rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text -->
|<br>{wi}iZ0<br>A; t = Yt — wrAtA, (10)<br>vw< f(A;)A.) ( (11)<br>S 12)<br>{cr} gue) (<br>t=0<br>Cr + Ars A. +Tk, t= 0,1,... ( 13)<br>At .<br>;;<br>.<br>>, BY (cr) (14)<br>{ce}9 t=<br>Ce + Ay 15< (1+ w;,)Ap, t = 0,1,... (15)<br>Ct Ars_— ;<br><!-- End of picture text -->
PRE Working Papei Ser.Žs

|||A L ho||Contact<br>Author<br>Date|
|---|---|---|---|---|
|WPS771|Macroeconomic Structure and<br>Policy in Zimbabwe: Analysis and<br>Empirical Model (1965-88)|Ibrahim A. Elbadawi<br>Klaus Schmidt-Hebbel|Septernber 1991|S Jonnakuty<br>39074|
|WPS772|Macroeconomic Adlustmen**t**o**Oil**<br>Shocks and Fiscal Reform:<br>Simulations for Zimbabwe, 1988-95|Ibrahim A. Elbadawi<br>Klaus Schmidt-Hebbel|September 1991|S. Jonnakuty<br>39074|
|WPS773|Are Ghana's Roads Paying Their<br>Way? Assessing Road Use Cost and<br>User Charges in Ghana|Reuben Gronau<br>|September 1991|J. Francis<br>35205|
|WPS774|Agricultural Pricing Systems and<br>Transportation Policy in Africa|Mark Gersovitz|October 1991|B. Gregory<br>33744|
|WPS775|The Macroeconomics<br>of Public<br>Sector Deficits: A Synthesis|William Easterly<br>Klaus Schmidt-Hebbel|October 1991|R. Martin<br>39065|
|WPS776|Enforcement of Canadian Untair"<br>Trade Laws: The Case for Competitio<br>Policies as an Antidote for Protectio|Mark A Dutz<br>n<br>n|October 1991|N. Artis<br>37947|
|WPS777|Do the Benefits of Fixed Exchange<br>Rates Outweigh Their Costs<sup>9 </sup>The<br>Franc Zone in Africa|Shantayanan Devarajan<br>Dani Rodrik|October 1991|A Bhalla<br>37699|
|WPS778|A Dynamic Bargaining Model of<br>Sovereign Debt|Eduardo Fernandez-Aria|s October 1991|S. King-Watson<br>31047|
|WPS779|Special Programme of Research,<br>Deveiopment and Research Trainng<br>in Human Reproduction|Janet Nassim|October 1991|0. Nadora<br>31019|
|WPS780|Optimal User Charges and Cost<br>Recovery for Roads in Developing<br>Countries|lan G Heggie<br>Vincy Fon|October 1991|P. Cook<br>33462|
|WPS781|The Korean Consumer Electronics<br>Industry: Reaction to Antidumping<br>Actioris|Taeho Bark|October 1991|N. Artis<br>37947|
|WPS782|The Economic Effects of Widespread<br>Application of Antidumping Duties<br>to Import Pricing|Patrick Conway<br>Sumana Dhar|October 1991|N Artis<br>37947|
|WPS783|The Origins and Evolution of<br>Antidumping Regulation|J Michael Finger|October 1991|N. Artis<br>37947|
|WPS784|Chemicals from Poland: A Tempest<br>in a Teacup|Andrzej Olechowski|October 1991|N. Artis<br>37947|
contribution rate for the health system consequently does not rise as much. The aggregate rise in the tax rate to cover constant health and social security benefits is 9.5 percentage points consisting of the 8 percentage points for social security contributions and 1.5 percentage points for health.

