rafmacalaba/datause-encoder-data huggingface dataset:
**Appendix 1: 15 Indicators of the Survey on Monitoring the Paris Declaration**

||**_Indicators_**|**_Partner_**<br>**_country_**<br>**_performanc_**<br>**_e_**|**_Developme_**<br>**_nt partner_**<br>**_performanc_**<br>**_e_**|**_How measured (including_**<br>**_source of information)_**|**_2010 target_**|
|---|---|---|---|---|---|
|1|Operational national<br>development<br>strategies<br>|<br>||Bank‘s assessment based on<br>information from partner<br>countries|**75%**of partner countries<br>have operational national<br>development strategies|
|2a|Reliable public<br>financial<br>management (PFM)<br>systems|||Country Policy and<br>Institutional Assessment<br>(CPIA) Indicator 13|Half of partner countries<br>move up at least one<br>measure|
|2b|Reliable<br>procurement<br>systems|||Self-assessment using<br>Methodology for Assessing<br>Procurement Systems (MAPS)<br>|No target (due to the small<br>number of countries<br>conductingMAPSin 2010)|
|3|Aid flows are aligned<br>with national<br>priorities (aid on<br>budget)|||Aid flows reported in the<br>annual budget by<br>government/aid flows actually<br>disbursed by development<br>partners(%)|Halve the gap (at least<br>**85%**reported on budget)|
|4|Strengthen capacity<br>by coordinated<br>support (technical<br>assistance)|||Amount of coordinated<br>technical cooperation / total<br>technical cooperation (%)|**50%**of technical<br>cooperation is coordinated|
|5a|Use of country PFM<br>systems|||Use of PFM/total assistance<br>(%)|A one-third (two-thirds)<br>reduction in the % of aid<br>for partner countries with<br>a score of 3.5-4.5 (5 or<br>above) on indicator 2a|
|5b|Use of country<br>procurement<br>systems|||Use of procurement / total<br>assistance (%)|No target (as no target is<br>set for indicator 2b)|
|6|Strengthen capacity<br>by avoiding parallel<br>project<br>implementation<br>units(PIUs)|||Number of parallel PIUs|Reduce by two-thirds the<br>stock of parallel PIUs (**-**<br>**67%**)|
|7|<br>Aid is more<br>predictable|||Actual aid flows recorded by<br>the government/scheduled aid<br>flows by development partners<br>(%)|Halve the proportion of<br>aid not disbursed within<br>FY for which it was<br>scheduled|
|8|Aid is untied|||OECD-DAC data|Continue progress over<br>time|
|9|Use of common<br>arrangements or<br>procedures<br>(program-based<br>approaches—PBAs)|||Program-based approaches<br>(budget support + non-budget<br>support PBAs)/total<br>assistance (%)|**66%**of aid flows are<br>through PBAs|
|10<br>a|Joint missions to the<br>field|||Number of joint<br>missions/total number of<br>missions (%)|**40%**|
|10<br>b|Joint country<br>analytic work|||Number of joint analytic work<br>/total number of analytic work<br>(%)|**66%**|
|11|Results-oriented<br>frameworks|||Bank‘s assessment based on<br>information from partner<br>countries|**38%**of partner countries<br>have sound results-<br>oriented frameworks|
|12|Mutual<br>accountability|||Partner countries have mutual<br>assessment reviews based on<br>their response|**All**partner countries have<br>mutual assessment<br>reviews|

_Source: World Bank (2011, p.4)_ 21
# **4. Trip-level results**

## **_4.1 Descriptive statistics_**

We queried 22,777,551 unique trip instances. After eliminating a small fraction of trips for which trip length is not well measured or larger than the haversine distance between origin and destination by more than 50 kilometers, we are left with 22,744,156 observations, 14.8% of which are weekend trips.<sup>22</sup> Some basic trip statistics are reported in table 1. Average travel speed is 22 kilometers per hour. While the interquartile range is fairly small at only about 8 kilometers per hour, the tails of the distribution are quite long. Similar observations can be made for trip duration and length. The average trip under actual traffic conditions lasts about 13% more time than its counterpart without traffic. Keeping in mind that we oversampled trips taken at night, we return to this issue below. Finally, the average trip is about 50% longer than its “effective” (haversine) length.

