rafmacalaba/datause-encoder-data huggingface dataset:
month deadline for recently started ongoing cases, or may be unable to distinguish between cases started just before and just after the decree application date. This would yield some fuzziness in the effective application of the new deadline in a small window around the application date. We return to this in Section 5.

# **5 Impacts of the decree on judicial efficiency**

## **5.1 Empirical strategy**

Our empirical strategy describes variation in our main outcomes of interest (case duration, judge’s behavior in hearings, quality of pre-trial and judgment) relative to the staggered introduction of Decree n<sup>_◦_</sup> 2013-1071. Specifically, we exploit cross-sectional variation that stems from the fact that, while the decree was ratified in July/August 2013, it was applied at different times across the six civil and commercial chambers of the Regional First Instance Court of Dakar. The staggered application began in October 2013 and reached full coverage in March 2014 (Figure 2).<sup>11</sup> We use high-frequency data around the multiple decree application cutoffs and two years of pre-intervention data to identify the causal effect of the reform in a flexible event study design. If the reform had an impact on our outcomes of interest, we expect to see a structural change in those outcomes at the time of the reform’s application. For example, we should see a sharp increase in the speed of adjudication for cases entering court right around the 11A seventh chamber, the 2 _nd_ civil chamber, closed in early 2013, before the decree was published (Figure 2). It does not contribute to the event study design, for two reasons. First, we do not know when the decree would have been introduced in that chamber. Consequently, there is no straightforward way to assign its pre-reform cases an entry period relative to decree application (see event study specification below). Second, we do not know which cases would have been assigned to this chamber, had it not closed. We check that this does not affect our conclusions by verifying the nature of the caseload assigned to this chamber over our study period. One main source of worry would be that cases in the 2<sup>_nd_</sup> civil chamber had a systematically faster pre-trial than in the rest of the court in the pre-period. Hence, excluding these cases would make the pre-period artificially slow. A simple means comparison over the pre-period indicates that this is not the case, as pre-trial for cases in the 2<sup>_nd_</sup> civil chamber lasted on average 163 days compared to 157 in our study sample in the pre-period.

10
necessary for a bank to have a physical presence in all geographic areas in which it lends (Petersen and Rajan, 2000) and could help large foreign banks to overcome some of the diseconomies and difficulties in lending to small borrowers.

However, even if foreign banks continue to primarily focus on serving large customers, foreign entry might still benefit small borrowers. Competition for large customers could displace some domestic banks, forcing them to seek new market niches such as providing credit to SNIEs. Consistent with this, Bonin and Abel (2000) find that as foreign bank penetration increased in Hungary some smaller domestic banks sought new market areas. Similarly, in a survey of banks from 78 countries, Jenkins (2000) finds that, 44 percent of those banks that lent to small and rmicro enterprises indicated that changed market conditions and increased competition in lending to large 7 and medium-sized enterprises were the two most important reasons for doing so.

As illustrated in the discussion above, studies that have focused on foreign versus domestic bank behavior (using individual bank balance sheet data) have not provided a defini.tive answer on the net effect of foreign bank entry on access to credit for small and medium-sized enterprises. Isolating the effect of foreign bank entry on domestic banks' lending from macroeconomic changes can be difficult in country case studies, especially since relatively few commercial banks operate in many developing countries.8 Furthermore, comparable crosscountry data on lending to SMEs is not easily available, especially since small local banks tend to be important in this area of lending.<sup>9</sup> Rather than relying upon information from bank balance sheets, this paper uses responses from a survey of over 4,000 enterprises in 38 developing and transition economies, to study whether borrowers' perceptions regarding interest rates and access to long-term credit are _5_
Pitt and Khandker (1998) also took aim at selection bias in an observational (i.e., not randomized) study. They used a World Bank survey of 1,798 member and non-member households of three Bangladeshi MFIs. The program rules stated that members should hold no more than one-half acre of land in order to be eligible for the program. With the half-acre rule providing the key to their identification strategy, they find very large microfinance impacts: notably, every 100 taka lent to a female borrower was estimated to increase household consumption by 18 taka. The result was widely cited, but it has not proved robust to re-analysis. Morduch (1998) shows that the half-acre rule was not followed closely in practice, and a simpler difference-in-difference model shows little evidence for increased consumption. A re-analysis by Roodman and Morduch (2014) turns up other problems with the original (see Pitt’s rejoinder in the same journal), suggesting the need to look elsewhere for credible evidence.

