiveness of aid and the marginal productivity of public investment, especially public expenditures, in education, health, and infrastructure.<sup>2</sup> Both sides of the debate are making statements about the future with scaled-up aid. It would seem that one way to resolve the narrow issue of Dutch disease or real exchange rate appreciation would be to estimate the relevant parameters empirically. Unfortunately, data problems are severe, and there has been a general lack of reliable empirical estimates of crucial relationships and parameters. Recent surveys by Adam (2006) and Radelet, Clemens, and Bhavnani (2006) conclude that the consequences of aid on Dutch disease can vary widely using available econometric estimates. Results generally depend on assumptions about the marginal productivity of additional aid and public expenditures or about the complementarities between public and private capital. Like the criticisms of growth regressions, the empirical bases of those assumptions are subject to further debate and statistical testing.

Partly because of the lack of solid empirical evidence, there is a parallel tradition of employing analytical and simulation models to assess the marginal effect of exogenous flows and shocks on the real exchange rate. The classic work on Dutch disease by Corden and Neary (1982) was quickly followed by several analyses using primarily a computable general equilibrium (CGE) framework, such as van Wijnbergen (1984), Gelb and associates (1988), and Benjamin, Devarajan, and Weiner (1989). But the analytical debate about aid from this literature is also almost always cast in a static framework, and the time dimension is at best derived from recursive dynamics. Recent work is no exception. Adam (2006) and Adam and Bevan (2004) examine the supply side effects of

> 1 See, for example, Tyrangiel, ( _Time_ , 2005), Sachs (2005), Gordon Brown (epigraph at beginning of paper), and World Bank (2007).

> 2 See, for example, Devarajan, Swaroop, and Zou (1996); Easterly (2001, 2003); Filmer, Hammer, and Pritchett (2000); and Pritchett (2001). A recent survey of the conflicting perspectives is found in Roodman (2007).

2