rafmacalaba/datause-encoder-data huggingface dataset:
movable property as collateral.<sup>20</sup> However, based on interviews with banks in the region, loans only secured with movable collateral are not widely used, due to the inability of banks to secure the right over the asset through a collateral registry and due to the difficulties in enforcing security interests in movable property. Movable property is usually accepted as a secondary type of collateral and usually taken as a complement to immovable assets (real estate property). Loans secured with assets such as receivables and inventory, are used even less often.

None of the MENA countries have a modern law on secured transactions. Most of the countries have obsolete and fragmented secured transactions provisions governed by different laws that have not been reformed in many years. This means that often times, the scope of assets that can be used is limited and some jurisdictions have established ―numerus clausus‖ of assets that can be considered movable collateral. This concept is against the modern principle of secured transactions in which all types of assets should be able to be used as collateral as long as they can be given a value. Among the MENA countries, different jurisdictions allow different types of movable assets to be used as collateral. In general, tangible movable assets are widely accepted, while intangibles such as account receivables are less used.

With regard to _tangible movable property_ , according to the World Bank Hawkamah report, vehicles are viable collateral in most MENA countries including Algeria, Morocco, Egypt, Jordan, Lebanon, Saudi Arabia, Tunisia, Qatar, the UAE, West Bank & Gaza and Yemen since vehicle registration is generally well-developed, although some data from different countries indicate that registration of security interests in vehicles is still an issue.<sup>21</sup> A pledge over an entire enterprise, the ―fonds de commerce‖, is also possible in many countries, including in Morocco, UAE, Tunisia, Palestine and Algeria. Enterprise pledges are the only way to take a pledge on a floating pool of assets, rather than identifying each individual piece of collateral (see Table 5 for an overview of scope of assets in MENA).

> 20 World Bank MENA SME Lending Survey 2010

> 21 _Hawkamah Report,_ Chapter 7 Egypt, Chapter 8 Jordan, Chapter 10 Lebanon, Chapter 12 Palestine, Chapter 13 Qatar, Chapter Chapter 15 UAE.

14
Since school fees were abolished in Kenya in 2003, school uniforms are the main direct financial barrier to access to education at the primary level. A uniform costs about $6, a substantial expense for parents in a country where the GDP per capital is $360 (World Bank, 2002).

Between February and July 2003, ICS distributed a free school uniform to each student who was enrolled in grade 6 in January 2003 (students enrolled in grade 6 are on average 14 years old). In total, about 10,000 uniforms were distributed. In order to avoid creating incentives for students to transfer between schools, ICS field officers visited all the schools to collect baseline enrollment data before announcing the program—only children enrolled at the time of the baseline were eligible for the uniform program. In the fall of 2004, ICS distributed a second uniform to the same students if they were still enrolled in the same school. It was announced at the onset of the program that students still enrolled in school would be eligible for a second uniform.

# **e. Selection of Schools and Program Design**

The program took place in two rural districts of Western Kenya: Bungoma and ButereMumias. The study is based on a sample of 328 schools. Among them, 163 were randomly chosen for the teacher training. The randomization was done by the generation of a random number, after stratifying by the geographical division of the school, average performance of the school on the Kenya Certificate of Primary Education exam of 2001, and the gender ratio among upper primary students.

11
Distribution of gross deforestation areas by classes of clearing size in Mato Grosso 1996-2002 <!-- Start of picture text --> 30.00<br>m 1996-97<br>:[i] =m 1998-99<br>gee 2000-01<br>£ ba<br>pet Oo 2002 x<br>3 20.00 ©d Finn oel =zsy<br>woot eee 2<br>o Nie ia] a<br>es w TM N<br>o =<br>‘a co i Bio<br>= 15.00 jh tp —_iis = ——<br>oO ms - a as ‘o 3 fey CN<br>® ts a) 7 re 4 uy co<br>ao ay ot | 7 i = a] in<br>- on ae oe - oa)<br>o 10.00 aa wom — — an wis<br>5 yy 4 4<br>£a<br>oo Ncw i! ae ca —_ _<br>a 5.00 oea<br>1<br>oy<br>0.00<br><15ha 15-50ha 50-100ha 100-200ha 200-500 ha 500-1000ha > 1000ha<br><!-- End of picture text -->
Rodríguez-García R and Kuseck J (2007) Planning and managing for HIV/AIDS results. A handbook. Global AIDS Monitoring and Evaluation Team - GAMET, World Bank Global HIV/AIDS Program. Washington:The World Bank.

