rafmacalaba/datause-encoder-data huggingface dataset:
48 If NEPA used these higher revenues to improve the quality of its transmission system, a further gain in efficiency might be realized. The suggested pricing schedule would offer additional benefits in the form of business and job creation because smaller firms, responsible for most job creation according to previous Bank research (Lee, 1985 and 1989), will respond favorably to lower electricity costs and higher quality electricity service. Neither higher tariffs as a source of revenue nor benefits to micro enterprises to spur job creation are nearly as important in Indonesia as they might be in Nigeria, so long as Indonesia's current economic boom is providing both revenues to government and jobs to workers.

ii) Private firms should be allowed to sell electricity to one another. This policy is already in place in Indonesia. It should be implemented in Nigeria. Large firms produce own electricity at lower marginal cost than small firms do. Allowing larger firms to sell electricity to smaller firms near them would improve economic efficiency. The efficiency gains from such a policy might well be enhanced if firms selling electricity to neighbors put competitive pressure on NEPA that resulted in improvements in NEPA electricity delivery. (Any attempts to regulate the prices on intra-firm sales, or on sales to the public utility, should be viewed skeptically in the light of Harberger's (1993) recent welfare analysis of electricity cogeneration and sale in Canada.) iii) The marginal costs of producing own electricity are much higher than the marginal costs of purchasing public power, and probably comparably higher than the utilities' marginal cost of producing that power. Embedded electricity production encroaches on firms' productivity because embedded production diverts resources away from primary output. Costly private provision of infrastructure certainly limits the growth and expansion of firms. Were NEPA and PLN efficient. shifting electricity production from embedded production to NEPA and PLN production would harness the utilities' much greater economies of scale. In the long run. firms would reap still larger benefits as they avoided the need to replace their
# **2. The evolution of prices and their relation with the import parity**

In the preceding paragraphs we described the evolution of market shares and retail outlets. In general, most of the complaints about competition in the Argentine gasoline market rely on the evolution of prices. It is apparent from figure 2 that gasoline prices in Argentina have a very different adjustment pattern to changes in crude oil prices than in the United States.

<!-- Start of picture text --> Figure 2<br>Gasoline Prices. Net of taxes. US and Argentina<br>so. . ._.<br>45<br>40<br>35<br>30 +,<br>25 t/<br>20 _>e<br>10<br>5<br>O ,,,, 1995 1996 1997 ............ 1998 ,,,,,,,, , ,,.., , , , , ,, , , , , , , , 1999 ,,,,,,,,,,,,,,,,,,,,,,,.. 2000 2001<br>-4Premium US - Premium ARG - Diesel US --Diesel ARG -- WTI|<br><!-- End of picture text -->

> Source: Secretariat of Energy, Argentina. Energy Information Administration, U.S

In the United States fluctuations in crude oil pricesl' are reflected -with a lag- in gasoline prices. Besides, prices of all types of gasoline, -diesel, regular (not shown in the figure) and premium- tend to move in the same direction. This price setting process is strikingly different in Argentina where diesel, premium and regular gasoline prices do not move in a parallel way. In Argentina, it is evident from figure 2 that gasoline prices do not closely respond to changes in crude oil prices. It is remarkable the stability of diesel prices from 1995 to 2000, a period characterized by significant upward and downward

> '° The West Texas Intermediate is considered in this paper as the international price of crude oil.

8
<!-- Start of picture text --> uD<br>°<br>o<br>a<br>oO<br>Did<br>oo<br>(= ))ae<br>o<br>=<br>2250©= — ee a<br>oO<br>8 a ——<br><!-- End of picture text --> <!-- Start of picture text --> 18<br>N<br>2011 2012 2013 2014<br>Year<br>—e— 8,: No controls<br>—e— £.,: Controls for education level<br>Note: 95% confidence interval is shown by bars<br><!-- End of picture text --> B2 B3 Bz = B3) f2 > B3)
As part of the practice MPL, subjects may review the choices they made in each practice binary choice. They may be asked to consider whether they would have been pleased with the MPL payout if a given choice had actually been randomly selected. This can decrease the chance that subjects view the MPL as a test with a “correct” answer. Many papers carry out practice elicitation, including, for example, Fuller and RickerGilbert (2021).