These concerns led researchers to turn to randomized controlled trials in which access to microfinance depends in part on a randomization process. Usually, lenders select some communities (treatment) and not others (control) using a random number generator. Researchers then compare the outcomes in treatment and control communities after a few years.

The best known so far are six studies published in _American Economic Journal: Applied Economics_ 2014 (the studies are from India, Ethiopia, Bosnia-Herzegovina, Mexico, Morocco, and Mongolia). As a group, the papers show strong increases in borrowing but modest impacts of microfinance.

The studies show the power of randomization together with its limits. To give an example, the paper from India investigates an urban microcredit program in South India. Banerjee et al. (2015a) found that small business investment and profits of existing businesses increased, but not average consumption by households. No significant impacts were found on 23
<mark>WPS3437</mark>

# **Bank Capital and Loan Loss Reserves under Basel II:**

# **Implications for Emerging Countries**

Giovanni Majnoni, Margaret Miller, and Andrew Powell*

## **Abstract**

This paper proposes an integrated approach to minimum bank capital and loan loss reserves regulation. The paper breaks new ground in two main areas. In the first place it provides an explicit measurement of the credit loss distribution for a sample of emerging countries providing a benchmark for discussing the appropriate calibration of new regulatory capital and loan loss provision requirements for non-G10 countries. Second, on normative grounds, it proposes a simplified version of the “internal rating based” (IRB) approach as a transition tool that, while retaining a risk-based definition of solvency ratios, implies reduced supervisory monitoring costs and could therefore be of interest to emerging countries where supervisory resources are particularly scarce.

JEL Classification: G21, G28 Keywords: Bank regulation, solvency ratios, loan loss reserves, emerging economies.

## World Bank Policy Research Working Paper 3437, October 2004

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ __________________ * The authors are from the World Bank (Giovanni Majnoni and Margaret Miller) and from the Universidad Torcuato Di Tella (Andrew Powell). This paper has been completed as part of a project on “Credit Information, Credit Risk Measurement, and Solvency Ratios in Emerging Countries” supported by a grant from the World Bank Research Committee. Andrew Powell gratefully acknowledges the World Bank, where he visited between August and December 2003, for financial support. Email: <u>gmajnoni@worldbank.org</u> , <u>mmiller5@worldbank.org, apowell@utdt.edu. We are grateful to Matias</u> Gutierrez from the Banco Central de la Republica Argentina, Jose Luis Negrin, Javier Marquez, and Alberto Romero from Banco de Mexico, and Ricardo Schectman from Banco Central do Brazil for invaluable help and support in the quantitative analysis. We thank Jerry Caprio and Patrick Honohan for comments on an earlier draft.
from potential technology spillovers. The survey data reports four different types of premises, namely household, non-household with a permanent structure, non-household with a temporary structure, and non-fixed premises. The statistics are summarized in Table 6. Around 40% of informal businesses operate within their household premises. An additional 27% are located in other permanent structures, whereas around 30% have either no premises or some kind of temporary structure.

**Table 6: Summary of Type of Premises: Informal Businesses**

|Type of Premises|Obs|Percent|
|---|---|---|
|Household|415|41.05|
|Non-household with permanent structure|274|27.1|
|Non-household with temporary structure|270|26.71|
|Non-fixedpremises,includinghawkers|52|5.14|
|Total|1,011|100|

Table 7 shows that the effects of distance on digital technology adoption remain negative and statistically significant when the sample is restricted to informal businesses with permanent structures.

