rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8544

# **Abstract**

A debt management system is the backbone of any sovereign debt management office. A robust, well-functioning and user-friendly system allows governments to strengthen their debt management environment. This study aims to contribute to the literature on (i) the essential requirements of a debt management system, (ii) the selection criteria for software that fits the system modernization and integration needs of a debt management office, and (iii) how the solutions currently used by governments meet those requirements. It also contains the results of a survey that shows the current landscape of solutions used by a sample of debt management offices from 31 countries. The target audience is emerging and developing countries that seek to strengthen the information technology platform they use for debt management. The study concludes that it is fundamental for a debt management system to meet the debt management office’s evolving needs, while at the same time differentiating among functions and coverage that are mandatory, relevant, and desirable. This differentiation provides a helpful guide for debt managers deciding between building a tailored debt management system from scratch or purchasing an off-the-shelf system. The survey results suggest that current systems can handle the critical functions and instruments of debt management offices. However, if the nature of respondents’ debt portfolios evolves over time, system limitations may present challenges. One clear takeaway is that debt managers should consider the ability of their debt management system to interact with external (for example, financial management information system) information technology platforms as an essential characteristic of their information ecosystem.

This paper is a product of the Financial Advisory and Banking, Department of Treasury. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at caslan@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
27 stock markets thai internationally integrated tend to have large financial systems and banks than less internationally integrated markets.

Using the stock market conglomerate INDEXES and the financial intermediary conglomerate FINDEXES, the strong positive correlation between stock market development financial intermediary development emerges even more strongly. As shown in Table 17, stock market INDEXES are all significantly correlated with financial intermediary FINDEXES at the 0.01 level. Figure 4 illustrates this correlation using INDEX-I and FINDEX-3.

Furthermore, measures of stock market inefficiency as represented APT, ICAPM, and CAPM, are positively correlated with banking inefficiency as measured by interest rate spread. Even with about 20 observations, CAPM and ICAPM are significantly negatively correlated with SPREAD. Stock market development (including measures of risk pricing efficiency) and financial intermediary development (including measures of banking efficiency) go hand-in-hand.

# **VI.** Conclusions

Having collected and summarized information on a wide assortment of indicators of stock market size. liquidity, efficiency, volatility, concentration, and the development of the regulatory system, we review five findings.

- (1) In the 41 country that we study, there are enormous cross-country differences in the level of stock market development for each particular indicator. For example, the ratio of market capitalization to GDP is greater than 1 in five countries and less than 0.10 in five countries.

- (2) There are intuitively appealing correlations among the individual stock market
Table 2: Probability of having an income-risk job based on marginal effects of the multinomial logit

