rafmacalaba/datause-encoder-data huggingface dataset:
,11 TABLE 6 The Impact of the Cost of former Defaults

|Constant|-2.601|
|---|---|
||(-4.729)|
|Svndicate|-0.032<br>(-0.355)|
|Public|-0.093<br>(-1.550)|
|Total Debt/GNP|0.563|
||(2.005)|
|Reserves/GNP|-0.051|
||(-0.463)|
|Debt Service/exports|-0.021|
||(0.067)|
|Maturity|0.112|
||(11.201)|
|Investment/GNP|-6.051|
||(-2.372)|
|GNP Growth|-1.272|
||(-2.312)|
|Imports/GNP|-2.533|
||(-3.725)|
|Inflation|1.42?|
||(2.410)|
|Devaluation|-0.011|
||(-0.366)|
|Cost|0.447|
||(3.912)|
|Nobs - 158<br>R<br>0.64||

# Notes:

Estimated equation is (2). where D is replaced **by** a measure of cost of default. The numbers in the parentheses are 't' values
values of 1 for countries in the region and 0 otherwise). Equation (4) is estimated to determine whether indeed as reported in Section 3 the non-structural measures of competition for MENA are different and, in particular, are worse than for all other regions. In other words, the purpose of estimating equation (4) is to test whether the MENA dummy is negative and significant, suggesting that the value of the H-statistic is lower among countries in MENA.

Second, we estimate equation (5) incorporating potential determinants of competition. The purpose of estimating equation (5) is to assess the factors that help explain the differences in competition between MENA and other regions. If once we control for the additional variables in equation (5), the MENA dummy is no longer significant, we can establish the factors driving differences in competition by looking at the variables that are statistically significant.

_H-statistici =_  _0 +_  _1MENAi +_  _2Concentrationi +_  _3Contestabilityi +_  _4Credit information environmenti +_  _5 Non-bank financial institutionsi + ei_ (5) Following Claessens and Laeven (2004) and Turk-Ariss (2009), we consider a number of factors as potential determinants of competition. First, we include a measure of concentration. According to the Structure-Conduct-Performance paradigm, we would expect a negative association between concentration and competition. In equation (5) _Concentration_ refers to the average share of assets held by the top three banks over the period 2002-2008. We use data from Bankscope to compute the aforementioned variable. The second column of Table 7 shows the average concentration ratios across regions. The average concentration ratio for MENA (57.8%) is higher than that of South Asia, East Asia, and Latin America, but it is lower than that of Former Soviet Union, Eastern Europe, and Sub-Saharan Africa. Hence, the examination of the concentration ratios reveals that while concentration in MENA is high, the ratios are line and, in many cases, are lower than what we observe in other regions.

Second, we consider as potential determinants of competition a number of variables that help characterize the degree to which the banking sector is contestable (i.e., it has low barriers to entry and exit).We expect more contestable markets to be more competitive since the threat of entry and exit reduces the ability of incumbents to exercise market power (see Claessens and 9
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The 1985-1994 Global Real Estate Cycle. Page 13 Figure 6: Domestic and Foreign Financial Market Liberalization in Finland, 1980-1992 <!-- Start of picture text --> RELAXATION OF LENDING RATE REGULATION PASS-THROUGH FORMLA<br>EOUAL ENTRY FOR FOREIGN BANIS TO THE CALL MONEY MAR1TT<br>D CALL MONEY DEPOSIT RATE SEPARATED FROM THE CREOIT RATE<br>O ABOLTION OF REGCLA1ION OF LENIN RATEES<br>M ITING RATES ALLOVWD ON SOME LOANS<br>E CO'S OEMPT FROM RESERVE REOLREMENT<br>S OPEN MARKET OPERATIONS NTROOUCED<br>T FIL1OR RATES INTROOUED<br>I CREDIT OJoIOBNES OISCONTINLIED<br>c FL,OATING PA-TES ALWED ON ALL IOAN<br>_P0E RATES ALLOWED AS REFPR E RATES<br>13980 198311984119851 198 19_ 7 198 1989 1990 1991 1992<br>F |fE HORT-TERM CAPITAL MOVEMENTS<br>O FREE NOU5tHOtD FOREIGN INVESTMENT<br>R F,%HOUSEHOLDS<br>E E F2:E; ONCSeff~~~~~~~~hOATLALIMUCOFOR S"EMOS<br>FREE DIRECT INVESTMENT ABROAD FOR NONFINANCLAL COMPAMES<br>FREE LONG-TERI FOREIGN BORROVING FOR OTHER COIPAMES<br>FREE LoNG-TERm FoREGN soUAowwNG OR MUAC - AND S4PPING COMAMES<br>NI<br>UMITED CURFENCY OPTIONS ALLOWED TO AVrHCtZE BANKS<br>OMNKS FREE TO COVER COMMERCAL FORWARO POSIONS<br><!-- End of picture text -->