**Formatting.** An engaging design and clear formatting and layout can increase attention, improve understanding, and decrease fatigue and mistakes. When subjects complete the MPL themselves, formatting choices such as a large font size and images can increase comprehension, including in contexts with low literacy. Attention may also be increased by prompting subjects to verify their response to a binary choice before moving on to the next one. Other visual aids, such as showing an animated coin flip at the end of the MPL, can help convey features of the MPL, here that the realized choice is selected randomly.

# **4.2 Implementation Package**

As part of this paper, we provide an implementation package for MPL data collection, processing, and analysis, described in detail in a technical appendix.

The package can create a SurveyCTO template that implements the design features above, along with randomization within MPL and within binary choice. SurveyCTO is a common survey tool used for conducting in-field surveys via an app on a phone or tablet. The SurveyCTO file is fully automatically generated from a Stata script together with a set of user-defined inputs, as described in Appendix S2. The template supports flexible randomization specifications for various elements of the MPL and can accommodate a pre-existing survey sample. With small changes, the SurveyCTO template can be used for a wide range of preference elicitation experiments, including risk aversion or time preferences. It can also be adapted for other Open Data Kit (ODK) data collection platforms.

We also provide Stata programs that prepare the data generated by the SurveyCTO questionnaire for analysis (Appendix S3), and an ado file that specifies a command to estimate WTP with our proposed scaled random-effects probit (Appendix S4). The command can output standard errors via the delta method or via cluster bootstrap and supports various diagnostics to ensure accurate estimates.

33
# **Appendix Table 6. Effect of preschool availability on female’s main activity**

||(1)<br>Working|(2)<br>Job searching|(3)<br>Attending<br>school|(4)<br>Housekeeping|
|---|---|---|---|---|
|**Panel A: Public**|||||
|Public * Eligible|0.017|0.002|-0.002|-0.003|
||(0.027)|(0.003)|(0.008)|(0.028)|
|Public Preschools|0.015|0.002|0.014**|-0.034|
||(0.030)|(0.003)|(0.006)|(0.031)|
|Eligible Child|-0.035***|-0.006***|-0.001|0.050***|
||(0.009)|(0.001)|(0.002)|(0.009)|
|FDR q-value|0.912|0.912|0.912|0.912|
|**Panel B: Private**|||||
|Private * Eligible|0.001|0.000|-0.002***|0.001|
||(0.002)|(0.000)|(0.001)|(0.002)|
|Private Preschools|-0.002|-0.000|0.005***|-0.002|
||(0.003)|(0.000)|(0.001)|(0.003)|
|Eligible Child|-0.037**|-0.007***|0.010***|0.045***|
||(0.014)|(0.002)|(0.004)|(0.014)|
|FDR q-value|0.693|0.693|0.002|0.693|
|Observations|36,287|37,223|37,223|37,223|
|Mean|0.446|0.008|0.038|0.491|

Notes: Sample is restricted to females aged 19-45 in at least two IFLS rounds. We infer preschool data in-between PODES years. Each column regresses the dummy for having main activity as indicated in column heading on the density of public (Panel A) or private (Panel B) preschools, defined as the number of preschools divided by the population of children aged 3-6 in each district, a dummy for having a preschool-aged eligible child, and their interaction. All regressions include mother’s age fixed effect, urban dummy, district, year, and individual fixed effects. Robust standard errors clustered at district level. Stars denote statistical significance at 1, 5, and 10 percent levels based on unadjusted p-values. FDR q- values for the interaction coefficient of preschool density and eligible child are computed over all 4 outcomes and are shown in the last row of each panel. FDR q-values indicate the probability of false positives among _significant_ tests. Number of female-year observations and means of dependent variables are indicated in the last two rows. Main activities are only asked pertaining to survey years and not to historical employment.