Rodríguez-García R. et al. (2009) Effective partnerships achieve results, En breve November 2000; Number 148, The World Bank.

Rodríguez-García R, Gaillard ME, Aran-Matero D (2010) AIDS Resource Efficiency in Latin America. World Bank.

Rodríguez-García R. et al. (2011) Analyzing Community Responses to HIV and AIDS. Operational Framework and Typology, Policy Research Working Paper 5532, The World Bank.

World Health Organization (2000) World Health Report 2000. Health systems: improving performance _._ Geneva, World Health Organization.

# **2. Stakeholders Interviewed**

Agudelo, Eliana. Ciudad Salud (IPS) Alquichire, Carlos. SaludCoop (IPS) Álvarez, Carlos. Sanitas (IPS) Ariza, Francisco. Ministerio de Educación Nacional (MEN) Ávila, Sandra. Comité de Prevención y Control del VIH/SIDA de las Fuerzas Armadas y Policía Nacional – COPRECOS Barrera, Alexander. Ciudad Salud (IPS) Belalcazar, Elba. UNFPA Bonilla, Edgard. Programa de Fármaco Vigilancia. INVIMA Campillo, Gustavo. Fundación Rasa Castaño, Martha. Secretaría de Salud, Departamento Valle del Cauca Castiblanco, Daniel. Programa de Fármaco Vigilancia. INVIMA Correa, Diana. Ecoopsos (EPS) Durán, Patricia. Programa de Salud y Seguridad Social de la Defensoría del Pueblo 62
# _Comparison with other countries_

The next question is how this relationship between the adoption of management practices and firm performance differs from the pattern found in other countries. The Enterprise Survey has collected similar management practices adoption data in 15 other countries and territories.<sup>15</sup> Figure 9 repeats the regression of Column 1 of Table 6, showing the (partial) correlation between firm performance, measured by log sales per employee, and the management practices score (normalized within each country), from both a conditional regression with controls and a unconditional regression without controls. Most countries, with the exception of Lao PDR, report a positive coefficient. The coefficient from Mozambique is among the highest, at a similar level as Gambia and Suriname, but higher than most Latin American countries as well as Kenya.

**Figure 9. Unconditional and conditional regression coefficients of a regression of log sales per employee on management practices, by country.**

<!-- Start of picture text --> Suriname 2018 0.48***<br>Gambia 2018 0.48***<br>Mozambique 2018 0.47***<br>Bolivia 2017 0.34**<br>Ecuador 2017 0.27**<br>Greece 2018 0.23***<br>Paraguay 2017 0.19***<br>Colombia 2017 0.11<br>Kenya 2018 0.09<br>Argentina 2017 0.08<br>Peru 2017 0.03<br>Lao PDR 2018 -0.34<br>-0.60 -0.40 -0.20 0.00 0.20 0.40 0.60<br>Coefficent<br>Unconditional Conditional<br><!-- End of picture text --> _Note:_ The reported coefficient is from a regression of log sales on management score<sup>16</sup> , with the conditional regression including controls on sector of activity, city and firm's age. The coefficients should be interpreted as log percentages. The management score is been normalized for each country separately. *** p<0.01, ** p<0.05, * p<0.1

> 15 Besides Mozambique, the Enterprise Survey includes questions on the adoption of management practices in the following countries: Argentina, Belarus, Bolivia, Colombia, Cyprus, Ecuador, Gambia, Greece, Kenya, Lao PDR, Paraguay, Peru, Suriname, Uruguay and West Bank & Gaza.