Source: Nyberg et al. f19941
Malawi's agricultural export earnings<sup>became less diversified according to nearly all the</sup> measures, and export performance<sup>as measured by both growth and stability was less satisfactory</sup> during 1974-1987 than 1961-1973.<sup>However, compared to Tanzania and Zimbabwe, Malawi was</sup> able to achieve a modest level of<sup>export earnings growth despite an increasingly unfavorable</sup> international price environment.<sup>Increased production and exports of commodities counteracted</sup> downward price trends.

According to most of the measures<sup>of concentration, Zimbabwe's agricultural export earnings</sup> became more diversified and the stability<sup>of export earnings consequently increased. At the same</sup> time, export earnings began to decline.<sup>The tradeoff between growth and stability manifested itself</sup> here, since the portfolio variance of<sup>export earnings declined even as the international price matrix</sup> exhibited more instability. In terms<sup>of the structure of exports, both Malawi and Zimbabwe became</sup> less diversified as their dependence<sup>on agricultural export earnings increased, though Zimbabwe's</sup> share of agricultural exports in total<sup>value exported was less than one-half that of Malawi's (42.8%</sup> compared to 91.1%). Tbis provides<sup>an indication that, despite the reasonable performance of their</sup> agricultural export sectors, the structure<sup>of the economy remains highly oriented toward agriculture.</sup> Tanzania's agricultural export earnings<sup>became less diversified, and instability increased. In</sup> contrast to Malawi, this increase in instability<sup>was not accompanied by export earnings growth.</sup> Domestic production stagnated and<sup>declined for many commodities, and some traditional exports were</sup> diverted to domestic markets.

Citing the example of Kenya, which<sup>successfully filled a market niche for cut flowers, there</sup> have been numerous feasibility studies<sup>and proposals for export diversification for Malawi, Tanzania</sup> and Zimbabwe. Exports such as flowers,<sup>strawberries, citrus fruits, exotic horticultural crops and</sup> livestock-based products have been considered.<sup>These market niche enterprises tend to require high</sup> 35
-8-

Table 3

# SECTOR AND RATING OF INSTITUTIONAL ANALYSIS

# IN SAMPLE PROJECTS

(in percent)

|SectorIOferation|Weak|Adequate|Good|Total|
|---|---|---|---|---|
|Agriculture|35|29|35|100|
|Human Resources|23|23|56|100|
|Infrastructure/Energy|23|23|55|100|
|Trade/Finance/Industry|29|29|43|100|
|SECALs|67|17|17|100|
|SALs|73|9|18|100|
|TALs|27|27|45|100|
|TOTAL|33|24|43|100|

constraints on the projects' viability.<sup>12</sup> Given these weaknesses, it is not surprising that in general country-specific sector ID strategies are not articulated in recent projects.13 (3) The Bank's upstream investment in economic and sector work can be a powerful aid to the quality and relevance of the institutional analysis and ID design work in projects. The usefulness of country economic work (CEW) for project level institutional analysis will depend (a) on the extent to which the analysis takes explicit account of institutional problems; (b) on whether an assessment of the institutional capacity to implement current policies exists in the country; and (c) on the degree to which institutional inadequacies have been diagnosed and directions for reform identified. The major conclusion of a recent review of a sample of 45 CEW reports is that the contribution of CEW to project level institutional analysis is modest.14 Its finding that only 5 out of the 45 reports (11%) met all the criteria mentioned above is disturbing.

**12** A critical and in-depth review of institutional analysis in ten Bank Projects is summarized in Appendix 1. For further details, see Samuel Paul, Institutional Analysis in World Bank Projects, CECPS, 1989.