||Model 1:|Model 2:|Model 3:|Model 4:|Model 5:|Model 6:|Model 7:|
|---|---|---|---|---|---|---|---|
||Raw gaps|Raw gaps|Individual|NUTS2|Language|Barriers to|Networks|
|VARIABLES||by origin|character.|FE|ability|certification||
|Foreign born|0.013***<br>(0.004)|||||||
|Origin EU15||-0.055***|-0.032***|-0.001|-0.017|-0.017|-0.019|
|||(0.008)|(0.009)|(0.010)|(0.010)|(0.010)|(0.015)|
|Origin NMS13||0.084***|0.055***|0.061***|0.044***|0.044***|0.011|
|||(0.009)|(0.009)|(0.009)|(0.012)|(0.012)|(0.018)|
|Origin EFTA||0.002|0.017|0.015|0.011|0.011|0.052|
|||(0.025)|(0.027)|(0.027)|(0.027)|(0.027)|(0.053)|
|Origin other Europe||<br>0.103***|<br>0.082***|<br>0.082***|<br>0.069***|<br>0.069***|<br>0.035*|
|||(0.008)|(0.009)|(0.009)|(0.011)|(0.011)|(0.018)|
|Origin MENA||<br>0.020|<br>-0.026*|<br>0.020|<br>0.002|<br>0.002|<br>-0.028|
|||(0.015)|(0.016)|(0.017)|(0.018)|(0.018)|(0.026)|
|Origin Sub-Saharan Africa||<br>-0.075***|<br>-0.088***|<br>-0.053***|<br>-0.059***|<br>-0.059***|<br>-0.104***|
|||(0.013)|(0.013)|(0.014)|(0.015)|(0.015)|(0.021)|
|Origin Asia||<br>0.041***|<br>0.015|<br>0.038***|<br>0.022|<br>0.022*|<br>-0.005|
|||(0.011)|(0.011)|(0.012)|(0.014)|(0.013)|(0.020)|
|Origin LAC||<br>-0.026**|<br>-0.041***|<br>-0.037***|<br>-0.041***|<br>-0.041***|<br>-0.098***|
|||(0.012)|(0.012)|(0.012)|(0.013)|(0.013)|(0.018)|
|Origin non-Europe OECD||-0.156***|-0.098***|-0.076***|-0.084***|-0.084***|-0.092***|
|||(0.017)|(0.020)|(0.021)|(0.021)|(0.021)|(0.031)|
|Age|||-0.012***<br>(0.001)|-0.013***<br>(0.001)|-0.013***<br>(0.001)|-0.013***<br>(0.001)|-0.012***<br>(0.001)|
|Age^2|||0.0001***|0.0001***|0.0001***|0.0001***|0.0001***|
||||(0.0000)|(0.0000)|(0.0000)|(0.0000)|(0.0000)|
|Male|||<br>0.089***|<br>0.085***|<br>0.085***|<br>0.085***|<br>0.080***|
||||(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|
|Years of Education|||<br>-0.049***<br>|<br>-0.050***<br>|<br>-0.050***<br>|<br>-0.050***<br>|<br>-0.051***<br>|
||||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Language – advance (reference||||||||
|category is mother tongue)|||||0.026***|0.023***|0.025|
||||||(0.010)|(0.009)|(0.016)|
|Language – intermediate||||||||
|(reference category is mother||||||||
|tongue)|||||0.034***|0.031***|0.017|
||||||(0.013)|(0.012)|(0.019)|
|Lanuae – beinner (reference||||||||
|gg g<br>category is mother tongue)|||||0.038***<br>|0.034***<br>|0.019<br>|
||||||(0017)|(0016)|(0023)|
|Barrier Certification|||||.|.<br>0.095***<br>|.<br>0.115***<br>|
|||||||(0.022)|(0.031)|
|Job through networks*Migrant|||||||<br>0.023*<br>(0.013)|
|Job through networks*Natives|||||||<br>0.044***|
||||||||(0.005)|
|Observations|395,284|395,284|394,291|394,291|393,300|393,300|<br>373,350|
|R-squared|0.003|0.006|0.078|0.088|0.089|0.089|0.088|
|NUTS2 division of residence FE|No|No|No|Yes|Yes|Yes|Yes|
|P(income-risk jobs)|0.368|0.369|0.358|0.356|0.356|0.356|0.353|

Note: Robust standard errors are reported in parentheses; ***: p<0.01, **: p<0.05, *: p<0.1.

44
<!-- Start of picture text --> Model Utility = 10 Counterfactual Utility = 10.0075, Reallocation = 0.10391<br>g::::g::::<br>2: : : : 2 : : : :<br>A::::A::::<br>2-4°: : . : ° : : ’ :<br>£ i: : : Ds: 2-4£ cc.: : : teense:<br>Sata Pe || —"aatual ees<br>=== Modeled : : : === Efficiency: U.S. Dispersion : :<br>10 12 14 16 18 10 12 14 16 18<br>In(population) In(population)<br>Counterfactual Utility = 10.0775, Reallocation = 0.29562 Counterfactual Utility = 9.9547, Reallocation = 0.24311<br>0 eee eee wee ete eee eee eee eee eee ey 0 eee eee eee mn ee ee eee eee eee eee ee<br>&a : : : : Ssa : : : :<br>A: : : : A : : : :<br>° : : . : ° : : . :<br>ee£ | ee: : ee ee: Pee: ee£ | ee: : ee Pee: See::<br>=—= Actual : ‘ =—_——= Actual : :<br>==———= Amenities: U.S. Dispersion : : === Exc. Frictions: U.S. Dispersion] : :<br>-6 i i -6 i i<br>10 12 14 16 18 10 12 14 16 18<br>In(population) In(population)<br><!-- End of picture text -->
and over time. As future rounds of data collection are completed, the database will allow researchers to provide empirical evidence linking financial inclusion to development outcomes and promote the design of policies firmly based on empirical evidence.

The complete economy-level database, disaggregated by gender, age, education, income, and rural or urban residence, is available at http://www.worldbank.org/globalfindex. Individuallevel data will be published in October 2012.