> 16 The management scores were calculated with a subset of questions that are common for all countries. Some questions asked in Mozambique were not asked in other countries, leading to a slightly different management score for Mozambique (and therefore a slightly different coefficient than found in Column 1 of Table 6).

29
**Figure 19: Simulation of alternative social policies for gas**

_Source_ : Simulations based on OPSM Survey, May 2002

# **Public transport in the Metropolitan Area**

5.34 In the case of public transport, the simulation concerns the replacement of the current US$60 million per year supply-side subsidy to bus, train and metro operators with a subsidized bus pass scheme. Given that the multi-dimensional poverty proxy is not so relevant for a non-household based service, the simulation considers giving these passes to all those enrolled in the current Heads of Household program.

**5.35** The results show that such a policy change would significantly improve the distributional incidence of subsidies to this sector, lowering the concentration coefficient from +0.16 to –0.21 (Figure 20). At the same time, the errors of inclusion and exclusion would fall from around 60% to around 35%.

**Figure 20: Simulation of alternative social policies for public transport in Metropolitan Area**

<!-- Start of picture text --> 60%<br>Status quo<br>50%<br>40%<br>30%<br>20%<br>Demand Side<br>Subsidies<br>10%<br>0%<br>1 2 3 4 5<br>Income Quintile<br><!-- End of picture text --> 44
# **<mark>Table 13: Exploring</mark>** **<u><mark>the Presence of Omitted Variable Bias in Base OLS Regression Results Underlying the Mean Decomposition North, Pooled Category of Additional Covariates Integrated to the Base Regression</mark></u>**

|_VARIABLES_|**Category of **<br>_Base North_|**Additional Covaria**<br>_Manager_<br>_characteristics_|**tes Integrated to the Bas**<br>_Plot characteristics_|**e Regression **<br>_Geospatial_<br>_characteristics_|_Crop fixed effects_|_Community_<br>_characteristics_|
|---|---|---|---|---|---|---|
|Female manager=1|-0.274***|-0.343**|-0.277***|-0.192*|-0.243**|-0.212*|
||(0.103)|(0.163)|(0.103)|(0.0999)|(0.0993)|(0.112)|
|Age|-0.00647|-0.0266*|-0.00740|-0.0138|-0.0149|-0.00299|
||(0.0130)|(0.0151)|(0.0131)|(0.0140)|(0.0122)|(0.0130)|
|Age Squared|3.90e-05|0.000220|4.75e-05|0.000112|0.000116|-1.28e-05|
||(0.000128)|(0.000149)|(0.000128)|(0.000137)|(0.000117)|(0.000129)|
|Years of schooling|-0.00859|-0.00840|-0.00889|-0.00824|-0.00175|-0.00813|
||(0.00586)|(0.00593)|(0.00590)|(0.00617)|(0.00571)|(0.00624)|
|Manager is nonmuslim†|-0.0320|-0.0244|-0.0281|-0.0484|-0.110|-0.0101|
||(0.0903)|(0.0915)|(0.0892)|(0.111)|(0.0872)|(0.0910)|
|Manager has nonfarm activity†|-0.0570|-0.0781|-0.0578|-0.109*|-0.0439|-0.0332|
||(0.0578)|(0.0585)|(0.0577)|(0.0632)|(0.0554)|(0.0612)|
|Household female adult size(age 12-60)|0.0367*|0.0436|0.0366|0.0446*|0.0281|0.0245|
||(0.0221)|(0.0397)|(0.0223)|(0.0246)|(0.0209)|(0.0229)|
|Household male adult size(age 12-60)|0.0677***|0.0810*|0.0671***|0.0571**|0.0738***|0.0453*|
||(0.0227)|(0.0431)|(0.0227)|(0.0245)|(0.0218)|(0.0240)|
|Child dependencyratio|-0.00260|0.0199|-0.00102|0.0107|0.00731|0.00470|
||(0.0393)|(0.0534)|(0.0393)|(0.0421)|(0.0387)|(0.0430)|
|Log [GPS-Land Area(HA)]|-0.711***|-0.716***|-0.712***|-0.698***|-0.732***|-0.676***|
||(0.0414)|(0.0416)|(0.0414)|(0.0440)|(0.0389)|(0.0466)|
|Log [GPS-Land Area(HA)Squared]|-0.0452***|-0.0465***|-0.0454***|-0.0427***|-0.0476***|-0.0587***|
||(0.0149)|(0.0148)|(0.0150)|(0.0157)|(0.0146)|(0.0172)|
|Plot distance to household|0.000268|0.000262|0.000272|0.000371**|0.000227|0.000605**|
||(0.000229)|(0.000228)|(0.000233)|(0.000160)|(0.000297)|(0.000306)|
|Access to extension†|0.124|0.127|0.114|0.0940|0.149*|0.113|
||(0.0891)|(0.0892)|(0.0872)|(0.102)|(0.0894)|(0.0817)|
|Grows cash crop†|0.0944|0.0897|0.0932|0.0462|0.860*|0.111|
||(0.0696)|(0.0711)|(0.0700)|(0.0758)|(0.471)|(0.0743)|
|Onlyone croponplot†|-0.454***|-0.454***|-0.451***|-0.488***|-0.0455|-0.451***|
|Robust standard errors inparentheses. ***p<|(0.0615)<br>0.01,**p<0.05,*p|(0.0621)<br><0.1. State fixed effe|(0.0607)<br>cts not displayed.†indica|(0.0647)<br>tes dummyvariable|(0.0670)|(0.0650)|