**Figure 5a. Population Growth is Likely to Slow Down and Life Expectancy will Rise over the** **<u>Period 2010-2050</u>**

**Figure 5b. To Cover the Fiscal Costs of Aging, either Debt or Contributions must Rise, or** **<u>Benefits must Fall</u>**

<!-- Start of picture text --> 80 79.9 35% 45% 45%<br>140% 140%<br>79 31% 30%<br>78 120%<br>77 76.9 25% 40% 40% 39% 100%<br>76 20%<br>80%<br>75<br>74 15% 35% 36% 60%<br>7372 72.3 11% 10% 35% 41% 50% 40%<br>71 1% 5% 31% 20%<br>70 0% 30% 0%<br>2010 2030 2050 2010 2030 2050<br>Life expectancy Population growth (right axis) Pension replacement rate<br>Pension contribution rate<br>Debt/GDP (right axis)<br>Source: UN Population Division. Source: Author’s estimations.<br><!-- End of picture text --> Source: UN Population Division.

In order to keep benefits constant, the social security contribution rate would need increase by 10 percentage points from its current level of 31 percent (11 percent workers’ contributions and 20 percent employers’ contributions) to 41 percent (Figure 5b) between 2010 and 2050.<sup>12</sup> This increase in the pension contribution rate assumes that the health system balances such that fewer workers also finance the health expenditures of more retirees and fewer workers. The contribution rate for the health system consequently does not rise as much. The aggregate rise in the tax rate to cover constant health and social security benefits is 11.5 percentage points consisting of the 10 percentage points for social security contributions and 1.5 percentage points for health.

**Figure 6a. Depending on the FinancingFigure 6b. Capital per Worker likely to rise if** **<u>Alternative, Saving Rate will Evolve Differently Aging is Financed by Reduced Pensions</u>**

<!-- Start of picture text --> 6.6% 1.3<br>6.5% 1.2<br>6.4% 1.1<br>6.3% 1.0<br>6.2% 0.9<br>6.1% 0.8<br>6.0% 0.7<br>5.9% 0.6<br>5.8% 0.5<br>5.7% 0.4<br>2010 2030 2050 2010 2030 2050<br>Saving rate (Benefit Financing) Capital per worker (Benefit Financing)<br>Capital per worker (Tax Financing)<br>Saving rate (Tax Financing)<br>Capital per worker (Debt Financing)<br>Saving rate (Debt Financing)<br><!-- End of picture text --> Source: Author’s estimations.

Source: Author’s estimations.

12 The system for social security contributions are, naturally, more complicated than simply two rates referred to. However, to make the general equilibrium model tractable, it is assumed that the contributions to the social security system can be reasonably replicated by an aggregate 31 percent contribution rate.

22
22 <!-- Start of picture text --> Chart I<br>BRAZIL<br>36<br>34-<br>32-<br>30 ,<br>28-<br>26-<br>24-<br>22-<br>20 -<br>16-<br>14 - M<br>12<br>1965 I 1967 1969 4 1971 1 1973 1 1975 I 1977 l 1979 I 1981 1 1983 1 1985<br>1966 1968 1970 1972 1974 1976 1978 1980 1982 1984<br>Year<br>0 Priv. Invt./GDP + Dom.Cred./GDP<br><!-- End of picture text -->
Table 16: Business dynamism and SOE concentration at the sectoral level (3d), 2016-2019.

||Entry rate|Exit rate|Share young|SD(growth)|Creation|Destr.|Net change|Realloc.|HHI|
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Share of emp. in BOS|0.0256|-0.00955|-0.0946***|0.463|0.136**|-0.147*|0.283**|-0.0109|0.578***|
||(0.0222)|(0.0302)|(0.0360)|(1.713)|(0.0607)|(0.0878)|(0.124)|(0.0858)|(0.121)|
|Average size|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Average age|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector emp. share|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector 3d|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1107|1107|1107|1096|1107|1107|1107|1107|1107|
|R-squared|0.831|0.724|0.949|0.372|0.775|0.715|0.579|0.830|0.968|

_Note:_ Dependent variables at the 3-digit industry level are, for each column, entry rate, exit rate, share of employment in firms up to 5 years of age, standard deviation of employment growth rate, gross job creation, gross job destruction, net employment change, job reallocation, and the Herfindahl–Hirschman index (HHI). The share of employment in SOEs is the fraction of employment in all SOEs relative to total employment. All models include year and sector-fixed effects. Data includes private sectors from RAIS, 2016-2019. Robust standard errors in parentheses. Significance levels: * 10%, ** 5%, *** 1%.
# **CHAPTER 1: FINANCIAL ACCESS IN COLOMBIA**