1.  See, for example, King and Levine (1993); Beck, Demirguc-Kunt, and Levine (2007); Beck, Levine, and Loayza (2000); Demirguc-Kunt and Levine (2009); Klapper, Laeven, and Rajan (2006); and World Bank (2008a).

2. The Bill & Melinda Gates Foundation funded three triennial rounds of data collection through the complete questionnaire. In addition, data on two key questions relating to the use of formal accounts and formal loans will be collected and published annually.

3. For example, Beck, Demirguc-Kunt, and Martinez Peria (2007); and Cull, Demirguc-Kunt, and Morduch (forthcoming).

4. Collins and others 2009.

5. For a detailed literature review, see World Bank (2008a) and references therein. Campbell (2006) also provides an overview of the household finance field.

6. Beck 2009.

7. Johnston and Morduch 2008.

8. Dupas and Robinson 2009, 2011.

9. In addition, the World Bank has designed surveys to assess financial access in developing economies including Brazil, Colombia, India, and Mexico.

10. The LITS includes high-income economies in Europe and Central Asia. For additional information, see EBRD (2011).

11. See Beck and Brown (2011) for a discussion of the use of banking services in transition economies using the LITS data set.

12. See Beck, Demirguc-Kunt, and Martinez Peria (2007). In addition, Honohan (2008) and World Bank (2008a) used these indicators as well as other data to estimate a headline indicator of access. In a separate exercise Beck, Demirguc-Kunt, and Martinez Peria (2008) documented cross-country eligibility, affordability, and geographic access barriers by surveying banks.

**7**

MEASURING FINANCIAL INCLUSION
Table 9: Productivity and Technology Sophistication, Continued

||(1)|(2)|(3)|
|---|---|---|---|
|VARIABLES|ln(VAPW)|ln(VAPW)|ln(VAPW)|
|GBF EXT|1.72***<br>(0.32)|||
|GBF EXT<sup>2</sup>|-0.20***<br>(0.05)|||
|GBF MOST||1.93***<br>(0.45)||
|GBF MOST<sup>2</sup>||-0.28***<br>(0.09)||
|GBF GAP||0.38***<br>(0.10)||
|GBF EXT*AGRI|||0.46**<br>(0.23)|
|GBF EXT*MANF|||0.61***<br>(0.08)|
|GBF EXT*SVC|||0.49***<br>(0.09)|
|SSBF EXT*Agriculture|0.33*|0.42**|0.43*|
||(0.20)|(0.19)|(0.25)|
|SSBF EXT*Food Processing|0.30*|0.32*|0.28|
||(0.18)|(0.18)|(0.18)|
|SSBF EXT*Apparel|0.44***|0.40***|0.36***|
||(0.11)|(0.10)|(0.11)|
|SSBF EXT*Retail and Wholesale|-0.02|-0.07|-0.05|
||(0.11)|(0.11)|(0.12)|
|SSBF EXT*Other Manufacturing|0.07|0.05|0.02|
||(0.05)|(0.05)|(0.05)|
|SSBF EXT*Other Services|0.55**|0.58**|0.61***|
||(0.23)|(0.23)|(0.23)|
|Var(ABF EXT)|0.29***|0.36***|0.39***|
||(0.10)|(0.10)|(0.10)|
|Firm Characteristics|_~~√~~_|_~~√~~_|_~~√~~_|
|Observations|2,746|2,746|2,746|
|R-squared|0.50|0.50|0.50|

Note: Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. The regressions controlled for observable firm characteristics including size (i.e., small, medium, and large), sector (i.e., agriculture crops, livestock, food processing, wearing apparel, pharmaceutical, moto-vehicle, wholesale and retail, finance, land transport, hospital, other manufacturing, and other services), age (i.e., 0-5, 6-10, 11-15, and 16 or more), export, and multinational status.

controls in regression (6) the book value of capital per employee in the firm and the cost of labor per worker as a proxy for the average human capital per worker. The results from this exercise are reported in the Appendix in Table F.2 (which controls for capital per worker) and Table F.3 (which includes the controls for both capital and human capital per worker).

35
cured by referral, friends or relatives making referrals will be under less family pressure to refer individuals who lack the skills, drive or innate ability to prefer tasks on the job. The equalizing effect of migrant opportunities on within village inequality raises the possibility that high ability individuals, lacking the personal connections necessary to secure local employment, use migration to raise their households income relative to those households with members already employed in the local non-agricultural labor market.