**Table 7: Effects of Distance for the Permanent Location Sample**

||(1)<br>PC|(2)<br>Tablet|(3)<br>Phone|(4)<br>MM|(5)<br>Website|
|---|---|---|---|---|---|
|Distance of Informal to Closest Formal Firm|-0.840***|-0.691***|-0.194**|-0.172***|-0.387|
||(0.141)|(0.201)|(0.093)|(0.037)|(0.440)|
|Num. of People Who Worked Last Month|0.404***|0.273|0.068|-0.057**|0.391***|
||(0.028)|(0.287)|(0.071)|(0.029)|(0.121)|
|Years in Operation|-0.031|-0.039|0.009|0.013|0.014|
||(0.021)|(0.040)|(0.022)|(0.023)|(0.022)|
|Age of Main Owner|-0.018|0.024|0.002|-0.005|-0.053*|
||(0.017)|(0.022)|(0.010)|(0.003)|(0.031)|
|Owner's Education (1=above secondary)|1.672***|0.163|0.870***|0.072|1.645***|
||(0.217)|(0.986)|(0.050)|(0.252)|(0.527)|
|Prior Experience in Same Type of Business|0.637*|1.080|0.377|0.362|0.136|
||(0.383)|(0.739)|(0.608)|(0.664)|(0.265)|
|Sector Dummy (1=manuf, 0=services (inc. retail))|-0.876|-1.097**|-0.363|-0.260|0.359|
||(0.736)|(0.433)|(0.652)|(0.729)|(0.280)|
|Number of Observations|606|606|606|600|435|

_Notes:_ MM denotes mobile money. The “Permanent Location” sample includes firms operating on household premises or nonhouseholds with permanent structures. See Table 6 for an overview of the types of premises. Sampling weights applied. City fixed effects are not shown. Standard errors are clustered at the city level. *** p<0.01, ** p<0.05, * p<0.1.

14
- [7] Butler, D., Lahiri, A., and Roy, P. [1995], India<sup>Decides: Elections 1952-1995,</sup> New Delhi.

- [8] Dua, B. D. [1979], "Presidential Rule in India:<sup>A Study in Crisis Politics", Asian</sup> Survey, 19, 611-628.

- **[9]** Evenson, R. E., Pray, C. E. and Rosegrant, M. W.<sup>[1994], "Sources of Agricultural</sup> Productivity Growth in India", IFPRI Research Report.

- [10] Golden, D. G. and Poterba, J. M. [1980], "The Price<sup>of Popularity: The Political</sup> Business Cycle Re-examined", American Journal of Political<sup>Science, 24, 696-714.</sup>

- [11] Grossman, G. and Helpman, E. [1996] "Electoral Competition<sup>and Special Inter-</sup> est Politics", Review of Economic Studies, 63:265-286.

- [12] Hardgrave, R. L. [1980], India: Government and Politics<sup>in a Developing Nation,</sup> Harcourt Brace Jovanovich, Inc.

- [13] Holmstrom, B. [1982], "Managerial Incentive Problems:<sup>A Dynamic Perspective",</sup> in Essays in Economics and Management in Honor of<sup>Lars Wahlbeck, Swedish</sup> School of Economics, Helsinki.

- [14] Hibbs, D. [1977], "Political Parties and Macroeconomic<sup>Policy", The American</sup> Political Science Review, 7, 1467-1487.

- [15] Infrastructure in India [1998], Centre for Monitoring<sup>the Indian Economy [CMIE].</sup>

- [16] Khemani, S. [1999], "Partisan Politics and Intergovernmental<sup>TRansfers in India",</sup> mimeo, The World Bank.

- [17] Lindbeck, A. [1976], "Stabilization Policies in Open<sup>Economies with Endogenous</sup> Politicians", American Economic Review Papers and Proceedings,<sup>1-19.</sup>

35
# **I. Introduction**

The sharp growth in the global count of forcibly displaced people during the past decade has created new challenges for host governments and aid organizations that will require a new approach to the measurement of poverty.<sup>1</sup> Host governments are keen to know the number and status of refugees living in their countries, as they struggle to maintain internal order while assisting the newcomers. Humanitarian organizations charged with managing displacement crises are confronted with increasing financial needs and, when these needs are not met by donors, with budget cuts and a shift from universal to means-tested targeting. The increasingly protracted nature of displacement also challenges development organizations to design sustainable poverty reduction programs for displaced people and host communities. For all these actors, measuring poverty among displaced populations has become a key ingredient of any effective economic policy. It also becomes increasingly clear that achieving the SDGs (Sustainable Development Goals) number one goal of poverty reduction will not be possible if the forcibly displaced are excluded from the count.