46
2015). Also, as explained above, it might be the case that domestic funds might have incentives to hold short-term liabilities. Domestic institutional investors might be subject to short-run monitoring by investors, regulators, and asset-management companies. This short-run monitoring generates incentives for managers to be averse to investments that are profitable in the long term but can have poor short-term performance (Opazo et al., 2015). Thus, international institutional investors, even with their shortcomings, might sometimes be an improvement over domestic institutional investors.

# **6. Conclusions**

Following the extensive debate on the effects of globalization, this paper argues that, although financial globalization has advanced significantly in recent decades, it has not developed as originally expected. The ways in which financial globalization has manifested differ from those anticipated by academic and policy discussions in several dimensions. The paper provides some examples of these deviations.

First, the net financing channel of financial globalization is less important than expected and, instead, the large movements of capital across borders are driven mostly by domestic and foreign investors seeking to simultaneously diversify their portfolio worldwide. Whereas risk sharing has its benefits, by allowing domestic investors to invest abroad and foreign investors to invest domestically, countries become more exposed to foreign shocks. Second, only a few firms seem to be benefiting from financial globalization. In addition, financial globalization could produce negative externalities that affect firms that are not direct participants of the globalization process. Third, several institutional investors are not necessarily diversifying risk as broadly as expected. Moreover, instead of behaving in a counter-cyclical manner, they tend to act in a procyclical way, which might exacerbate financial crises and help expand them across countries.

These examples illustrate that the mechanisms through which financial globalization was expected to promote growth are operating in a more limited way. Moreover, although financial 30
tendency for the enterpiise sector to operate with "soft' budget constraints,<sup>and the existence</sup> of a built-in "investment-hunger" by managers and planners generated,<sup>at an aggregated</sup> level, chronic excess demand in the economy. These disequilibria,<sup>in turn, showed-up as</sup> shortages of final products and inputs, queues, black markets,<sup>low quality products and</sup> forced substitution of final goods by consumers and inputs by producers.

