rafmacalaba/datause-encoder-data huggingface dataset:
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Black, B., R. Kraakman, A. Tarassova, 2000. Russian privatization and corporate governance: What went wrong? _Stanford Law Review_ , 52:1731-1808.

Bliss, C. and R. Di Tella. 1997. Does Competition Kill Corruption? _The Journal of Political Economy_ 105(5): 1001-1023.

Bo and Rossi, 2007. Corruption and inefficiency: Theory and evidence from electric utilities. _Journal of Public Economics._ 91(5-6):939-962.

Boehm, F. and J. Polanco. 2003. Corruption and privatization of infrastructure in developing countries. Transparency International Working Papers. www.transparency.org.

Calederón, C. and L. Servén. 2003. Latin America’s infrastructure in the era of macroeconomic crises. In W. Easterly and L. Sérven (eds): _The limits of stabilization: infrastructure, public deficits and growth in Latin-America._ Stanford University Press and World Bank.

Cave, M. and P. Crother. 2004. Co-ordinating regulation and competition law _ex ante_ and _ex post._ In: _The pros and cons of antitrust in deregulated markets._ Swedish Competition Authority Cavill, S. and M. Sohail, 2007. Accountability Arrangements to Combat Corruption: Literature Review mimeo, Loughborough University.

Celentani, M. and J. Ganuzab, 2002. Corruption and Competition in Procurement, _European Economic Review_ , 46 (7): 1273-1303.

Clarke, G.R.G. and L.C. Xu. 2004. Privatization, competition, and corruption: how charactersitics of bribe takers and payers affect bribes to utilities. _Journal of Public Economics_ , 88:2067-2097.

Comte, O., A. Lambert-Mogiliansky and T. Verdier 2005. Corruption and Competition in Procurement Auctions, _RAND Journal of Economics,_ 36 (1): 1-15.

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Cook, C.C., T. Duncan, S.Jitsuchon, A.Sharma, W.Guobao 2005. _Assessing the impact of transport and energy infrastructure on poverty reduction_ . Asian Development Bank, Manila.

Crampes, C. and A. Estache. 1997. Regulatory tradeoffs in designing concession contracts for infrastructure networks. Policy Research Working Paper 1854, Washington D.C.: The World Bank.

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Davis, J. 2004, Corruption in public service delivery: experience from South Africa’s water and sanitation sector. _World Development_ 32(1): 53-71.

De Soto, H. 1989, _The other path_ , New York: Harper and Row.

19
<!-- Start of picture text --> 0.0000800% -<br>0.0000700% - environmental sustainability<br>0.0000600% -<br>0.0000500% -<br>0.0000400% -<br>aaa economic + debt sustainability<br>social sustainability<br>0.0000200% -<br>0.0000100% -<br>0.0000000%<br>1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text -->
# School Costs, Short-Run Participation, and Long-Run Outcomes: Evidence from Kenya

David K. Evans World Bank Mũthoni Ngatia World Bank JEL codes: O15, I21, I22 Keywords: Education, economic development, cost reduction, school uniforms, long-term impacts Acknowledgments: The authors thank David Blackburn, Mariana Colacelli, Pascaline Dupas, Deon Filmer, Caroline Hoxby, Lawrence Katz, Edward Miguel, Owen Ozier, Seema Jayachandran, Bryce Ward, and participants at various seminars for comments. They also thank to Pauline Ambani, Charles Kesa, Franklin Makokha, Hellen Masambu, Hellen Mukanda, Caroline Nekesa, Rachel Podolsky, Adina Rom, Evelyne Ruto, Maureen Wechuli, and Joseph Wasikhongo for data gathering, programing, and insight into program administration. Special thanks to Michael Kremer for supporting this work at every stage. The authors can be reached at Evans (devans2@worldbank.org) and Ngatia (mngatia@worldbank.org).
where _Vnimω_ is the indirect utility of worker _ω_ if he/she chooses transportation mode _m_ to travel from _n_ to _i_ . This is the classic framework that Berry (1994) studies. The parameter _δ_ measures the sensitivity of the decision of the worker/consumer to the average time she spends on moving across locations.<sup>43</sup> The parameter _γm_ captures preferences for transportation mode _m_ relative to a baseline mode; in my case, I normalize _γ_ bus to zero. For example, _γ_ car captures preferences for car relative to buses, which can include the price of a car, or the stress of driving in a complicated city such as Mexico City. The variable _ψ_ is common to all transportation modes for worker/consumer _ω_ within group _g_ and has a distribution function that depends on _λ ∈_ (0 _,_ 1). This latter parameter measures the correlation of errors within each nest. If this parameter is zero we are in the standard multinomial logit case. Finally, _εnimω_ is an idiosyncratic shock to worker _ω_ of choosing _m_ . The error term of this equation is _ψnigω_ + (1 _− λg_ ) _εnimω_ which is drawn from an extreme value-type I distribution.

