rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 9. Cross section 1984-1995, 2SLS.**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||2SLS|2SLS|2SLS|2SLS|2SLS|2SLS|
||pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|
|Constant|-3.785|-6.569|-6.276|-10.275|-3.602|3.534|
||(2.091)*|(3.047)**|(1.999)***|(2.832)***|(2.562)|(17.124)|
|GDPpc71|-0.000|0.000|-0.000|0.000|-0.000|0.003|
||(0.000)|(0.001)|(0.000)|(0.000)|(0.000)|(0.004)|
|prim_enrol8495|-0.011|0.030|0.017|0.023|-0.052|0.222|
||(0.022)|(0.045)|(0.023)|(0.020)|(0.032)|(0.285)|
|Invt_GDP8495|0.245|0.180|0.248|0.264|0.286|0.122|
||(0.070)***|(0.083)**|(0.083)***|(0.078)***|(0.087)***|(0.307)|
|Govstab8495|0.019|0.511|0.190|0.935|0.387|1.065|
||(0.395)|(0.924)|(0.417)|(0.527)*|(0.497)|(2.511)|
|Corrup8495|-0.130|-0.303|0.003|0.065|-1.030|-1.900|
||(0.368)|(1.085)|(0.407)|(0.359)|(0.591)*|(3.664)|
|main8495|0.024||||0.047||
||(0.017)||||(0.021)**||
|egc8495||-5.502||||-38.323|
|||(14.823)||||(51.504)|
|rail8495|||1.393||||
||||(1.892)||||
|roads8495||||-1.789|||
|||||(1.217)|||
|tel_faults8495|||||0.028||
||||||(0.022)||
|elec_loss8495||||||-1.380|
|||||||(1.764)|
|Observations|48|47|44|40|43|37|
|Wu-Hausman F<br>test, p-value|0.29|0.77|0.14|0.29|0.33|0.05|

Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Instruments: 1971 Infrastructure physical stock, 1971 share of agriculture in GDP, 1971 population density.

29
# **REFERENCES AND BACKGROUND MATERIAL**

# **Cross-cutting**

- Agustina, Cut Dian R.D. 2007. Experience with Reconstruction Finance Tracking Systems, Jakarta: Technical Note.

- Baltaci, Mustafa and Yilmaz, Serdar. 2006. Keeping an Eye on Sub-national Governments: Internal Control and Audit at Local Levels. WBI Working Papers. World Bank. Washington D.C

- Campos, Jose Edgardo and Pradhan, Sanjay. 2007. Exploring Corruption in Public Financial Management, in Taming the Elusive Beast. World Bank. Washington D.C.

- Cliffe, Sarah and Manning, Nick. 2006. Building Institutions after Conflict. Washington D.C.

- Collier, Paul., Hoeffler, Anke. 2004. Greed and grievance in civil war," Oxford Economic Papers, Oxford University Press, vol. 56(4), pages 563-595

- Collier, Paul., Hoeffler, Anke., and Soderbom, Mans. 2006. Post-Conflict Risks. Center for the Study of African Economies. CSAE WPS/2006-12. Department of Economics, University of Oxford

- Demeter, Katalin. 2007. Disaster Governance/Disaster Management Systems. Discussion Paper prepared for “Responding Effectively to Crisis and Emergencies”  Workshop, May 2-3, 2007. Washington D.C.

- DFID. 2005. A Platform Approach to Improving Public Financial Management, Policy Discussion Briefing (July)

- Dorotinsky, William and Pradhan, Shilpa. 2006. Exploring Corruption in Public Financial Management in _The Many Faces of Corruption: Tracking the Vulnerabilities at the Sector Level_ , Edited by Campos, J. Edgardo and Pradhan, Sanjay. World Bank. Washington D.C.

- Economic Commission for Latin America and the Caribbean (ECLAC). 2003. Handbook for Estimating the Socio-Economic and Environmental Effects of Disasters, New York/Washington D.C.