## **1.1 INTRODUCTION**

1. The present report is part of a series of recent World Bank studies which explore the theme of access to financial services for low-income populations.  To date, regional efforts include work on financial access in urban Brazil (Brazil: Access to Financial Services, 2003), in Mexico (Broadening Access to Financial Services Among the Urban Population: Mexico City’s Unbanked, 2003) and in Nicaragua (The State of Microfinance in Nicaragua, 2002).  World Bank studies and operations on financial access have also been conducted in India (Access to Finance in India, 2004) and Romania (Romania Rural Finance Program).

2. The World Bank has worked with the Colombian government over the past years to address the challenge of low penetration of banking services for the poor. To this extent, a comprehensive study has been conducted on access to finance in rural Colombia (Colombia Rural Finance, Access Issues, Challenges and Opportunities, 2003). In addition, a conference jointly organized by Asobancaria, the Colombian Banking Assocation and the World Bank on ‘ _Bancarizacion – Una Herramienta para el Desarollo’_ in Cartagena de Indias in April 2004 raised awareness of the importance of access to financial services with Colombia’s private banks, regulators and politicians. Finally, the World Bank’s recently approved Second Programmatic Financial Sector Adjustment Loan to the Republic of Colombia includes a component on improvements in the provision of housing finance for the poor.

3. In this context, the present report focuses on access to financial services in Bogotá and urban Colombia, drawing on existing publications and internal reports, a demand side survey and an initial overview of supply side issues. This report also highlights a number of issues requiring further research. Due to the parallel efforts of the World Bank, rural finance and housing finance in Colombia are not included in the scope of this report.

4. The term unbanked has traditionally been applied to individuals without deposit accounts in a regulated financial institution. In this report, it refers more broadly to individuals without access to formal financial services. Financial services are understood as products and services provided by formal financial institutions, including savings and checking accounts, debit and credit cards, other payment services, consumer loans, microcredit and remittances.

5. The importance of access to financial services in the context of macroeconomic growth and financial sector stability is well documented in economic literature. Development theory has proved the existence of 17
African citizens’ dissatisfaction with the ‘supply’ of democracy arguably explains the steady decline in African citizens’ trust in representative institutions, in particular for the executive (Figure 14).<sup>36</sup> A healthy dose of critical, rather than blind, trust is needed to generate positive spillovers for development.<sup>37</sup> Such critical trust requires public transparency and access to information to critically evaluate policies, and institutional channels to contest them and hold government accountable. When transparency and institutionalized accountability are perceived as limited, mistrust can turn into distrust. This crisis of trust can have deleterious effects: it could first radicalize social protests – and harm socio-political stability.<sup>38</sup> It is indicative, in that regard, that the rise of peaceful protests across SSA since 2019 has been paralleled by a heavy increase in violent demonstrations. Declining trust could also harm development prospects. An extensive literature shows that economic growth, social cohesion and well-being are indeed highly contingent on citizens’ trust in political institutions (Fukuyama 1995, Knack and Zac 2001, Algan & Cahuc 2013, Algan 2018, IADB 2022).

The gap between civic demands/mobilization and state response has likely contributed to fuel distrust between citizens and states.<sup>39</sup> Many protest movements have had to contend with strong repression and political backlashes. In a number of countries, social protests were initially successful in making their demands heard and toppling autocratic rulers. Yet, the lack of institutionalized opposition has complicated the task of alternance and the legacy of military involvement in politics has, in places such as Burkina Faso and Sudan, opened space for military coups and civil conflict.

# **Figure 14. A decline in trust in executive institutions? African countries (2005-2021)**

> 36 Gyimah Boadi 2019.

> 37 Zmerli, Sonja and Tom W.G. Van Der Meer. 2017. Handbook of Political Trust. Cheltenham: Edward Elgar Publishing. Norris, P., et al. 2019. “Trust but Verify”. Trustgov Working Paper Series, no. 2 (September) OCDE. 2017.