Table B6 shows the main result after estimating the nested logit specification. The first column reports the results for commuting, and the second column reports the results for trade trips. I obtain a value for _δd_ of -0.009, which is consistent with previous findings from the literature (Ahlfeldt et al., 2015). The point estimate for _δτ_ is -0.013, which is also consistent with the literature. On the other hand, in terms of preferences, when people go to work, the most preferred transportation mode is car, whereas, when they travel to restaurants or retail shops, the most preferred transportation mode is walking. The last two rows report the average iceberg commuting and trade costs across locations in Mexico City before and after the transit shock. On average, after Line B of the subway opens, commuting costs drop by 4.7%, and trade costs by 4.5%.<sup>44</sup>

# **5.2 Commuting and Trade Elasticities**

**Commuting Elasticities:** To estimate the commuting elasticities, I use the 2015 Intercensal Survey. In this survey, workers report the municipality of their residence and workplace, and I am also able to define formal and informal workers by using employment and social security information. From the model, it is easy to derive the following gravity equation relating commuting flows across municipalities and iceberg costs:

> 43I also estimate a heterogeneous _δ_ between the formal and informal sector for the consumption trips. However, I do not find significant differences between the two coefficients, _δ_ I = _−_ 0 _._ 0128, and _δF_ = _−_ 0 _._ 0114. These results are available upon request. However, I cannot distinguish between the formal and informal sectors for the working trips since I construct the commuting flows using the 2015 Intercensal survey.

> 44I restrict the sample to trips that only use one transportation mode or two transportation modes + walking.

26
and Recovery (2011), Law 24/2007 provides the framework for how disasters are handled and the responsibilities of central and local governments. In general, minor disasters are handled by local governments through their budgets, whereas any disaster deemed a national disaster or a disaster of national importance will be financed by the central government. In terms of timing and budget appropriation, The Global Facility for Disaster Reduction and Recovery (2011) points to problems arising when disasters happen “outside” of season. It is our understanding that additional resources cannot be allocated outside of year-end and mid-year budget reviews. This can lead to situations as mentioned above, when the tsunami struck after the 2005 budget had already been approved, and hence changes to the budget could not happen till mid-year.<sup>12</sup> The disaster funding is split into three phases: the response phase immediately after the disaster, then the recovery phase typically being the period three to six months post-disaster and finally the reconstruction phase. The central government assists in all three phases when a disaster is deemed a national disaster or when the costs go higher than what the local governments can afford. It is noted that what constitutes a national disaster and not is not clearly defined. For example the Merapi eruption in 2010 was not declared a national disaster, but the central government did support local governments in both response and recovery phases.

# **4.3.1 Sector Results**

The first sector results are presented in the left panel of Table 5, which shows the coefficients

> for the sector regressions.<sup>13</sup> Each column presents the coefficient for each disaster by sector.

> 12This seems to clearly be the case for the central government, but it is not clear if this holds all the way down to local districts.

> 13Only main sectors are shown. For full results, see Appendix C.