- Fengler, Wolfgang. 2007. Tracking and Measuring Financial Flows after Disasters: Establishing a Reconstruction Expenditure Tracking Analysis Methodology (RETAM), PREM-Note, World Bank, Washington D.C.

- Glenn Ware, J. Edgardo Campos, J. Oliver Moss, and Gregory Noone. 2006. Corruption in Public Procurement: A Perennial Challenge, In The many faces of corruption: Tracking vulnerabilities at the sector level. Edited by Jose Edgardo Campos and Sanjay Pradhan. Washington D.C.: The World Bank Publication, Annex 1- Annex A: The Special Case of Emergency Procurement: A Justified Exception or an Avenue for Corruption?

- PEFA. 2005. Public Expenditure and Financial Accountability (PEFA): Public Financial Management Performance Measurement Framework. PEFA Secretariat-World Bank. Washington D.C.

31
mandate certain key terms but also the format in which these terms are presented to encourage comparison shopping and improve financial wellbeing. It is encouraging that, in recent years, some regulators have begun mandating standardized formats.<sup>12</sup> Interestingly, the standardized format tested increased the probability that individuals choose the best credit product by a factor of three, relative to the increase in probability between financially literate and illiterate individuals. More importantly, the standardized format seems to “democratize” financial decision-making as it is particularly effective for individuals that are financially illiterate. The effects are however concentrated in credit products rather than savings. The lack of impact in savings is concentrated in Mexico, where the simplified format failed to disclose the total yield of the savings account in pesos, which may indicate that total yield calculations can be a useful requirement for disclosure rules to improve consumers’ ability to understand the cost or return of the savings account.

The laboratory setting approach taken also suggests an effective mechanism to test the design of financial disclosure initiatives. This approach is not new. For example, the Consumer Financial Protection Bureau and the Federal Reserve of the U.S. constantly survey and test financial consumers on how they understand information, which information they think is useful, and finally how the information can be more effectively conveyed (Kroszner, 2007). Interestingly, Online Appendix Table AO3 shows that among individuals who showed up for a session in Mexico, those that participated in the laboratory experiment (compared to those that were randomly turned away due to lack of space) were more likely to report contracting the session’s financial product in the following 6 months. Owning a business and being a male were also positively correlated with the self-reported likelihood of contracting the financial product.

In addition, regulators in Mexico and elsewhere are requiring lenders to send detailed product information in a machine-readable format so they can be downloaded by startups like ComparaBien, ComparaGuru, and rocket.la which provide timely comparative

> 12 For an emerging market example of such mandatory formats, see Bank of Ghana mandatory disclosure format for loan products: https://www.bog.gov.gh/supervision-a-regulation/consumer-information/preagreement-truth-in-lending-disclosure-statement

21
across the income spectrum actually benefit from higher agricultural commodity prices. Finally, Nicita, Olarreaga, and Porto (2013) examine the presence of a pro-poor bias in trade protection of six Sub-Saharan African (SSA) countries, Burkina Faso, Cameroon, Cˆote d’Ivoire, Ethiopia, Gambia, and Madagascar. Their model includes consumption effects, agricultural income effects, and labor income effects. They find that trade policies tend to be biased in favor of poor households because protection increases the agricultural prices of goods that are sold by African households, and this effect dominates the impacts on both expenditures and wages.

# **3.4 Spillovers**

Broadly speaking, spillovers are the impacts of a change in market _i_ on the activity in market _j_ . There are two types of spillovers. Production linkages occur when the expansion of a sector affects upstream activities (backward linkages) or downstream activities (forward linkages). Expenditure linkages occur when the increase in income due to the expansion of a sector raises the demand for outputs and thus the derived demand for inputs in other sectors. Given our focus on commodities, we emphasize here spillovers in agriculture. The large literature on the topic uncovers two main regularities. First, the linkages are strong in rural areas because households earn a large share of their income from and spend a large share of their total expenditures in goods produced locally. Second, both production and expenditures spillovers are important (Mellor and Johnston, 1984; Delgado, 1996; Harriss, 1987). Evidence on spillovers in agriculture in Latin America can be found in the regional study by De Ferranti et al. (2005). They report that while the share of the primary sector in GDP is 12 percent, there are massive spillovers to the rest of the economy. In fact, the multiplier factor from the primary sector to national GDP is 0.22 so that, for instance, a one percent growth of the rural natural resource sector is associated with an increase of 0.22 percent increase in national GDP (much higher than the 0.12 percent increase to be expected from the sector’s share of GDP). The multiplier for the income of the poorest households is even higher at 0.28.