> 38 Cheeseman et al. (2023) showed, in this regard, in Nigeria, that anti-corruption messages raise awareness of corruption risks, and lead to reduced tax collection due to concerns that taxes will be wasted. This demonstrates the unintended consequences of interventions aimed at increased transparency of governments failures.

> 39 Börzel, Tanja A., and Thomas Risse. "Dysfunctional state institutions, trust, and governance in areas of limited statehood." _Regulation & Governance_ 10.2 (2016): 149-160.

27
**Table 16: All instruments combined: Student test scores regressed on classroom observation variables (level 1) and teacher test scores**

|||All te|achers||Tea|chers with|test score <|=0|Te|achers with|test score >|0|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Share of time teaching (SDI)|0.9962***|0.311|1.2284***|0.447*|1.7596***|2.064***|2.5704***|2.299***|0.5681*|0.542**|0.4511|0.492*|
||(0.3102)|(0.258)|(0.2724)|(0.241)|(0.5458)|(0.742)|(0.5214)|(0.444)|(0.3185)|(0.254)|(0.3090)|(0.292)|
|Share of time lost (SDI)|-0.9036**|-0.114|-0.9973***|-0.0736|-0.8839**|-0.312|-1.3845***|-0.516|-0.6778|-0.265|-0.6568|0.182|
||(0.3548)|(0.379)|(0.3636)|(0.369)|(0.3524)|(0.684)|(0.3865)|(0.533)|(0.5783)|(0.627)|(0.6303)|(0.624)|
|Good Teacher Demeanor (SDI)|-0.1249|-0.139|||0.2360|0.961|||-0.2602|-0.969**|||
||(0.3847)|(0.327)|||(0.5541)|(0.624)|||(0.4901)|(0.400)|||
|Good Pedagogical Practise (SDI)|-0.6965|-1.071**|||-1.5701**|-1.599***|||-0.2187|-0.483|||
||(0.4906)|(0.420)|||(0.6165)|(0.379)|||(0.7559)|(0.412)|||
|Classroom Environment (SDI)|-0.2646|-0.298**|||-0.0817|-0.153|||-0.6463**|-0.352**|||
||(0.1652)|(0.138)|||(0.1777)|(0.118)|||(0.2409)|(0.148)|||
|Availability of materials (SDI)|1.2443*|1.122**|||1.4295|0.171|||2.0536*|2.098**|||
||(0.7018)|(0.528)|||(0.9693)|(0.623)|||(1.0212)|(0.808)|||
|Use of materials (SDI)|-0.2610|0.351|||-0.4364|-0.110|||-0.3071|0.333|||
||(0.3094)|(0.270)|||(0.3149)|(0.377)|||(0.3560)|(0.286)|||
|Emotional Support (CLASS)|-0.0681|-0.0133|||-0.2340|0.209|||0.2271|0.0991|||
||(0.1471)|(0.126)|||(0.2386)|(0.235)|||(0.2172)|(0.158)|||
|Classroom Organisation (CLASS|-0.0107|0.0871|||0.0926|-0.348*|||-0.2296*|0.0476|||
||(0.1342)|(0.117)|||(0.1950)|(0.203)|||(0.1342)|(0.115)|||
|Instructional Support (CLASS)|0.1843|0.159|||0.4169*|-0.500**|||0.0030|-0.0342|||
||(0.1744)|(0.140)|||(0.2232)|(0.244)|||(0.2352)|(0.140)|||
|Student Engagement (CLASS)|-0.0098|0.0381|||-0.1626|0.598***|||0.0330|-0.0377|||
||(0.1244)|(0.0862)|||(0.1784)|(0.163)|||(0.1269)|(0.0866)|||
|Classroom culture (Teach)|0.0324|-0.0248|||0.3869|-0.758***|||0.1369|0.233|||
||(0.2220)|(0.170)|||(0.2610)|(0.253)|||(0.1869)|(0.152)|||
|Instruction (Teach)|-0.4386**|-0.163|||-0.8424***|-0.146|||0.4375*|0.454***|||
||(0.2121)|(0.178)|||(0.2526)|(0.248)|||(0.2276)|(0.163)|||
|Socioemotional Skills (Teach)|0.6576***|0.288*|||0.8604***|1.180***|||0.3802**|0.0251|||
||(0.1912)|(0.163)|||(0.2919)|(0.258)|||(0.1825)|(0.158)|||

continued...