74
**Figure 3:** Average prevalence of Rohingya households, for Teknaf and Ukhia by camp and non-camp villages

<!-- Start of picture text --> 90.00%<br>80.00%<br>70.00%<br>60.00%<br>50.00%<br>40.00%<br>30.00%<br>20.00%<br>10.00%<br>0.00%<br>Teknaf Camp in No camp in Ukhia upazila Camp in No camp in<br>upazila village village village village<br>Teknaf Ukhia<br>Prevalence: Old Rohingya Prevalence: New Rohingya<br><!-- End of picture text --> Source: IPA Population Count, 2018. The IPA population count counts with 201 villages in Teknaf and 209 villages in Ukhia. Old Rohingya refers to displaced population arrived before September 2017. New Rohingya refers to population arrived after September 2017.

In summary, analysis of the IPA population count indicated a very small presence of recently arrived Rohingya outside camps and in upazilas that did not host camps. The few Rohingya who were reported as living outside camps were located in the adjoining upazilas of Cox’s Bazar Sadar and Ramu that border Ukhia. These persons were almost all from the older cohort of Rohingya who arrived before the 2017 influx.

However, analyses of the count highlighted two challenges of relying on these data alone to conclude that the prevalence of Rohingya displaced outside camps was low. First, due to inconsistencies in the use of geographic codes, 35 percent of the villages covered by the IPA count could not be matched to the official Housing and Population Census host population data, and therefore no refugee prevalence information was available for these villages.<sup>13</sup> Second, the IPA count relied on a non-standard methodology for collecting population numbers. Standard quick counts are typically used by all national households’ surveys and even population censuses, to inform segmentation into census tracts or enumeration areas (EAs). Similar counts are also done for refugees by the International Organization for Migration (IOM). The reliability of those quick counts relies on getting

> 13 Bangladesh has three administrative tiers below the district: upazilas, unions, and mauzas. Census villages, which are not an administrative unit, come below the mauza (a mauza in Cox’s Bazar includes, on average, 3.9 villages) _._ The IPA count recorded only two out of the three administrative levels and did not use census geocodes for census villages. This resulted in matching issues and led to a significant challenge in generating complete data on refugee prevalence at the village level, and in validating the count based on other auxiliary data sources before using it as a reliable sample frame.

6
<!-- Start of picture text --> Current year horizon Median: Current year horizon Current year horizon Median: Current year horizon<br>Next-year horizon Median: Next-year horizon Next-year horizon Median: Next-year horizon<br>8 1<br>6 .5<br>4 0<br>2 -.5<br>0 -1<br>Panel C. Ex-Ante Variance of Term Spread Panel D. Ex-Ante Skewness of Term Spread<br>Current year horizon Median: Current year horizon Current year horizon Median: Current year horizon<br>Next-year horizon Median: Next-year horizon Next-year horizon Median: Next-year horizon<br>8 1<br>6 .5<br>4 0<br>2 -.5<br>0 -1<br>Oct 2005 Jan 2007 Apr 2008 Jul 2009 Oct 2010 Jan 2012 Apr 2013 Jul 2014 Oct 2015 Oct 2005 Jan 2007 Apr 2008 Jul 2009 Oct 2010 Jan 2012 Apr 2013 Jul 2014 Oct 2015<br>Oct 2005 Jan 2007 Apr 2008 Jul 2009 Oct 2010 Jan 2012 Apr 2013 Jul 2014 Oct 2015 Oct 2005 Jan 2007 Apr 2008 Jul 2009 Oct 2010 Jan 2012 Apr 2013 Jul 2014 Oct 2015<br><!-- End of picture text --> FIGURE 2. FORWARD-LOOKING CONDITIONAL VARIANCE AND SKEWNESS OF TERM SPREAD AND ABSOLUTE DEVIATIONS FROM INFLATION TARGETS Source: Consensus Forecasts and author’s calculations.

Note: The global term spread statistics are constructed using fifteen countries: 1) Australia; 2) Canada; 3) France; 4) Germany; 5) India; 6) Indonesia; 7) Italy; 8) Japan; 9) the Republic of Korea; 10) the Netherlands; 11) Spain; 12) Sweden; 13) Switzerland; 14) the United Kingdom; and 15) the United States. The global inflation statistics are derived using the twenty most significant economies in terms of GDP size: 1) Australia; 2) Brazil; 3) Canada; 4) China; 5) France; 6) Germany; 7) India; 8) Indonesia; 9) Italy; 10) Japan; 11) the Republic of Korea; 12) Mexico; 13) the Netherlands; 14) the Russian Federation; 15) Spain; 16) Sweden; 17) Switzerland; 18) Turkey; 19) the United Kingdom; and 20) the United States. The global variance series are scaled down using the sample standard deviation of the original series. The global skewness series is scaled down using the maximum value of the corresponding original series.