Note that spillovers can be interpreted as general equilibrium effects, but they are not 14
Figure 7: Political Influence Concentration Curve, example <!-- Start of picture text --> Namibia (2004)<br>Senegal (2011)<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Share of individuals from the top<br>1<br>.9<br>.8<br>.7<br>.6<br>.5<br>.4<br>.3<br>.2<br>Share of individuals from the bottom<br>.1<br>0<br><!-- End of picture text --> index for all countries in our sample. There is no clear pattern among the two indices: political concentration may be very high or very low for each level of poverty. Nevertheless, we can see some regional patterns. Almost all countries in Europe and Central Asia and North Africa and the Middle East are found in the bottom left quadrant: these are countries where both the poverty level and the political influence concentration indices are relatively low with respect to their averages (represented, respectively, by the vertical line at about 0.35 and the horizontal line right above 0.7 in Figure 8). The largest share of countries in Sub-Saharan Africa are instead mostly positioned in the upper-right quadrant, where both the political influence concentration index and the headcount poverty index are higher than average. Furthermore, almost all countries in Latin America have lower poverty rates than average, but higher political influence concentration.

One piece of empirical evidence is worth noticing. Although theory defines the lower bound for _I_<sup>Π</sup> being zero when the political power is equally shared among all individuals in the society, the data show that almost all countries have a level of _Political Influence Concentration Index_ higher than 0.5 (the only two exceptions are and the Czech Republic, with 0.498 and Ukraine with 0.493).

18
separately, i.e. users of formal banking, other formal and informal financial services and, second, considering the most formal strand. In the latter case, one takes into account that many users of formal financial services also use informal financial services and so focuses on the most formal form of financial service. In the following, we will follow the first approach, focusing on (i) users of formal banking services, (ii) users of informal financial services (who could also use formal banking services) and (iii) individuals excluded from formal and informal services.

Figure 2 shows that, on average, women are less likely to use formal financial services than men, while gender differences in the use of informal financial services vary across countries. Here, we graph the share of surveyed in each country that (i) uses formal banking services, (ii) uses informal services, and (iii) is excluded from any financial service, separately for men and women. All observations are weighted according to their representativeness.

Panel A shows that women are less likely to use formal banking services across all 11 surveys, although the gender differences vary across countries. In Botswana (2004), the gender gap is less than two percent and not significant, whereas in Kenya (2009), the gender gap is 11 percent, with 32 percent of men using formal banking services, but only 21 percent of women. The Panel A graphs also show the large cross-country variation in use of formal banking services, documented elsewhere, with almost 70 percent of surveyed in South Africa using formal banking services, while only 17 percent using formal banking services in Zambia (2005).