56
28.1). For out-of-sample municipalities, the household and sub-area models are equally accurate (correlation of 0.7), while the area-level model is moderately less accurate (correlation of 0.66).

6. All geospatial estimates are substantially less accurate than the official 2010 municipal poverty estimates when predicting 2015 municipal poverty rates, particularly for out-ofsample municipalities. The correlation between the 2010 estimates and the 2015 benchmark is 0.91 in-sample and 0.90 out of sample, as compared with 0.86 and 0.70, respectively, for the preferred household model geospatial estimates. This suggests that poverty in Mexico was relatively stable, at least between 2010 and 2015.

Overall, the results confirm that combining survey data and geospatial data can greatly improve the accuracy and precision of monetary poverty estimates. However, accuracy and precision both suffer markedly when making synthetic predictions into out-of-sample municipalities. This underscores the importance of using a statistical method that conditions predictions on the sample data and of including as many target areas as possible in the sample. In this context, householdlevel models generate more accurate and precise predictions than the basic sub-area and area models considered here.<sup>8</sup> The household-model results, however, are substantially less accurate for predicting 2015 municipal poverty than the official 2010 census-based estimates. This correlation may be overstated because data from the 2010 census are used to generate both the 2010 and 2015 estimates. Nonetheless, this finding underscores the value of regular census data collection in this context and the possible importance of designing surveys to include all target areas if successful small area estimation is one goal of the survey.

While predictions that combine survey and satellite data are not always more accurate than older census-based poverty maps for predicting current poverty rates, they greatly improve upon estimates solely based on survey data and are a viable second-best solution in settings where recent census-based estimates do not exist, which is a common situation in much of the developing world. The relative accuracy of estimates produced from survey and geospatial data, compared with older census-based estimates, depends on several context-specific factors. These include the pace of change in regional patterns of poverty since the previous census, the size of the sample, and the

> 8 More complex and sophisticated versions of the area and sub-area levels, such as those that transform the dependent variable, may improve on the accuracy of the simple versions considered here.

7
Investment is expected to rise in Latin America as the region emerges from the shadow of the debt crisis of the past decade and builds on the stabilization and structural gains of recent years, and in South Asia as that region makes a break from inward-looking policies and pervasive govermment controls that shackled private sector growth in the past. Over the next ten years, total investment in these two regions could average around $400 billion and $125 billion per annum, again in constant 1994 dollars. Total investment in the Middle East and North Africa region is likely to average roughly the same as in South Asia, starting from a higher base than in South Asia but growing more slowly.

In Sub-Saharan Africa also, which at an average of about 16 percent of GDP has the developing world's lowest investment rate, some rise in investment is expected, supported by the economic reforms that a number of countries in the region are now undertaking. But total investment in Sub-Saharan Africa will remain small relative to other developing regions, likely averaging $75 billion or so annually over the next ten years (at 1994 prices), and accounting for less than 5 percent of total investment in developing countries over the same period.

_Supply of CapitaL_ The prospect of strong growth in investment spending in developing countries, however, does not imply a parallel large increase in their demand for foreign capital. The bulk of the prospective rise in investment in developing countries is expected to be financed from increases in these countries' own savings. Over the past two decades or so, average investment and saving rates in developing countries have generally moved closely together around a slightly rising long-term trend (Chart 4). Current World Bank projections are for the average gross national saving rate in developing countries to rise from 23 percent in the past decade to around 26 percent in the next, roughly matching the projected rise in their investment rate (Chart 5).