Table 2 reports summary statistics for the 154 cities in our sample. They are on average large, with a mean population above 1.3 million, and fast growing, having doubled in population since 1990.<sup>23</sup> Variation across cities in rates of access to personal motorized transportation and road infrastructure stocks are substantial.

Table 3 reports descriptive statistics for various naive measures of mean city travel speed. Mean travel speed across cities is 24.4 kilometers per hour.<sup>24</sup> This is rather slow, especially given that faster night trips are somewhat oversampled. By comparison, Akbar and Duranton (2018) estimate a similar mean speed using a comparable methodology in Bogotá, Colombia, a highly congested city of nearly nine million, and Couture _et al._ (2018) report a mean trip speed by privately-owned vehicles of 38.5 kilometers per hour in us metropolitan

> 22Google Maps often provides problematic routes for motorized travel on short trips. Furthermore, Google Maps rounds trip lengths, and moves our origin and destination points to the nearest road. In extreme cases, such as when a sampled origin is in the middle of a large park, this can lead to routes that are shorter than the haversine distance between the sampled origin and destination. To limit these problems we consider only trips longer than one kilometer. These problems still sometimes arise beyond one kilometer.

23The two sources of population differ both because of the target year and because they are based on slightly different boundaries. In most cases differences are small, but a few cities in Kerala are substantially smaller using our lights-based definition than in the un database. These cities appear to have a particularly expansive urban agglomeration as defined by the Indian census.

24This cross-city mean is slightly larger than the overall population mean of 22.1 kilometers per hour reported in table 1 because travel speed is faster in smaller cities for which we have fewer observations.

13
steps of tobacco production, particularly baling, which requires lots of strength. Once again, part of this impact will be captured by the unmarried-women dummy. On top of this, we also include an indicator of baling activities to deal with inherent differences in this production process and to control for specific tobacco expertise.

Once all these controls are included in the model, our instrument is only capturing the high risk of default in the event of a marriage and the exclusion restriction should be satisfied.

A similarly important issue is that our eligibility condition must be exogenous. Exogeneity may fail if, for instance, unmarried women decide to get married in order to become members of a tobacco club. The Tobacco Clubs Survey, however, explicitly rules out this possibility. When asked whether young women may envision marriage as a means to club membership, 99 percent of the respondents asserted that no such marriage market exists.

Table 6 presents results from the 2SLS estimations. As before, we investigate impacts on the four major tobacco farming outcomes, namely yields, sales per acre, land shares and unit values. We define three sets of controls and estimate four model specifications. The set of controls A includes characteristics of the household and of its head. The set of controls B includes variables related with the plot and the activity of growing tobacco. Meanwhile, the set of controls C includes inputs used in the production of tobacco such as fertilizer and temporary work (ganyu).

Panel A reports results from the first stage regression of burley club membership on the instrument and the controls. Results show that being unmarried female under 45 years old significantly reduces the probability of belonging to a tobacco club in all models. Concretely, an unmarried woman under 45 is around 54-55 percent less likely to be a burley club member than an unmarried women over 45 (notice that the controls include gender dummies and an interaction unmarried-female dummy). In all the specifications, the instrument is highly significant with associated F-tests always above 10. These results rule out any concerns with weak instrumentation (Staiger and Stock, 1997; and Hahn and Hausman, 2002). In our case, weak instrumentation may arise not precisely because of the lack of explanatory power of the instrument—as is often the case—but because of small sample size. In our sample of 25
THE DEMOCRATIC REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE REGIDESO is currently in the process of implementing a management contract that aims to improve operational performance.