13 This finding is based on an analysis of the 20 percent sample of projects approved in FY89.

**14** Henry Laurant, Review of ID Aspects of Economic Growth, CECPS (mimeo), 1989.
- 33 -

# V. Conclusion

In order to formulate effective policies to reduce poverty in developing countries, the nature and causes of poverty must be well understood. This requires data from these countries which are relevent, detailed, accurate and up to date. The LSMS program was developed at the World Bank to meet this need. The surveys undertaken so far have provided a wealth of information on poverty in developing countries and have been used in a number of studies which examine the impact of various government policies on the poor. Much more can be done with the existing data, and as more countries undertake LSMS surveys a better understanding of poverty in developing countries will emerge.

Of course, there is room for improvement in the design and implementation of LSMS surveys, and new techniques will continue to be explored both in terms of data collection and research. Many lessons can he learned from the experience of other household survey programs, such as the United Nations Household Survey Capability Program '(IUNHSCP) and household survey activities in the developed countries. At the same time this experience must be conveyed to the statistical and research organizations in developing countries so that their participation in this process can be increased.

When Robert MacNamara declared in 1973 that the World Bank would direct its attention toward improving the plight of the poor in developing countries, it was not always clear how this could be done, and indeed some skeptics may have honestly thought that very little could be done. The development of the LSMS program at the World Bank constitutes on important
- (i) industries yielding an unusual positive coefficient for the distance variable: Food, Beverages and Tobacco (31); Chemicals and chemical Products, Petroleum, Coal, Rubber and Plastic Products (35), Basic Metal industries (37); and Fabricated Metal Products, Machinery and Equipment (38);

- (ii) industries yielding the expected negative coefficient for the distance variable, a negative and significant coefficient for the localization economies proxy, and a positive and significant coefficient for the urbanization economies proxies: Non-Metallic Mineral Products (36).

- (iii) industries yielding the expected negative coefficient for the distance variable and a positive and significant coefficient for the localization economies proxy: Textile, Wearing Apparel and Leather industries (32); Wood and Wood products, including Furniture (33); and Paper and Paper Products, Printing and Publishing (34);

Table 3: Specifications using initial non-adjusted database

|Sector|Ln(ACCESSr<br>)|Ln(NPs,r,t<br>)|Ln(Nr,t<br>)|Ln(URBANr,t<br>)|R<br>2<br>|P-value|Obs.|
|---|---|---|---|---|---|---|---|
|All|-0.011<br>c<br>|0.085<br>a<br>|0.105<br>a<br>||0.367|0.000|5217|
||-0.013<br>b<br>|0.173<br>a<br>||0.170<br>a<br>|0.360|0.000|5217|
|Food & beverages|0.008|0.080<br>c<br>|0.127<br>a<br>||0.388|0.000|1176|
||0.018<br>b<br>|0.242<br>a<br>||0.204<br>a<br>|0.378|0.000|1176|
|Textile|-0.051<br>a<br>|0.190<br>a<br>|-0.009||0.273|0.000|721|
||-0.069<br>a<br>|0.246<br>a<br>||-0.485<br>a<br>|0.285|0.000|721|
|Wood products|-0.029<br>c<br>|0.256<br>a<br>|-0.078<br>a<br>||0.304|0.000|481|
||-0.046<br>a<br>|0.228<br>a<br>||-0.373<br>a<br>|0.306|0.000|481|
|Paper & printing|-0.077<br>a<br>|0.214<br>a<br>|-0.127<br>c<br>||0.267|0.000|299|
||-0.116<br>a<br>|0.285<br>a<br>||-1.021<br>a<br>|0.345|0.000|299|
|Chemicals|0.015|0.415<br>a<br>|-0.027||0.291|0.000|531|
||0.081<br>a<br>|0.168<br>a<br>||1.433<br>a<br>|0.347|0.000|531|
|Minerals|-0.031<br>a<br>|-0.546<br>a<br>|0.358<br>a<br>||0.361|0.000|893|
||-0.039<br>a<br>|-0.261<br>a<br>||1.061<br>a<br>|0.293|0.000|893|
|Basic metals|0.113<br>a<br>|0.158<br>a<br>|0.144<br>a<br>||0.480|0.000|401|
||0.058<br>a<br>|0.348<br>a<br>||-0.356<br>a<br>|0.475|0.000|401|
|Fabricated metals|0.038<br>a<br>|0.237<br>a<br>|0.075<br>c<br>||0.624|0.000|715|
||0.028<br>b<br>|0.353<br>a<br>||-0.346<br>a<br>|0.627|0.000|715|

> <sup>a</sup> significant at 1%;<sup>b</sup> significant at 5%;<sup>c</sup> significant at 10%.