The Panel B graphs show that the gender gap in the use of informal financial services varies across countries. In Kenya (2006 and 2009), South Africa (2008) and Tanzania (2006 and 2009), women are more likely to use informal financial services than men, while the reverse holds in Namibia and Rwanda. The graphs also show the wide-spread variation in the 19
|Knowledge EconomyProject|Arabela Sena Aprahamian|
|---|---|
|Knowledge EconomyProject|Arabela Sena Aprahamian|
|Knowledge EconomyProject|Arabela Sena Aprahamian|
|Knowledge EconomyProject|Arabela Sena Aprahamian|
|Knowledge EconomyProject|Arabela Sena Aprahamian|
|Knowledge EconomyProject|Arabela Sena Aprahamian|
|Consolidation Support Program PolicyGrant|Antonio S. Franco|
|Consolidation Support Program PolicyGrant|Antonio S. Franco|
|Consolidation Support Program PolicyGrant|Antonio S. Franco|
|AR APL1 Urban Flood Prevention and Drainage|John Morton|
|AR APL1 Urban Flood Prevention and Drainage|John Morton|
|AR APL1 Urban Flood Prevention and Drainage|John Morton|
|AR APL1 Urban Flood Prevention and Drainage|John Morton|
|AR APL1 Urban Flood Prevention and Drainage|John Morton|
|AR APL1 Urban Flood Prevention and Drainage|John Morton|
|AR APL1 Urban Flood Prevention and Drainage|John Morton|
|AR APL1 Urban Flood Prevention and Drainage|John Morton|
|AR APL1 Urban Flood Prevention and Drainage|John Morton|
|AR APL1 Urban Flood Prevention and Drainage|John Morton|
|AR APL1 Urban Flood Prevention and Drainage|John Morton|
|AR APL1 Urban Flood Prevention and Drainage|John Morton|
|AR APL1 Urban Flood Prevention and Drainage|John Morton|
|AR APL1 Urban Flood Prevention and Drainage|John Morton|
|AR APL1 Urban Flood Prevention and Drainage|John Morton|
|AR APL1 Urban Flood Prevention and Drainage|John Morton|
|Monglia:StrengtheningExpenditure Management Capaci|tyin Ulaan Saiyed Shabih Ali Mohib|
|CGAP Associate Networks with MFC - Pilot|Jeanette Thomas|
|CGAP Associate Networks with MFC - Pilot|Jeanette Thomas|
|CGAP Associate Networks with MFC - Pilot|Jeanette Thomas|
|CGAP Associate Networks with MFC - Pilot|Jeanette Thomas|
|CGAP Associate Networks with MFC - Pilot|Jeanette Thomas|
|CGAP Associate Networks with MFC - Pilot|Jeanette Thomas|
|Financial Sector Development PolicyLoan|Didier Debals|
|Financial Sector Development PolicyLoan|Didier Debals|
|Financial Sector Development PolicyLoan|Didier Debals|
|Financial Sector Development PolicyLoan|Didier Debals|
|Cyclone EmergencyRecoveryProject|Thakoor Persaud|
|Cyclone EmergencyRecoveryProject|Thakoor Persaud|
|Cyclone EmergencyRecoveryProject|Thakoor Persaud|
|Cyclone EmergencyRecoveryProject|Thakoor Persaud|
|Cyclone EmergencyRecoveryProject|Thakoor Persaud|
|Cyclone EmergencyRecoveryProject|Thakoor Persaud|
|Cyclone EmergencyRecoveryProject|Thakoor Persaud|
|MUNICIPAL SERVICES PROJECT|Elisabetta Capannelli|
|MUNICIPAL SERVICES PROJECT|Elisabetta Capannelli|
|MUNICIPAL SERVICES PROJECT|Elisabetta Capannelli|
|MUNICIPAL SERVICES PROJECT|Elisabetta Capannelli|
- Gabaix, X. (2011), ‘The Granular Origins of Aggregate Fluctuations’, _Econometrica_ **79** (3), 733–772.

**URL:** _https://ideas.repec.org/a/ecm/emetrp/v79y2011i3p733-772.html_

- Gamst, G., Meyers, L. S. & Guarino, A. J. (2008), _Analysis of Variance Designs: A Conceptual and Computational Approach with SPSS and SAS_ , Cambridge University Press.

- Grullon, G., Larkin, Y. & Michaely, R. (2019), ‘Are us industries becoming more concentrated?’, _Review of Finance_ **23** (4), 697–743.

- Guti ́errez, G. & Philippon, T. (2017), Declining Competition and Investment in the U.S, NBER Working Papers 23583, National Bureau of Economic Research, Inc. **URL:** _https://ideas.repec.org/p/nbr/nberwo/23583.html_

- IMF (2023), World Economic Outlook Update (April 2023): A Rocky Recovery, Technical report, International Monetary Fund.