Ex-ante variance and skewness information of the term spread and absolute deviations of inflation targets from a variety of countries are used to proxy the corresponding global measures (Figure 2). Implied volatilities of options contracts are the source of information for estimating the ex-ante variance and ex-ante skewness of the S&P 500 and crude oil prices (Figure 3).

20
<!-- Start of picture text --> (A) Rural (B) Urban<br>Burkina Faso Bi 4:6 | 7<br>Central African Republic |TT <.2 es<br>Gabon | (i «<0 SE<br>Mali -~ J 3s : : : : | coe : :<br>Mozambique DSS «:«<br>Niger a} 31 i 31 : :<br>Sudan [ :2 a =:<br>Tunisia | 37.9 Iii is 49.8: : :<br>Zambia |TT Sy <:<br>Zimbabwe iis 26:2 | 35.9<br>a | —_—_ToT._— —o>r saad<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Share of households with income losses, %<br><!-- End of picture text -->
# **Acronyms**

|BNPP|Bank-Netherlands Partnership Program - Global and Regional Initiatives|
|---|---|
|CNIPRE|_Centre National de l’Innovation Pédagogique et des Recherches Educatives_<br>(National Center of Innovation and Educational Research)|
|EARC|Educational Assessment and Research Centre (Ghana)|
|EFA|Education for All|
|FTI|Education for All Fast-Track Initiative|
|HDNED|World Bank Human Development Network|
|IBE|International Bureau of Education (UNESCO)|
|IEG|Independent Evaluation Group|
|OECD|Organization for Economic Cooperation and Development|
|PASEC|_Programme d’analyse des systèmes éducatifs_|
|PPAR|Project Performance Assessment Review|
|PIRLS|Progress in International Reading Literacy Study|
|SAEPE|Education Evaluation System of the State of Pemambuco|
|SACMEQ|Consortium for Monitoring Educational Quality|
|SPSS|Statistical Package for the Social Sciences|
|TIMSS|Trends in International Mathematics and Science Study|
|WBI|World Bank Institute|
|USAID|United States Agency for International Development|
|UNESCO|United Nations Educational, Scientific, and Cultural Organization|
Phelps, E.S. (1972) “The statistical theory of racism and sexism”, _American Economic Review_ , Septiembre, vol. 62, pp. 659-661.

Rama, M. (2003) “Globalization and Workers in Developing Countries”, World Bank Policy Research Working Paper No. 2958.

Rutherford, T. F. and Tarr, D. G. (2008) “Poverty effects of Russia’s WTO accession: Modeling “real” households with endogenous productivity effects”, Journal of International Economics, vol. 75, pp. 131–150.

Siddiqui, R. (2004) “Modelling gender dimensions of the impact of economic reforms on time allocation among market, household, and leisure activities in Pakistan”, MPRA Working Paper.

Siddiqui, R. (2007) “Modelling Gender Dimensions of the Impact of Economic Reforms in Pakistan”, MPRA Working Paper 2007-13, PEP.

Sorensen, E. (1990) “The crowding hypothesis and comparable worth”, _Journal of Human Resources_ , pp. 55-89.

Tarr, D. G. (2012) “Putting Services and Foreign Direct Investment with Endogenous Productivity Effects in Computable General Equilibrium Models” in Dixon, P. And Jorgenson, D. (Eds.) Handbook of Computable General equilibrium modeling, Elsevier, North-holland, available at: http://www-wds.worldbank.org/external/default/WDSContentServer/IW3P/ IB/ 2012/03/26/000158349_20120326084225/Rendered/PDF/WPS6012.pdf Tarr, D. G. (2002), "On the Design of Tariff Policy: Arguments for and Against Uniform Tariffs,” in B. Hoekman, A. Mattoo and P. English (eds.), Development, Trade and the WTO: A Handbook, Washington: World Bank.