17
income, the ability to finance education may also be higher for parents involved in non-farm activities.<sup>37</sup> Another important factor is that educated parents may be more effective in providing homework help and a home environment conducive to learning. However, it is also possible that resources are primarily substitutes for parental education in producing children’s schooling. For example, higher income may enable the parents to buy the required educational inputs (such as better schools, tutors etc.). If the market for educational inputs is well developed, low educated non-farming parents may be able to offset some of the disadvantages of their own educational deficit (inability to help with homework, for example). Also, the relation between a parents education and the time devoted to children may not be monotonic. A highly educated parent may not have time to spend with his kids, especially given the higher price of labor in the market. This may create a negative correlation between direct educational inputs provided by a parent at home and his labor market opportunities, especially at the right tail of schooling distribution. Table 6 reports evidence on the existence and nature of interaction between parents education and non-farm occupation using alternative econometric approaches.

Column (1) in Table 6 reports OLS estimates of the parameter _β_ 3 in equation (3) which shows the effect of the interaction between two dummies: more than primary schooling and non-farm occupation. The evidence across gender and generations in general suggests the lack of a significant interaction effect; out of the four cases, only in the case of fathers the effect is statistically significant at the 10 percent level. More important, the sign is negative in this case, implying substitutability, rather than complementarity. One might however have reservations about this simple test, as the conclusions may be affected by the linearity in parameters assumption.

> 37In our rural survey data for parents-children sample, among the low educated nonfarmers, 29 percent have high skilled nonfarm jobs, while among the parents with more than primary schooling 43 percent are engaged in skilled nonfarm economic activities.

35
<!-- Start of picture text --> Contract value & value share of Green Public Procurements<br>1600eo eeCT 030<br>vol<br>= 1200 2<br>=2<br>21000 ee is<br>oDuw =<br>© ago 5<br>~ , PL 5<br>5 1) ae<br>5 0.102<br>ad5 i FTw<br>2 400 G<br>. a ; ; ; ; ; ; ; ; a<br>ft] 0.00<br>2011 2012 2013 2014 2015 2016 2017 218 2019<br>Contract year<br>Contract number & volume share of Green Public Procurements<br>—— 030<br>2500eeCT<br>025<br>ha<br>2000 2<br>0.20<br>= E-<br>5 1500 #<br>5 O15 a<br>g c<br>Ee iw<br>s 1000 2<br>= ‘ 9.1%, 0106<br>Fr<br>J + if to<br>: - /-<br>ft] 0.00<br>2011 2012 2013 2014 2015 2016 2017 218 2019<br>Contract year<br><!-- End of picture text -->
# Result 1: Using Only Hong Kong SAR as Control City.

We begin by estimating the causal impact of a strict lockdown policy on traffic congestion in Bogotá, New Delhi, New York City, and Paris. We use traffic conditions in Hong Kong SAR as well as daily temperature and precipitation data for the relevant treated cities as well as for the control series. The control covariates of the regression sub-model in the BSTS are unlikely to be directly affected by the Covid-19 restriction policies in the treated cities, as Hong Kong SAR did not implement any lockdown measure and Covid-19 cannot plausibly explain short-term variations in the weather variables.

As shown in both Figure 3 and Panel A of Table 1, we find that Covid-19 lockdown policies drastically reduced daily traffic congestion—as measured by the logarithm of the traffic congestion index—in each of the four selected cities that implemented strict stay-at-home orders during the pandemic. Overall, the BSTS model fits very well the trajectory of the traffic congestion index during the pre-lockdown period in all cities. For example, the synthetic control series using Hong Kong SAR and the weather series did a remarkable performance of picking up nearly all episodes of spike and dip of the pre-lockdown traffic conditions, especially in New Delhi and Paris (Figure 3).