- Jardet, C., Jude, C. & Chinn, M. (2023), ‘Foreign direct investment under uncertainty evidence from a large panel of countries’, _Review of International Economics_ **31** (3), 854–885.

**URL:** _https://ideas.repec.org/a/bla/reviec/v31y2023i3p854-885.html_

- Juvenal, L. & Monteiro, P. S. (2021), Risky Gravity, Discussion Papers 21/02, Department of Economics, University of York.

**URL:** _https://ideas.repec.org/p/yor/yorken/21-02.html_

- Mezzanotti, F. & Simcoe, T. (2023), Innovation and Appropriability: Revisiting the Role of Intellectual Property, NBER Working Papers 31428, National Bureau of Economic Research, Inc.

**URL:** _https://ideas.repec.org/p/nbr/nberwo/31428.html_

- OECD (2018), Market Concentration, Technical report, OECD Secretariat: issues paper for the hearing on market concentration.

- Philippon, T. & Gutierrez, G. (2018), ‘How eu markets became more competitive than us markets: A study of institutional drift’.

- Shekhar Aiyar, D. M. & Presbitero, A. (2023), ‘Investing in friends: The role of geopolitical alignment in fdi flows’, _CEPR Discussion Paper No. 18434_ pp. 89–169. **URL:** _https://cepr.org/publications/dp18434_

- Tukey, J. W. (1977), _Exploratory Data Analysis_ , Addison-Wesley.

United Nations (2023), World Investment Report, Technical report, United Nations.

- World Bank Group (2020), _Global Investment Competitiveness Report 2019/2020_ , number 33808 _in_ ‘World Bank Publications - Books’, World Bank Group. **URL:** _https://ideas.repec.org/b/wbk/wbpubs/33808.html_

15
below also shows how the vulnerability incidence varies between poor and non-poor when we vary the threshold above which households are classified as vulnerable.

**Figure 5**

<!-- Start of picture text --> Vulnerability Incidence<br>0 10 20 30 40 50 60 70 80 90 100<br>Vulnerability Threshold<br>Overall Poor Non-poor<br>Source: ELIM 2010 and authors'calculations.<br>The vertical line represents the chosen threshold for our vulnerability analysis.<br>100<br>80<br>60<br>40<br>20<br>Vulnerability Incidence (%)<br>0<br><!-- End of picture text --> At the regional level, average vulnerability also tends to be higher where poverty rates are higher, but again, there is no one to one mapping between poverty and vulnerability. Sikasso appears to be the region where both poverty and vulnerability are the highest in the country (Figure 6).

**Figure 6**

<!-- Start of picture text --> Poverty Rate and Vulnerability Incidence<br>By Region<br>Vulnerability Poverty<br>Source: ELIM 2010.<br>The horizontal line represent the average poverty (43.6%) rate and the aggregate vulnerability level (43.2%).<br>Kayes Koulikoro Sikasso Ségou Mopti Tombouctou Gao Kidal Bamako<br>80<br>60<br>40<br>20<br>Poverty Rate and Vulnerability Incidence (%)<br>0<br><!-- End of picture text --> From Figure 7 and 8, we see however, that the high vulnerability incidence in Sikasso is mostly a concern of the households who are already poor. Among households with levels of welfare above the poverty line, vulnerability is not higher in Sikasso than other regions. One implication is that to reduce vulnerability in a region like Sikasso, designing safety net 19
# TABLE OF CONTENTS