The post-socialist transition changed the picture. One view is<sup>that restrictive demand</sup> policies and the ensuing 'transformational recession" have shifted the r iegime from excess demand and shortages in which output was suppIX determined<sup>(c</sup> rce constrained) to a regime in which output was dmand determined<sup>9</sup> On<sup>thc *</sup> - hand, since the transition renders a part of the existing capital stock<sup>economically obsolete, the</sup> supply side could still be a binding constraint to output expansion<sup>in the medium run. Thus,</sup> in our framework, the price level (or the rate of inflation) and aggregate<sup>output (or the rate</sup> of GDP growth) are both iointly determined by the interplay of aggregate demand factors (money, fiscal variables, the exchange rate) and aggregate<sup>supply factors (wages, the local</sup> price of imported inputs which in turn depends upon the<sup>exchange rate, the rate of</sup> productivity growth). Let us turn now to the main building<sup>blocks of our model.</sup> A-rate supply.

Let us assume that the ecoromy has an aggregate production function, F(.),<sup>in three</sup> factors: labor, L, imported intermediate inputs, IM, and physical<sup>capital, K.</sup> (1) Y = F (L,IM, K) 9 Kornai (1993) argues that in the post-socialist tiansitions suffering<sup>a "transformational</sup> recession" the economy shifted from being "resource (supply)<sup>constrained" to be "demand</sup> constrained".

12
SADC’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE services. Based on regional experience, every 10 percent reduction in broadband prices can be expected to bring about a significant increase in broadband penetration. Based on available information, the benefit of completing the SADC’s regional is estimated to total $203 million per year, against costs of only $139 million—a rate of return of 150 percent. Most benefits derive from the addition of new broadband users, making regional integration a positive business prospect for broadband service providers, since the revenue lost from existing customers is more than compensated by the revenue gained from new customers.

**Table 4.6 Gaps in intraregional connectivity, and total investment required to attain minimum levels of regional connectivity**

||Gaps (km)|Necessary investment ($ million)|
|---|---|---|
|Angola|782|21|
|Congo, Dem. Rep. of|1,781|48|
|Lesotho|2|<1|
|Madagascar|637|17|
|Malawi|477|13|
|Mozambique|21|1|
|South Africa|12|<1|
|Tanzania|1,220|33|
|Zimbabwe|226|6|
|**Total**|**5,158**|**139**|

_Source:_ AICD calculations.

A number of greenfield investments have been made to develop the communications backbone in several countries. When the gaps in the backbone and connectivity are bridged, the rate of return will escalate. For example, in Malawi a $13 million one-time investment to address ICT gaps will lead to a 37 percent rate of return on this investment annually (table 4.7).

**Table 4.7 Cost-benefit analysis of regional integration**

||Broadband p<br>Baseline 2008|rice ($/mo.)<br>Induced|Broadband subs<br>Baseline 2008|criptions ( ̳000s)<br>Induced|<br>Benefits ($ millions/yr)|Costs ($ millions)|Rate of return<br>(%)|
|---|---|---|---|---|---|---|---|
|Angola|157|98|16|65|28.6|21|135|
|Lesotho|50|31|0.1|7|0.8|0.1|1,629|
|Madagascar|102|63|4|11|3.3|17|19|
|Malawi|493|308|3|1|4.9|13|37|
|Mozambique|80|50|10|14|4.4|1|443|
|South Africa|27|17|426|2,126|154.4|12|1,287|
|Tanzania|64|40|6|44|7.1|0.3|2,231|

_Source:_ AICD calculations.

57
# **Table 1B: Growth and Uphill Flows of Sophisticated Exports (Cross-Sectional Regressions; Sophisticated exports to richer countries scaled by GDP)**