There is also an unambiguous divergence between observed traffic conditions and the counterfactual predictions following the implementation of the Covid-19 lockdown policies, with much less traffic congestion in the post-lockdown period. More specifically, as shown in Panel A of Table 1, the lockdown-induced reduction in the traffic congestion index varies between 68% and 81% in comparison to a counterfactual _business-as-usual_ scenario in the treated cities. Bogotá and Paris exhibit the largest decline in relative congestion with 81% and 79.5%, respectively, followed by New Delhi (-77%) and New York City (-68%). These estimated causal effects are substantial and statistically significant, according to the 95% posterior confidence interval reported in brackets, which excludes 0 for all treated cities. Furthermore, the probability of obtaining this effect by chance is very small (the one-sided tail-area probability p-value is less than 0.01 for all cities), 21
51 _we_ **_(had]_** _good growth in the South, even In parts of Latin America when they ware following the most awful policies._ _Now it may well be that with less good growth in the North but wlth better policies In the de7eloping countries, they can grow just as well if not better. The world is changing. It may well be that (developing countries] will become the dynamic force which is helping us along, not necessarily immediately, but if considering twenty years out, it seems perfectly possible._ _MR. BANETH: I would like the IECAP representatives to answer on the specific technical questions on the investment ratios and the linkages in the projections._ _MR. FARDOUST: The historical figures on investment ratios for the developing countries, partlcularly for the 1980s, are heavily influenced by the performance of China and India._ _For many countries, however, there has been an extraordinary decline in the investment ratios. The disaggregated figures show a collapse in Latin America and Sub-Saharan Africa in the early 1980s, while, by and large, the East Asian countries -- even if one excludes China -- maintained their investment ratios. Our baseline projections show that_ **_If_** _real Interest rates begin to decline and world trade and commodity prices recover in the next few years, both imports and investment ratios in many developing countries will begin_ **_to_** _improve in the 1990s._

**_More on the North-South linkage..._**

_MR. ARMINGTON: On_ **_the_** _question of the linkage between export volume growth of the developing countries and_ **_GDP_** _growth of the Industrial countries, I have a couple of points to make. Basically, our projections of developing country exports come from our socalled central case, which essentially is a report on the Country Economists' Survey [the long-term survey of the Bank's country specialists.]_ _Looking at historical data, as Andrew Steer pointed out, our baseline projections for export volume growth of 4.2 percent tper annum) from 1986 to 1995, for developing countries, looks a bit high_ **_developing_** _in relation to either their_ **_GDP_** _growth of 4.0 percent [per annum] or_ **_countries'_** _real GDP growth of industrial countries of 2.5 - 2.8 percent [per_ **_exports..._** _annum]. That is the point at issue here._ _That figure of 4.2 percent essentially is the aggregation of what the country economists say is, in effect, their export supply forecast, given the policy sets that they are assuming. It is also heavily weighted by_ **_the_** _export performance of the NIEs._ WP/BOLThO/SF:1g
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30

# 11: Price incentives Soybeans

<!-- Start of picture text --> Trade liberolization vs Price Guarantee<br><!-- End of picture text --> <!-- Start of picture text --> 12<br>Ii~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~I<br>8<br>:194 7 716 717 7'8 79 80 811 812 8 4<br>08 0 price guarantee ' + tradje lib. ///\<br>0~~ 1:Production Soybeans \<br>Trade liberalization vs Price Guorantee<br>0 7 /J1<br>06<br>0.5 -<br>04<br>0 3<br>197t4 75 7 6 77 78 79 80 81 82 83 84 85<br>fears<br>0 = price guarantee + trode lib<br><!-- End of picture text -->
# The Effects of Childcare on Women and Children: Evidence from a Randomized Evaluation in Burkina Faso<sup>_∗_</sup>

Kehinde F. Ajayi<sup>_†_</sup> Aziz Dao<sup>_‡_</sup> Estelle Koussoub ́e<sup>_§_</sup> _Keywords_ : gender, labor, welfare, childcare, early childhood development _JEL codes_ : J16, J13, I38, O15