Terra, M. I., Bucheli, M., Estrades, C. (2007), “Trade Openness and Gender in Uruguay: a CGE Analysis”, MPRA Working Paper 2008-16, PEP.

Thurlow, J. and Wobst, P. (2003) “Poverty-focused social accounting matrices for Tanzania”, TMD Discussion paper no. 112, International Food Policy Research Institute, Washington D.C. USA.

United Nations (2009) “2009 World survey on the role of women in development: Women’s control over economic resources and access to financial resources, including microfinance”, New York.

UNCTAD (several years) _World Investment Report_ , Geneva.

25
Policy Research Working Paper 9470

# **Abstract**

Leveraging data in 147 countries from the Johns Hopkins University, the World Bank, and other sources, we investigate how the COVID-19 pandemic spread and how the mortality rates are associated with preexisting vulnerabilities, the government’s mobility restriction policy, institutions (democracy), and culture (i.e., trust and individualistic culture). While delay in domestic mobility restrictions is not significantly associated with pandemic outcomes, increasing delay in restricting international mobility is associated with higher pandemic mortality rate. Some pre-existing vulnerabilities are positively associated with the spread of the pandemic but not the mortality rate. However, when vulnerabilities are accompanied with delay in domestic mobility restrictions, the pandemic has higher mortality. Democracy is associated with lower policy delay in restricting mobility, lower pandemic mortality rate, and features better protection of the vulnerable population from pandemic harms. However, delay in domestic mobility restrictions have more adverse effects in democratic countries. Trust is not associated with worse pandemic outcomes, but its combinations with policy delay and obesity are. More individualistic countries do not feature different pandemic outcomes. However, in such countries, government delay in domestic mobility restrictions is associated with significantly higher pandemic mortality rate, and the marginal effects of the share of elderly on pandemic spread is greater.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at lxu1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
low spending by Khartoum and a low level of γ could result in Juba preferring war to unity, but we assume away this possibility with the preference for peace. However, this possibility should serve as a caution against any international intervention which would serve as a substitute for (or more likely, augment) military spending by Juba.

As shown in the previous section, even if neither actor has an interest in going to war, both actors have an incentive for military expenditure to reduce their opponent’s gains and make conflict as costly for their opponent as possible. This rationale explains the very high levels of military spending by both the GNU and GoSS<sup>19</sup> . According to the World Bank’s 2007 Public Expenditure Review (PER) for Sudan, the GNU’s expenditure for defense and national security in 2006 was 325.6 billion Sudanese Dinar (1.175 billion US$). This represented 19.8% of total government expenditure for the GNU. Security expenditures for GoSS in 2006 were 555 million US$, representing 41.7% of the South’s government expenditures<sup>20</sup> . As Figures 1 and 2 demonstrate, even if Khartoum has no interest or intention of going to war, it has every incentive to maintain a credible threat of war to ensure that Juba will choose unity in the referendum. Likewise, even if Juba has nothing to gain from conflict, it must make conflict as costly as possible for Khartoum to prefer peace over war if peaceful partition is to be a real strategic option for Juba<sup>21</sup> . Unfortunately, this is the logic of conflict; non-productive military expenditure is a

> 19 For example, a recent ICG (2008) report argues that (p. 1),” having concluded that it cannot rely on the guarantors, the SPLM has been building up its military capacity, which many members consider its only realistic leverage over the NCP, as well as developing alliances with marginalized movements and rebel factions within Darfur, Kordofan, the East and the far North.”

> 20 Figures from the CPA Monitor for the 2007 budgets are different and may reflect changes in spending from 2006 to 2007 as well as differing definitions.  The CPA Monitor lists the total budget for the security sector for the GNU at US$375 million.  Military spending for GoSS is listed as US$592 million.  These statistics are the most readily available and most accurate, though discrepancies clearly exist.   The PER figures are consistent with the CIA World Factbook estimate of Sudan’s military expenditure of 3% of GDP (which would suggest US$ 1.13 billion spending on defense for GNU in 2006).

> 21 For an analysis of military expenditure as a cause, not an effect, of post-conflict relapse into violence, see Collier and Hoeffler (2006).

17