**Table 9. Benchmarking water and sanitation indicators**

||Unit<br>Low-income cou|ntries<br>**DRC**|Fragile states|
|---|---|---|---|
|Access to piped water|% pop<br>8.4|**21.0**|27.0|
|Access to standposts|% pop<br>13.1|**7.1**|17.5|
|Access to wells/boreholes|% pop<br>34.4|**49.0**|61.5|
|Access to surface water|% pop<br>51.4|**22.4**|30.8|
|Access to flush toilets|% pop<br>2.7|**1.6**|7.1|
|Access to improved latrines|% pop<br>5.3|**10.8**|20.3|
|Access to traditional latrines|% pop<br>44.5|**71.2**|56.9|
|Open defecation|% pop<br>58.4|**16.4**|51.6|
|Domestic water consumption|liter/capita served/day<br>76.3|**51.3**|53.5|
|Urban water assets in need of rehabilitation|%<br>36.1|**42.0**|36.7|
|Revenue collection|% sales<br>97.2|**70.0**|100.2|
|Distribution losses|% production<br>33.8|**40.8**|32.8|
|Cost recovery|% operating expenses<br>covered by revenues<br>107.3|**64.4**|82.3|
|Total hidden costs|% of revenue<br>112.9||202.3|
|U.S. cents per m<sup>3</sup>at 10 m<sup>3</sup>|**DRC**|Fragile states|Other developing regions|
|Residential tariff|4.6|42.0||
|Nonresidential tariff|0.6|150.0|3.0–60.0|

_Source:_ Banerjee and others 2009; Morella and others 2009, derived from AICD water and sanitation utilities database downloadable from http://www.infrastructureafrica.org/aicd/tools/data **.** 20
3 Table 1 Female LaLar Force Participation Rate by Age Group

|Country|End-|Year||Female P|articipation|Rate by A|ge Group||
|---|---|---|---|---|---|---|---|---|
||Early|Late|<|<br>20|<br>20|<br>-59|<br>6|0+i|
||||Early|Late|Early|Late|Early|Late|
|Argentina|1960|1980|8.2|6.4|24.4|33.1|6.6|5.1|
|<br>Boliviana|1976|na|6.9|na|23.1|na|14.1||
|Brazil|1960|1980|6.3|9.4|18.2|33.0|10.0|7.4|
|Chile|1952|1982|28.4|14.0|28.6|28.9|15.6|6.2|
|Colombia|1951|1985|9.0|12.5|19.0|39.4|12.3|_15.5_|
|CostaRica|1963|1984|13.5|9.5|18.6|26.4|5.8|4.3|
|Ecuador|1962|1982|4.9|4.0|17.7|22.6|13.2|10.4|
|Guatemala|1964|1981|9.4|8-1|13.1|14.7|8.7|7.3|
|Honduras|na|1974|na|14.2|na|17.8|na|7.7|
|Jamaica|1962|1982|10.5|6.1|52.7|48.2|13.8|9.8|
|Mexico|1960|1980|9.7|_15.5_|19.1|32.7|30.0|20.3|
|Panama|1950|1980|23.4|16.8|24.9|35.7|10.1|7.2|
|Peru|1961|1985|15.9|10.5|22.7|29.0|14.2|_15.5_|
|Uruguay|1963|1985|29.4|15.2|32.0|46.0|5.2|6.3|
|<br>Venezuela|1961|1981|9.7|9.4|22.1|35.0|8.9|7.-|
|Average*|||13.7|10.6|24.1|32.7|1'.9|9.4|
|<br>Standard de|viadion*||7.9|3.8|9.5|8.6|6.1|4.6|
|Coefficient o|f variatio|n|57.4|36.2|39.6|26.4|51.6|48.9|

Notes: 1) The "below 20" is "0-19" in Bolivia, Brazil, Ecuador and Jamaica; "10-19" in Argentina, Guatemala, Mexico, Peru and Venezuela; "10-24" in Colombia; _"15-_ 19" in Chile, Honduras and Parama; and "12-19" in Costa Rica. In Uruguay it refers to those aged 15-19 in the early period and those aged 12-19 in the late period.

2) The "20-60" refers to "20-64" in Bolivia and Jamaica with corresponding changes to the "60+" age-group.

Source: Based on ILO (1990), Table 1.

* Excluding Bolivia and Honduras in both periods.