This is not an easy task. Measuring poverty among refugees is more complex than for regular populations because refugees are mobile. They also live in areas that are often difficult to reach due to environmental or security barriers. Indeed, the global count of the poor excludes, for the most part, displaced populations because these populations are not usually captured by censuses and, as a consequence, are largely excluded from consumption surveys, the main instruments used to measure poverty. The various challenges related to micro survey data collection, such as survey administration, sampling, and questionnaire design or funding, are exasperated for displaced populations and will require years of efforts to meet the poverty measurement standards that we

> 1 The UNHCR estimates that the number of forcibly displaced people at the end of 2018 was 71.4 million, the largest number since the beginning of records in 1951.

2
# (e) Does Stronger State Presence in Markets Limit Better Market Outcomes?

**Given the unique position of SOEs across many markets, their presence could limit competition and better market outcomes** . This is especially so when  no clear rationale for SOE operations based on economic efficiency exists. This section presents the results relating to the contribution of Romanian SOEs to aggregate productivity, determining how their presence in markets correlates with firm- and market-level measures of allocative efficiency. This section also presents the main effects of  SOEs in markets on overall and private sector or market outcomes, including business dynamism, competition, and allocative efficiency. Except for the firm-level allocative efficiency analysis, all analyses are performed at the 2-digit NACE level.<sup>9</sup>

## _SOE Presence, Likelihood of Exiting the Market, and Static Allocative Efficiency_

**Romanian SOEs were less likely to exit the market than their POEs peers** . This is irrespective of whether they are majority-owned or minority-owned, directly owned or indirectly owned, and centrally owned or locally owned (Table 7, column 1). That SOEs are less likely to exit the market is not surprising given the privileges and protections they might enjoy. They are sometimes protected from exposure to market discipline and operate in an environment that may not be competitively neutral (World Bank 2020, OECD 2023).

Table 7: Conditional Correlations Between State Ownership and Firm Exit in Romania, 2011-2019

||Probability of firm exiting a<br>sector|Firm-level measure of<br>allocative efficiency|
|---|---|---|
|**State ownership**|||
|_Panel A:_|||
|10-100%|-0.023<sup>***</sup><br>(0.003)|-0.029**<br>(0.013)|
|_Panel B:_|||
|10-24.9%|-0.015<sup>***</sup>|0.098|
||(0.003)|(0.060)|
|25-49.9%|-0.024<sup>***</sup>|0.034|
||(0.004)|(0.027)|
|50% or more|-0.024<sup>***</sup>|-0.048<sup>***</sup>|
||(0.003)|(0.014)|
|_Panel C:_|||
|Direct|-0.024<sup>***</sup><br>(0.003)|-0.044<sup>***</sup><br>(0.014)|
|Indirect|-0.015<sup>***</sup>|0.061<sup>*</sup>|
||(0.002)|(0.037)|
|_Panel D:_|||
|Central|-0.012<sup>***</sup>|0.011|
||(0.003)|(0.025)|
|Local|-0.027<sup>***</sup>|-0.046<sup>***</sup>|
||(0.003)|(0.016)|
|Age dummies|Yes|Yes|
|Size dummies|Yes|Yes|
|Industry effects|Yes|Yes|
|Year effects|Yes|Yes|
|Observations<sup>**†**</sup>|4,173,917|4,027,292|
|Within R-squared<sup>**†**</sup>|0.004|0.061|

> 9 For robustness checks, the analyses will be replicated at the 4-digit NACE level.

26
3 1985-1999 period.<sup>3</sup> It has also been argued that this episode of trade liberalization had a substantial impact on labor and total factor productivity growth, with the latter increasing by 6 percentage points in annual rate terms (Ferreira and Rossi, 2003).