||(1)|(2)|(3)|
|---|---|---|---|
|Variable|Dependent varia|ble is Annual average|growth 1994-2003|
|Initial per capita GDP (log)|-0.001<br>(0.005)|-0.000<br>(0.005)|-0.000<br>(0.005)|
|Downhill export flows of sophisticated|-0.099**|-0.087**|-0.112**|
|(median) products (as share of GDP)|(0.047)|(0.035)|(0.050)|
|Uphill export flows of sophisticated|0.263***|0.122**|0.096*|
|(median) products (as share of GDP)|(0.080)|(0.048)|(0.048)|
|Years of primary schooling|-0.001<br>(0.017)|-0.009<br>(0.013)|-0.009<br>(0.013)|
|Capital stock|-0.003<br>(0.006)|-0.001<br>(0.004)|-0.000<br>(0.004)|
|Institutional quality (Rule of law)|0.010***<br>(0.003)|0.010***<br>(0.003)|0.010***<br>(0.003)|
|Exports of Non-sophisticated (median)<br>products (share of GDP)|||0.024<br>(0.022)|
|Observations|61|59|59|
|Adjusted R-squared|0.314|0.236|0.235|
|F-test|6.17|7.90|7.89|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Columns 2 and 3 exclude China and Ireland.

28
We use _OCCISCO_ variable which records the person’s primary occupation<sup>2</sup> coded according to the major categories in the International Standard Classification of Occupations (ISCO) scheme for 1988 and have 11 categories: _Legislators, Senior Officials and Managers; Professionals; Technicians and Associated Professionals; Clerks, Skilled Agricultural and Fishery Workers; Crafts and Related Trades Workers; Plant and Machine Operators and Assemblers; Service Workers and Shop and Market Sales; Elementary Occupations; Armed Forces_ and _other occupations and no identified occupations_ . Table 1 lays out the categories we include in more detail.

Autor (2010) and Autor and Dorn (2013) map these 3-digit categories into a distinct set of skill sets listed in figure 1 to better capture “routine” tasks. Hence, in the original ISCO categorization, operators of machines in manufacturing appear in “Plant and Machine Operators, and Assemblers” (category 8) but manufacturing workers who do not operate machinery appear in “ elementary occupations” (category 9). Both may be more routine than, for instance, food preparation or personal care, also found in category 9, which require potentially less skill, but which are also less easy to automate. As we are working with numerous countries with varying degrees of disaggregation and sometimes inconsistent or ambiguous categorizations across time that have been standardized by the census categories, we work directly with those. As we show, for the United States, this does not change the conclusions appreciably.

The available census data for developing countries for which we can follow employment in a substantive way before and after 1990 are limited, but not unrepresentative. From Latin America we have Brazil, the Dominican Republic, Ecuador, El Salvador, Mexico, Nicaragua, Panama, and Peru, most of which have had a manufacturing tradition. From Asia we have India, Indonesia, Vietnam, and Fiji. From the Middle East: Arab Republic of Egypt, Mo2For someone with more than one job, the primary occupation is typically the one in which the person had spent the most time or earned the most money.

6
# **VI. Conclusions**

This study attempts to highlight the key functions expected from a DMS and provide guidance to debt managers in emerging and developing countries on the options available to them. We conclude that it is fundamental for the DMS to meet the DMO’s evolving needs, while at the same time making a differentiation between functions and coverage that are mandatory, relevant and desirable. This differentiation guides the debt managers’ decision between building a DMS from scratch with the objective of tailoring the solution for the DMO or purchasing an off-the-shelf system that addresses most of its needs. We conclude that to favor an in-house solution requires the DMO to carefully consider how professionally it can lead the IT project, whether there is enough staff expertise, the time constraints, and the financial resources available to it.

Additionally, we share the results of a survey conducted by the WB, covering 31 countries with the objective of drawing the current landscape of solutions used by a sample of debt managers. The expectations of the debt managers for the future also were addressed by the survey. We find that the survey results show that most systems capture most standard instruments, but do not provide too much flexibility for capturing new instruments. This implies that the current systems can handle the critical functions and instruments these institutions deal with. However, a potential system limitation may arise if the nature of the debt portfolio evolves over time.

One clear result of the survey is that debt managers consider the ability of the DMS to interact with external IT platforms (e.g. FMIS) and within the DMO as an essential characteristic of the information ecosystem. This, however, appears to be the weakest feature of the current DMS used by the surveyed DMOs. The authors note that this might stem from the institutional weaknesses in a country rather than the software’s inability to connect to other platforms.

28