> _∗_ This paper is a product of the World Bank Africa Gender Innovation Lab, Office of the Chief Economist, Africa Region. We thank David Evans, Markus Goldstein, Rachael Pierotti, Christophe Rockmore, colleagues from the Africa Gender Innovation Lab, and seminar participants from the Center for Global Development and the Center for the Study of African Economies 2022 Conference for thoughtful comments. We are grateful to Rebekka Grun, Gilberte Kedote, Florence Kantiono, Roland Berehoudougou, Marie Bernadette Kabre, L’Institut de Formation et de Sp ́ecialisation des Personnels de la Petite Enfance (IFSPPE), and the PTR-HIMO team for their collaboration on this project. This work was funded by the Early Learning Partnership and the Umbrella Facility for Gender Equality multi-donor trust funds. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent. This study was registered on the AEA RCT Registry (AEARCTR-0006811). The Institutional Review Boards at the Health Media Lab, Washington DC and Comit ́e d’Ethique Institutionnel pour la Recherche en Sciences de la Sant ́e (CEIRES), Burkina Faso approved this project.

> _†_ Center for Global Development, 2055 L Street NW, Washington, DC 20036. Email: `kajayi@cgdev.org` .

> _‡_ The World Bank, 1818 H Street NW, Washington, DC 20433. Email: `adao1@worldbank.org` .

> _§_ The World Bank, 1818 H Street NW, Washington, DC 20433. Email: `mkoussoube@worldbank.org` .
# Outline

|Introduction..........|1|
|---|---|
|II.<br>From Corporate Finance to National Finance .............................................|2|
|III.<br>Why is Risk Management so Important? ..............................................|3|
|Asset Management .................................<br>............................|3|
|Liability Management ..............................................|3|
|_WV._National Risk Management ..............................................|7|
|National Balance Sheet ..............................................|9|
|Managing Financial Stability: Some Basic Rules ........................................|10|
|(1) Establishing Fiscal Discipline and Price Stability ...............<br>...............|10|
|(2) Asset Diversification ..............................................|11|
|(3) Building Stable Financial Structures .............................................|12|
|Debt versus Equity .............................................................|12|
|Development of Diversified Financial Instruments<br>.....................................|14|
|Management of Foreign Exchange Risks ..............................................|15|
|(4) Institutional and Legal Framework for Bank Supervision .........<br>..........|16|
|_(5)_Financial Sector Liberalization and Risk Management ...........<br>............|16|
|V.<br>Summary and Conclusions ...........................................|17|
|Table 1: Growth Performance of High Debt||
|vs. Low Debt Countries (1971-1990) ............................<br>................|_5_|
|Table 2: Export Concentration, Growth and Stability of Economies of<br>Ghana and Malaysia (1965-1989) .............................................<br>.....|6|
|Table 3: Production Diversification and Growth Performance||
|of Developing Countries (1965-1989) .........................<br>....................|7|
|Table 4: Export Diversification and Growth Performance||
|<br>of Developing Countries (1965-1989) ................................<br>.............|7|
|Figure 1: Private Investment and GDP Growth (1970-1989) ...................................|13|
- 21 -

For further concreteness, consider the concocted 'regression': Y = 46 + 50x, a =2083 and (20) where forecast x = 2, V[x] = 2 and where unconditional variance (SE) is 2341 (48.8) but conditional variance (SE) is 8141 (90.2), illustrating that the standard error of the conditional forecast is nearly double that reported traditionally once uncertainty in X is accounted. The present example is based on the 1982 forecast for cocoa price in 1990 (World Bank 1982).

= In conditional variance terms, unconditionally this was 48.8/246 .20, combined with the cv(x) = 2o5/2 = .71 results in a conditional cv = 90.2/246 = .37.

The practicability of such an approach hinges on the estimation of cov(Xi,X;), since it has already been demonstrated that all the other elements of the computation can be handled (see Annex D of World Bank, 1982, Volume I of Report No. 814/82).

To address some further possibilities, consider first the univariate case of x in the 'regression' above. The (mean) forecast value of x is 2. One might simply 'have' a subjective cv of .71 as illustrated. Alternatively, one might resort to a distributional representation, such as the convenient triangular distribution.

The triangular distribution is defined by three parameters, A, M, B, (range and mode). For the present, suppose that these pararmeters are 0, 2, 4--