# 5.5 Migrant Networks, Investment and Specialization

The results in Tables 5 and 7 combine to show that increases in household per capita consumption and income are associated with increasing out-migration, and that these effects are stronger among the poor. However, they do not shed light on the mechanisms by which migration affects consumption or income. First, the migrant labor market may relax credit constraints locally through remittances, resulting in higher productive investment either in agriculture or non-agricultural selfemployment which contributes to increased earnings. Alternatively, households may not respond to the relaxation of credit constraints by investing proceeds from migration in housing or consumer durables. Second, income may increase because migration makes it possible for households to supply more labor to productive activities, either directly as employees in migrant destinations, or through local employment as out-migration reduces the local labor supply. Third, households who have a comparative advantage in agriculture may find it easier to expand their land holdings and earn more income from agriculture.

Understanding these mechanisms may have quite significant implications for rural policy in China. For example, if labor market policies relaxing restrictions on living in urban areas increase agricultural investment, policy makers charged with designing agricultural policy should take these increases into account. Alternatively, if loosening labor market restrictions does not affect agricultural investment, agricultural policy makers would need to account for capital requirements in designing policies aimed at facilitating investments which promise high returns, such as green houses. To learn about these mechanisms, we next directly examine the relationship between migration and factors which may drive income generation: investment, labor supply, and land use.

## 5.5.1 Investment

To observe whether credit constraints are relaxed by migration, we examine whether out-migration affects either productive investment or investment in housing or durables. We do so using the 25
governments prefer to adopt the multidimensional set-up as the size of transaction increases. It is reasonable that many other factors than prices—e.g., employment and investment in rural areas—would likely matter in the case of large-scale railway concessions.

With endogeneity controlled for as mentioned above, we find that the selection of multidimensional auctions increases the possibility of renegotiation. Table 11 presents the second stage regression results. It indicates why renegotiation happened or under what circumstances renegotiation tends to take place. The coefficient of the predicted probability of choosing multidimensional auctions is positive and significant. It means that the authorities are motivated to adopt the multidimensional format for certain reasons (as discussed above), but they are likely to fail in benefiting from the potential effects of multidimensional auctions, whence resulting in more renegotiation events. This is consistent with a number of episodes highlighting the real difficulty in managing multidimensionality.

The table also shows that several elements of governance are important to reduce the likelihood of renegotiation. Anti-corruption policies are found particularly powerful. The corruption control index has a significant negative coefficient. A sound regulatory environment may also be useful to mitigate the renegotiation risk. Meanwhile, the probability of renegotiation increases with the size of project in the road sector. Largescale road concessions are more likely to undergo contractual adjustments in due course.

<u>Table 11. 2SLS Estimation of Renegotiation Equation</u>

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Road length|0.00050 <sup>**</sup><br>(0.00021)|0.00014<br>(0.00022)|0.00067 <sup>***</sup><br>(0.00023)|0.00057 <sup>***</sup><br>(0.00018)|
|Track length|-0.00004|0.00003|-0.00006 <sup>*</sup>|-0.00004|
||(0.00003)|(0.00004)|(0.00003)|(0.00003)|
|Contract duration|-0.0179 <sup>**</sup>|-0.0305 <sup>***</sup>|-0.0144 <sup>*</sup>|-0.0174 <sup>**</sup>|
||(0.0079)|(0.0079)|(0.0085)|(0.0082)|
|Govt effectiveness|0.3303|0.8342 <sup>*</sup>|-0.1315||
||(0.4485)|(0.4395)|(0.2517)||
|Regulatory quality|-0.4738|-1.1963 <sup>**</sup>||-0.0748|
||(0.5839)|(0.5483)||(0.3056)|
|Control of corruption|-0.5222 <sup>***</sup>||-0.5915 <sup>***</sup>|-0.5312 <sup>***</sup>|
||(0.1085)||(0.1078)|(0.1147)|
|_MULTI_<br>_D_|0.6008 <sup>*</sup>|-0.3500|0.9569 <sup>**</sup>|0.6825 <sup>**</sup>|
||(0.3119)|(0.3601)|(0.3711)|(0.2871)|
|Constant|0.9938 <sup>**</sup>|1.9252 <sup>***</sup>|0.5573 <sup>**</sup>|0.7228 <sup>***</sup>|
||(0.4205)|(0.3702)|(0.2401)|(0.2507)|
|Obs.|106|106|106|106|
|_F_statistics|33.80|15.37|37.01|37.52|
|R-squared|0.3833|0.4412|0.4040|0.4089|

Note: The dependent variable is the dummy variable for the incidence of renegotiation. The robust standard errors are shown in parentheses. *, ** and *** indicate the 10%, 5% and 1% significance levels, respectively.