During this period Brazilian inequality, which had been rising until 1989, started a gradual but persistent decline. Figure 2 shows the long-term evolution of two commonlyused inequality measures, the Gini and the Theil indices, between 1981-2004, for household income per capita. The bands around the point estimates denote the 95% bootstrapped confidence intervals. The Figure highlights the trade-liberalization period of 1988-1995, during which inequality briefly rose (for one year) and then began to fall. The Theil index fell from 0.75 to 0.71 and the Gini fell by almost two points from 0.61 to 0.59 over this seven-year interval. Both declines are statistically significant at the 1% level. As shown in Figure 3, inequality also fell in the distribution of hourly wages: the Gini fell by almost three points, from 0.61 to 0.58 and the Theil fell from 0.78 to 0.72. Both declines are significant at the 5% level. The economy-wide skill-premium (defined as the ratio of the wages of skilled workers to those of unskilled workers) fell by 14.3% (see Figure 4).<sup>4</sup> Looking only at the skill premium in manufacturing, Gonzaga et al. (2006) find a similar (15.5%) decline between 1988 and 1995.

Were these two phenomena linked? Did trade liberalization (and other aspects of increased openness which took place alongside it) cause at least part of the contemporaneous decline in Brazilian inequality? The literature is somewhat inconclusive. Focusing on the specific channels of industry wage premia (and industryspecific skill premia) Pavcnik et al. (2004) find no evidence of any effect from trade liberalization on the Brazilian wage distribution. More recently, Gonzaga et al. (2006) argue persuasively that, through the more general channel of changes in the economywide skill premium (as opposed to industry-specific premia), trade liberalization did

> 3 The data reported in Table 1 weigh tariff rates by lagged industry imports. An alternative weighting scheme (by industry value-added) generates an even more pronounced decline: citing data from Kume et al. (2000), Abreu (2004) reports a decline in nominal tariffs from 54.9% in 1987 to 10.8% in 1995. Effective rates of protection fell from 67.8% to 10.4% in the same period.

> 4 We use an education-based definition of skill: skilled workers have 11 or more years of schooling; and unskilled workers are those with ten or fewer. We return to a discussion of this definition and alternatives below.
- 33 -

# BIBLIOGRAPHY

- AGARWALA, R., 1983, Price Distortions and Growth in Developing Countries, World Bank Staff Working Papers, no. 575, The World Bank, Washington.

- AHMAD, S. and KWON, S., 1981, "IMF and IBRD Data on,National Accounts. Sources of Discrepancies," Division Working Paper no. 1981-5, EPD, The World Bank, Washington D.C.

- AZAM, J.P., 1986, "Examer de quelques problemes econometriques souleves par la methode d'analyse des strategies," in CERDI 1986.

- BALASSA, B., 1985, "Exports, Policy Choices, and Economic Growth in Developing Countries after the 1973 Oil Shock," Journal of Development Economics, 18, , 23-25.

- BANETH, J., 1986, "Methodes d'analyse et resultats generaux en matiere de croissance: commentaires," in CERDI, 1986.

- BARNETT, V. and LEWIS, T., 1978, Outliers in Statistical Data, Chichester, Wiley.

- BORNSHIER, V. and HEINTZ, P., 1979, Composition of Data for World-System Analysis, Soziologisches Institut der Universitat Zurich, Bulletin (ed.), March 1979.

- BREUSH, T.S. and PAGAN, A., 1979, "A Simple Test for Heteroscedasticity and Random Coefficient Variation", Econometrica, 47, 1287-1294.

- CERDI, 1986, Strategies de developpement comparees (zone franc et hors zone franc) Studies coordinated by P. and S. Guillaumont, Economica, to be published in 1987.

- CHARPIN, F., 1987, "Les defauts de comptes a prix constants," Observations et diagnostics economigues, no. 20, July 1987, p. 189-207.

- CHENERY, H.B., 1986, "Growth and Transformation," in Chenery, H.B, Robinson, S, and Syrquin, M, Industrialization and Growth: A Comparative Study, A World Bank Research Publication, Oxford University Press, 1986.

- CHOW, G.C., 1960, "Tests of Equality between Subsets of Coefficients in Two Linear Regressions," Econometrica, , 591-605.

- COOK, R.D., and WEISBERG, S. 1982, Residuals and Influence in Regressions, New York, Chapman and Hall.
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