13
**Weak regulatory framework for financial agencies and business disputes resolution underscored the strong demand for financial inclusion reform in OIC countries.** The statistics show that OIC countries fell behind other developing countries in terms of regulatory supervision coverage. For example, only 59% of microfinance institutions are under regulation compared with the average level of 72% in low income countries (Table ). Current laws are also insufficient to provide enough protection for disputes resolution. Take the instance of client confidentiality protection, only 68% of OIC countries provide laws to restrict such unfair treatment, while 78% of developing countries do (See Figure 11).

**Figure 11. Disputes resolution: provisions in existing laws and regulations to restrict unfair treatment**

<!-- Start of picture text --> 78% 76%<br>70% 68% 71%<br>51% 50%52%<br>42%<br>38%<br>35%<br>32% 31%<br>27% 26% 24% 24%<br>21% 21%<br>16%<br>Deceptive Unfair or high‐ Abusive Breach of client No fair treat‐<br>advertising pressure selling collection confidentiality ment provi‐sions<br>practices practices<br>OIC countries Developing countries OIC‐GCC low‐income countries<br><!-- End of picture text --> Source: CGAP (2010)

**However, the statistics also shows that OIC countries have been aware of the issue and taking actions more actively than other developing countries.** According to the CGAP survey, OIC governments have been more actively promoting reforms in all aspects of financial inclusion, especially in fields of financial consumer protection and microfinance expansion. The proportions of countries taking reforms on financial capability (38%) and SME financial access (49%) are also higher than those of developing countries (37% and 43%), much higher than lowincome countries (28% and 45%). What is surprising is that non-GCC governments are even more active in financial inclusion reforms than GCC governments, especially in microfinance development (62% for OIC-GCC vs. 57% for OIC) (See Figure 12).

36
<!-- Start of picture text --> Figure 8. Progressivity and Redistributive Effect of Taxes<br>Figure A. Direct Taxes<br>0.7 0.04<br>0.6 0.03<br>0.5 0.03<br>0.4 0.02<br>0.3 0.02<br>0.2 0.01<br>0.1 0.01<br>0 0.00<br>Kakwani Marginal contribution<br>Figure B. Indirect Taxes<br>0.05 0.005<br>0.000<br>0<br>-0.005<br>-0.05<br>-0.010<br>-0.1 -0.015<br>-0.020<br>-0.15<br>-0.025<br>-0.2<br>-0.030<br>-0.25 -0.035<br>Kakwani Marginal contribution<br>(change in Gini points)<br>Kakwani Index<br>Marginal contribution to equity<br>Regressive <------------------> Progressive Guatemala (2010) Colombia (2014) Sri Lanka (2010) El Salvador (2011) Peru (2009) Jordan (2010) Russia (2010) Romania (2016) Chile (2013) Poland (2014) Mexico (2010) Brazil (2009) Armenia (2011) Georgia (2013) Croatia (2014) South Africa (2010)<br>Kakwani Index (change in Gini points)<br>Marginal contribution to equity<br>Regressive <------------------> Progressive<br>Croatia (2014) Georgia (2013) Poland (2014) Bolivia (2009) Romania (2016) Guatemala (2010) Russia (2010) Indonesia (2012) Colombia (2014) Jordan (2010) Brazil (2009) Armenia (2011) El Salvador (2011) Sri Lanka (2010) South Africa (2010) Chile (2013) Mexico (2010)<br><!-- End of picture text --> Source: Armenia: Younger & Khachatryan (2017); Bolivia: Paz Arauco et al (2014); Brazil: Higgins & Pereira (2014); Chile:Martinez Aguilar et al (2017); Colombia: Melendez and Martinez (2015); El Salvador: Beneke et al., (2014); Georgia: Cancho & Bondarenko (2017); Guatemala: Cabrera and Moran (2015); Indonesia: Jellema et al (2017); Jordan: Alam et al (2017); Mexico: Scott (2014); Peru: Jaramillo (2013); Poland: Goraus & Inchauste (2016); Russia: Lopez-Calvo et al (2017); Sri Lanka: Arunatilake et al (2017); South Africa: Inchauste et al (2017); Uruguay: Bucheli et al (2014); Croatia: Inchauste and Rubil (2017); Romania: own estimates using 2016 HBS. Note: Old-age pensions are treated as deferred income for all countries shown. Marginal contributions are the <u>difference between the consumable income Gini coefficient with and without indirect taxes.</u> 19
variables: Firms with higher firm-wide productivity confront a lower threshold for the attribute that guarantees positive profitability from the provision of a given product and, hence, are able to supply a wider range of products to the market. Idiosyncratic distortions interfere with this decision. Firms that are taxed at a positive rate would find it harder to supply products to the market, while firms that are subsidized will find it easier. This mechanism of transmission of misallocation frictions to aggregate productivity is new to the literature and is one of the forces that we are interested in characterizing quantitatively.