It is worth dwelling a 'ittle longer on the case of Jamaica, the only country in our sample where the rate of female participation in the labor force was lower in the latest period. Jamaica had a (statistically) abnormally high rate of female participation in the early period. The
# CONTENTS

|Introd|uction|1|
|---|---|---|
|1.|Disequilibrium in wages, pensions and employment|2|
|2.|The budget revenue effect of wage increases|6|
|2.1.|Effect at constant employment|6|
|2.2.|The effect at constant employment, counterfactual approach|9|
|2.3.|<br>Employment effect|10|
|3.|The pension spending effect of high wage and pension claims|15|
|3.1.|The pensions of 1989 pensioners|15|
|3.2.|New entrants and their pensions|18|
|3.3.|Adding the three effects|19|
|4.|Other income-related expenditure|21|
|4.1.|Wages in government|21|
|4.2.|Unemployment costs|23|
|4.3.|<br>Peasant pensions|24|
|4.4.|Family allowances|25|
|5.|Policy evaluation and implications|25|
|5.1.|From profit taxes and subsidies to payroll taxes and benefits|25|
|5.2.|The importance of pensions during the transition|27|
|5.3.|The benefits|27|
|5.4.|Retirement eligibility|29|
|5.5.|<br>Financing and cost|32|
|5.6.|The role of wages|33|
|6.|Income claims and structural deficits|34|
|7.|Conclusion|38|
|Appe|ndix 1: Pension Rules|40|
|<br>Appe|<br>ndix 2: Budget Tables: General Government and Extrabudgetary Funds|42|
|REFE|RENCES|47|

# REFERENCES

# FIGURES:

# Figure 1. GDP growth, producer wages and new taxes.

3 Figure 2. Labour force, retirement, unemployment 5 Figure 3. A decomposition of pension revenue 14 Figure 4. Sources of pension expenditure 20 Figure 5. Structural burdens 37
- Affordability: The childcare regulation improves childcare service provision especially for low-income or vulnerable families through government support (financial or non-financial) to parents, private childcare centers, employers, or cost-regulating structures.

- Quality: The childcare regulation ensures a safe environment for children, contributes to healthier nutrition and school readiness, and promotes uptake.

Data collection builds on established WBL methodology and expertise and relies on desk research through the review and assessment of official laws and regulations as well as consultations with WBL experts in labor legislation. Both federal and local legislation applicable to a country’s main business city are considered. For federal systems, where provision of childcare is not established at the national level, legislation applicable to the main business is documents and instruments – re- city explored. Non-binding typically ferred to as policy notes, national strategies, guidelines, recommendations, declarations, and opinions – are not considered for the purposes of data collection. Official ministerial websites specifying or explaining certain regulatory aspects covered within the established three-pillar framework are cited in limited circumstances.

To measure the separate effect of childcare availability, affordability, and quality on FLFP, we construct a score for each pillar as a simple average over all variables included in each pillar, each of which is either a dummy variable (e.g., “Does the law mandate employers to provide or support childcare services?”) or is normalized between 0 and 1 (e.g., “What are the mandated minimum hours of operation of public childcare centers if regulated in the law?”). The score takes a value of 0 before the enactment of the law and 7
Policy Research Working Paper 8164

# **Abstract**

This paper presents results from four simulations of the impact of potential tax reforms in Pakistan on poverty, shared prosperity, and inequality. The simulations are carried out in the context of a dynamic computational general equilibrium model that incorporates endogenous tax evasion. The simulations link the computational general equilibrium model to household survey data that are incorporated in a micro simulation model. The combined models suggest that equal yield increases in sales and corporate tax rates differ mildly in their impacts on consumption and poverty. Endogenously modeled tax evasion plays an important role in the results.

This paper is a product of the Poverty and Equity Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at dnewhouse@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
methodologies often must be followed for verification of the GHG emissions reductions, and approval.

# **4. Transformational project-based MF**

As widely recognized in recent literature, a radical contraction of fossil-fuel-intensive sectors, and a similar build-up of non-fossil energy use, at national, regional and global scales, will be essential for successful implementation of a transformational decarbonization program.<sup>15</sup> “T ransformational project-based MF” represents a crucial component of such a transition. This was vividly illustrated in Figure 1, in section 2 above. As long as the _vH e_ curve lies below the marginal cost (MC) curve for the donors (for _Q_ below _QC_ ), the levelized cost of non-carbon energy investments is too high to make MF provision to support such investments in L countries beneficial to donors. As _Q_ moves to the domain beyond the “tipping point” _QC_ , however, supplying project-based MF becomes attractive for donors. Appendix 1 shows that the “tipping point” is reached whenever where _A_ is the levelized cost of establishing the new (non-carbon) energy technology. (4.14) is at the same time a basic condition on _A_ for the transition to non-fossil technology to be efficient: (levelized) unit costs of the new technology ought to be less than the overall unit costs of the old technology, when climate cost components are included in the latter.