21
Substituting (2) into (1) and including sector fixed effects in addition to economy-pair fixed effects gives our baseline estimating equation:<sup>13</sup> We estimate (3) using Poisson pseudo-maximum likelihood (Santos Silva and Tenreyro, 2006) in order to take into account the presence of bilateral trade flows that are zero or missing from the dataset. The intuition behind this approach is simple. The first order conditions for Poisson estimation are mathematically equivalent to those for weighted least squares of the non-linear model given by exponentiation of (3). Thus, the potential problem posed by taking the logarithm of zero on the left-hand side is avoided.<sup>14</sup> Our data and sources are set out in full in Table 1. For our baseline results, we use bilateral trade data disaggregated to the HS 2-digit level. Our tariff data come from the MAcMap database (Laborde et al., Forthcoming). MAcMap applied tariffs are bilaterally disaggregated, and take full account of regional agreements and preference schemes. We aggregate the original HS 6-digit data to the HS 2-digit level using a reference group weighting scheme that limits endogeneity problems (Laborde et al., Forthcoming). Essentially, tariffs for economy i are weighted by the import patterns of comparable countries, rather than by those of economy i itself. We take ad valorem equivalents of non-tariff barriers from Kee et al. (2006)—and aggregate them to the 2-digit level in the same way—while GDP data are sourced from the World Development Indicators. We estimate the model for a cross-section of APEC member economies for the year 2004.

> 13 In fact, this involves a slight simplification. A strict derivation from (1) would imply a large number of additional parameters, including fixed effects in the country-pair-sector dimension and interaction terms between each of the trade cost parameters and the sector fixed effects. See Baldwin and Taglioni (2006) on this and similar points. The expedient we have adopted represents a compromise between theoretical rigor and empirical tractability.

> 14 We prefer Poisson to the Heckman sample selection estimator proposed by Helpman et al. (2007) for two largely technical reasons. First, over-identification of the Heckman model is difficult in this context, and the literature does not yet provide a convincing solution to this problem. Second, the first stage probit model on which the Heckman estimator is based can be biased and inconsistent in the presence of standard, unconditional fixed effects (see generally Greene, 2004, on this point). Poisson is one of relatively few nonlinear panel data models which do not suffer from this problem, and remain consistent in fixed effects models.

22
Page 10 **Figure II.1 illustrates the very striking upward trend in household incomes over the ten year period under study. The median income has increased to 2.7 times what it was in 2001.** While we have no comparable data for housing prices over the period **,** it is nonetheless striking that formal housing is still not affordable for a large part of the population. The population living in challis and slums alone represents more than two million people, and they cannot be placed in new housing overnight. As such, there is a dramatic and immediate need for better services for this very substantial share of the population. A strategy for meeting this need will be discussed in detail in Sections IV and V below.

If the supply of housing were perfectly elastic any increase in income for the higher income slum dwellers would allow them to move into formal housing. If the supply of housing were completely inelastic then higher incomes would result in higher prices and a general increase in income would results in households living in the same type of housing in spite of their increased income.

**Figure II.1** shows that a similar proportion of households who were living in slums/challis in 2001 are also living in slums/challis in 2011 in spite of a large increase in nominal income<sup>8</sup> . However, if we take into account the demographic conditions in slums (higher natural growth rate, higher rate of in-migration) it is clear that quite a number of slum/challi dwellers must have “escaped” from their slum into higher standard housing. If it was not the case the proportion of slum dweller in the total population would have increased (as it appears to be the case in Mumbai). For the slum households whose increased income allowed them to move out of a slum, it is likely that they have moved to the older part of the housing stock, many in the old city, which is cheaper and often more centrally located than the newer apartments in the more distant suburban areas.

> 8 It should be noted, as discussed later in the text, that the share of the population living in slums/challis was calculated independently using our mapping of slum areas combined with data on density per hectares from detailed surveys of representative slums and on average household size. Those computations provide us with the number of households living in slums/challis. The results are shown in Figure III. 2