# **2.4 Distribution and Evolution of Idiosyncratic Productivity**

Firms are endowed, upon entry, with a realization of firm-wide productivity and a continuum of attributes for each of the in the unit mea- product product types sure. We assume that entrants start off at a firm-wide productivity level consistent with the ratio of the average size of entrants to incumbents in the US data, and then fan out over time according to a stochastic process. Attributes for each product, on the other hand, are drawn independently and identically from a known distribution _F_ � _e_<sup>_λ_�</sup> . Unlike firm-wide productivity, we assume that the product attribute is constant throughout the life-cycle of the firm.

Firm dynamics are driven by the stochastic process for firm-wide productivity. I parameterize such process taking a discrete-time random walk approximation to a Brownian motion with drift _μ_ and variance _σ_<sup>2</sup> . Following Stokey (2008), I assume that given current log-productivity _ω_ , next period’s log-productivity could give an upward jump of size _h_ , with probability _α_ , or a downward jump also of size _h_ , with 1 _− α_ probability ( ) . The discrete time approximation of the drift and variance of the process is The appeal of the discrete time random walk approximation of the Brownian motion is that it easily maps into the model of Luttmer (2007), who shows that the resulting stationary distribution from such a stochastic process displays a right-tail 10
Based on data for 2019 or the latest year available g <!-- Start of picture text --> Non-FCS country<br><!-- End of picture text --> <!-- Start of picture text --> FCS country<br><!-- End of picture text --> <!-- Start of picture text --> fes || FCS country i Non-FCS country || NA<br>) 25 50 75 100<br>SPI Score<br>Source: World Bank. Statistical Performance Indicators. Non-FCS<br>countries include all countries not classified as FCS. This includes<br>high income countries.<br><!-- End of picture text -->
There is little evidence of change overall in three survey items on “state capture.” Paying bribes “to influence the content of new legislation, rules or decrees” appears to be about equally common in both years. Similarly, the share of firms reporting a significant impact on their business from Parliamentarians receiving bribes to affect their votes is little changed.<sup>30</sup> A slight improvement is evident for a similar question on payments to Government officials to affect the content of government decrees.

Overall, Table 2 indicates notable progress between 2002 and 2005 in administrative corruption, but not in state capture. Moreover, while most areas of administrative corruption show improvement, progress appears to be uneven and even absent in a couple of important areas, such as the courts.

This overall progress also hides uneven progress across countries.<sup>31</sup> Countrylevel data on four BEEPS measures are reported in Tables 3 and 4. Table 3 shows country-level means for bribe tax (Q40), bribe frequency (Q39a), costs of state capture (Q44b, on payments to government officials to influence rulemaking), and corruption as a serious obstacle to doing business (Q54q).<sup>32</sup> Figures in parentheses show ranks, for 2002 and 2005 respectively, among the BEEPS countries. Table 4 shows the average change over time for the same four variables, and in parentheses shows ranks in terms of change, with “1” showing the largest improvement and “27” the largest deterioration.

> 30 The question does not specify whether the “private payments/gifts” to Parliamentarians were paid by the firm or by other firms, but simply refers to the “impact on your business” from such practices.  Survey questions are printed in full in Appendix B.

> 31 Detailed country-by-country results are reported in Anderson and Gray (2006), which also contains several short case studies on corruption successes (Georgia) and failures (Kyrgyz Republic).

> 32 These four were selected from the larger set primarily for their prominent use in Gray, Hellman, and Ryterman (2004).  Several of the “state capture” variables they used were not included in the 2005 BEEPS; among the remaining state capture items Q44b was selected because firms cited it more frequently than Q44a (on payments to Parliamentarians), and it is correlated most highly with an overall index of the three state capture measures in the 2005 BEEPS.

29