8
|**Table A31: Vanderbilt University**<br> <br>|**Americ**|**as Ba**|**romet**|**er (VA**|**B)**||||
|---|---|---|---|---|---|---|---|---|
|**Data Provider**<br>Vanderbilt University|||||||||
|**Description**<br>United States university|||||||||
|**Website**<br>www.lapopsurveys.org|||||||||
|**Data Source**<br>Americas' Barometer|||||||||
|**Type**<br>Survey|||||||||
|**Respondents**<br>Households|||||||||
|**Frequency**<br>Bi-annual since 2004|||||||||
|**Coverage**<br>Sample of Latin American co|untries||||||||
|**Public Access**<br>Some country-level aggregat<br>available via subscription|es are fr|eely a|vailabl|e, the r|est of|the d|ataset is||
|**Description**<br>The Americas Barometer i<br>values and behaviors in th<br>households in Latin Americ<br>Politics and Institutions, So<br>Issues.|s an effo<br>e Amer<br>a with q<br>cial Poli|rt by<br>icas u<br>uestio<br>cies, C|LAPO<br>sing c<br>ns on<br>ivic|P to m<br>ommon<br>areas s<br>Culture,|easu<br>que<br>uch a<br>and|re de<br>stionn<br>s Dem<br>other|mocratic<br>aires to<br>ocracy,<br>Current||
||2007|2006|2005|2004<br>|2003|2002|2000<br>1998|1996|
|**Voice and Accountability**|||||||||
|Trust in Parliament|X|X|X|X|..|..|..<br>..|..|
|Satisfaction with democracy|X|X|X|X|..|..|..<br>..|..|
|**Political Stability and Absence of Violence**|||||||||
|NA|..|..|..|..|..|..|..<br>..|..|
|**Government Effectiveness**|||||||||
|NA|..|..|..|..|..|..|..<br>..|..|
|**Regulatory Quality**|||||||||
|NA|..|..|..|..|..|..|..<br>..|..|
|**Rule of Law**|||||||||
|Trust in Supreme Court|X|X|X|X|..|..|..<br>..|..|
|<br>Trust in Justice system|X|X|X|X|..|..|..<br>..|..|
|<br>Trust in Police|X|X|X|X|..|..|..<br>..|..|
|Have you been a victim of crime?|X|X|X|X|..|..|..<br>..|..|
|**Control of Corruption**|||||||||
|<br>Frequency of corruption among government officials|X|X|X|X|..|..|..<br>..|..|
|**Country coverage**|**22**|**20**|**11**|**11**|**..**|**..**|**..**<br>**..**|**..**|
|**Year of Publication**|**2006**|**2006**|**2004**|**2004**|**..**|**..**|**..**<br>**..**|**..**|

69
# **A Additional Figures**

Figure A1: Informality Rates-Latin America and the Caribbean <!-- Start of picture text --> Chile UruguayCosta Rica BrazilVenezuela ArgentinaEl Salvador ColombiaDom. Rep. Mexico PeruGuatemala Nicaragua Bolivia Paraguay<br>80<br>60<br>40<br>Informality Rate<br>20<br>0<br><!-- End of picture text --> _Notes:_ This figure plots informality rates across countries from Latin America and the Caribbean. The data source is the online appendix from Ulyssea (2018) that uses data from _SEDLAC_ , an initiative from the World Bank and Universidad Nacional de la Plata. Informal workers are defined as those without social security. The orange line represents the average informality rate of countries from the OECD. The figure shows that informality rates in LAC are very high, and even within the region, Mexico is one of the countries with the highest informality rates.

Figure A2: Firm size and Productivity Distribution-Economic Census 1999 <!-- Start of picture text --> 1 4 16 64 256 1024 4096 16384 65536 -5 0 5 10<br>Firm Size Sales per worker<br>Legal and informal Illegal and informal Legal and informal Illegal and informal<br>Mixed Legal and formal Mixed Legal and formal<br>(a) Firm size (b) Productivity<br>.6<br>.4<br>.3<br>.4<br>.2<br>Density function .2 Density function<br>.1<br>0 0<br><!-- End of picture text --> _Notes:_ This figure plots the firm size and productivity distribution for the four different categories of firms: 1) Legal and informal 2) Illegal and informal, 3) Mixed, and 4) Legal and formal. I use the 2004 economic census. Panel (a) plots the firm size distribution and panel (b) the productivity distribution. Firm size is measured as the number of workers, and productivity as the logarithm of sales per worker.

1