|I.<br>INT|RODUC|TION . . . . . . . . . . . . . . . . . . . . . . . . .|1|
|---|---|---|---|
|A.<br>B.|OBJE<br>METH|CTIVES<br>.1...............<br>ODOLOGY .<br>. . . . . . . . . . . . . . . . . . . . . . .|2|
|II.<br>OVE|RVIEW|OF BANK LENDING FOR DIVESTITURE . . . . . . . . . . . .|.<br>3|
|A.|RAT|IONALE FOR DIVESTITURE . . . . . . . . . . . . i. . . .|3|
|B.|MAG|NITUDE AND NATURE OF BANK LENDING FOR DIVESTITURE<br>. .<br>.<br>.|4|
|**III.**<br>AN|ASSES|SMENT OF EXPERIENCE . . . . . . . . . . . . . . . . . . .|.<br>7|
|A.|DIVE|STITURE OUTCOMES<br>. . . . . . . . . .<br>. . . . . . . . . .|.<br>8|
|B.|PRAC|TICAL ISSUES IN IMPLEMENTATION . . . . . . . . . . . . .|10|
||1.|Formulation of a Strategy: A Sector-Wide vs Case-by-Case<br>Approach<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.|10|
||2.|The Process of Divestiture<br>. . . . . . . . . . . . . .|13|
||3.|Managing Divestiture:<br>Institutional<br>.,Lrangements<br>and Capacity<br>. . . . . . . . . . . . . . . . . . . .|17|
||4.|Financing Divestiture ......<br>.. .. .. .. .<br>.<br>.|21|
||5.|Divestiture and Labor . . . . . . . . . . . . . . . . .|21|
|C.|SELE|CTED MANAGEMENT ISSUES FOR THE BANK . . . . . . . . . . .|23|
||1.|The Lending Instrument: Timing and Sequencing<br>. .<br>.<br>.|23|
||2.|Design of Divestiture Conditionality<br>. . . . . . . . .|24|
||3.|Monitoring and Evaluation<br>. . . . . . . . . . . . . .|25|
|IV. CONCLU<br>BIBLIOGRAPH|SIONS<br>Y|<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . .|2|

TABLES ANNEX

* Assistance in the preparation of this paper was provided by Polly Means.
and therefore cannot take into account the redistributive effect of de facto exemption of informal consumption.

Several key results emerge from Figure 8. First, our different optimal consumption tax scenarios achieve substantial reduction. The effects are policy inequality inequality large compared to estimates of the effect of current consumption tax policies, which suggest consumption taxes in developing countries achieve very little inequality reduction (0.3% reduction in Gini, row 1 of Panel B), despite taking into account reduced rates and exemptions. On the contrary, we find that when we take into account the de facto exemption of informal varieties, simply setting a uniform rate on all goods achieves a non-trivial inequality reduction (a 2.3% decrease in the Gini, row 1 of Panel A). Second, the amount of redistribution achieved by this scenario is still large when we compare it to a scenario with perfect enforcement in which governments optimally differentiate rates on food and non-food products (row 2 of Panel A). Comparing rows 1 and 2 of Panel A, we see that taxing only formal varieties achieves 75% of the inequality reduction obtained under the perfect enforcement and de jure rate differentiation scenario.<sup>44</sup> Third, introducing rate differentiation on top of de facto exemption of the informal sector further reduces inequality (row 3 of Panel A). Comparing rows 1 and 3, however, we see that simply setting a uniform rate on all goods achieves two-third of the redistribution obtained by optimally differentiating rates once we assume that only formal varieties can be taxed.

Finally, by comparing panel A to row 2 of Panel B, we find that our estimated effects of consumption tax policy on inequality have the same magnitude as the effect of the direct tax system (income taxes and social security) in developing countries.<sup>45</sup> We note however that the redistributive potential of direct tax systems in these countries is constrained because they only cover the small share of the workforce which is not selfemployed (Jensen, 2019). Direct tax systems that do not face this constraint have a much larger effect on inequality.<sup>46</sup>

> 44Optimally differentiating rates when all varieties are taxed reduces inequality substantially, despite the relatively small progressivity achieved by differentiating rates on food and non-food described in section 4.3. It occurs because this (unrealistic) scenario assumes the government can tax the entire consumption base, which yields much higher effective tax rates.

> 45Since direct taxes are generally considered most strongly suited for redistribution, our results are large in magnitude among tax policy impacts. They are, however, smaller in magnitude than the inequality reduction achieved in CEQ-countries by in-cash and in-kind transfers (Lustig, 2018).

> 46Using data from the OECD Income Distribution Database, we calculate that direct taxes in developed countries achieve a 11.2% Gini-reduction in inequality.

29