We may now relate this analysis to our broader discussion of “transformational climate finance” in section 2. The main relation is via the conditions for our solution here to be implemented; which relates to the relative position of the RMF ( _vH_ times _e_ ; “marginal MF return”) and MC (“marginal supply cost of MF funds”) curves in Figure 1. When one is not yet in the “transformational” range for _Q_ beyond _QC_ , the RMF curve lies below the MC curve. The fundamental efficiency condition

> 15 See, among others, Fischer et. al. 2004; Rivers and Jaccard 2006; Kalkuhl et al. 2012; Lecuyer and Vogt-Schilb 2014; Audoly et al. 2014; Victor et al. 2019. World Bank 2016; Engle et al. 2018; Greaker et al. 2018; Vogt-Schilb et al. 2018; Rissman et al. 2020. See Popp 2019 for a survey of recent related work on low-carbon energy technology development. Audoly et al. 2014 calculate that net GHG emissions from the global electricity sector need to be reduced to (close to) zero by 2050 in order to reach the 2 degrees C global warming goal set as part of the PA. Full decarbonization will clearly be much more complex than discussed here. Rissman et. al. 2020 indicate paths to full decarbonization of key sectors by 2070, which require a combination of policy and MF support interventions similar to those discussed by us.

11
- 16 -

- EXD3 - dummy variable, taking the value of 1 for 1987.3 (the 'Sumarlin shock'), and 0 otherwise;

- Ml - narrow money (currency outside banks + demand deposits);

WPI - wholesale price index (excluding oil and gas)

- GDPQ - real quarterly GDP (obtained from annual series by linear interpolation);

- EPINW expected inflation rate (proxied by previous four-quarters' WPI inflation rate);

- QMD - rupiah quasi-money (rupiah time and savings deposits);

- MM money multiplier (ratio of M2 (- Ml + QMD) to reserve money);

DNFA = change in net foreign assets of Bank Indonesia;

- NEDM nominal excess demand for money (sum of fitted values of Ml and QMD, derived from equations (2) and (3) and converted to nominal term, minus actual M2 of preceding period);

- NFB net official long-term foreign borrowing;

-

- D Log X rate of change of any variable X;

- IMPP - import price index (import prices measured in domestic currency);

- RPRI - wholesale price of rice;

- EDM - excess demand for real money balances (defined as logarithm of real (deflated by WPI) fitted values of Ml + QMD, as derived from equations (2) and (3), minus logarithm of real M2 of preceding period);

- NEER - nominal effective exchange rate (defined so that an increase represents an appreciation of the rupiah);

- REER - real effective exchange rate

- INF - inflation differential between Indonesia and her trading partners (a series compiled by the IMF using CPI measures).

12. We proe:eed now to a brief discussion of the individual equations. In equation (la), the domestic interest rate is determined as a function of the swap-adjusted foreign interest rate, the central bank's discount rate, the price of oil and inflation differential between Indonesia and her trading
Es avaricia y contagio, más que ausencia estatal, Working Paper 2003-03, CEDE, Universidad de los Andes, March.

Caulkins, J. (1994); Developing Price Series for Cocaine. Santa Monica, CA, RAND.

---- (1996); Estimating Elisticities of demand for Cocaine and heroin with DUF Data, Working Paper, Carnegie Mellon University.

Caulkins, J. and P. Reuter (1998); What Price Data Tell us About Drug Markets, Journal of Drug Issues, Vol. 28, 3, pp. 593-613.

Caulkins, J., Reuter, P., Iguchi, M., and Chiesa, J. (2005); How Goes the “War on Drugs”? An Assessment of US Drug Problems and Policy, RAND occasional paper series .

Center for International Policy – CIP (and other NGO’s) (2004); Informe de la misión de observación sobre los efectos del Plan Colombia en los departamentos de Nariño y Putumayo, November. Available at: http://www.ciponline.org/colombia/0404mision.pdf Central Intelligence Agency (2004); Coca fact paper: a primer. Available at: http://www.odci.gov/saynotodrugs/cocaine_q.html Chaloupka, F., M. Grossman, and J. Tauras (1999); The demand of Cocaine and Marihuana by Youths, in _The Econometric Analysis of Substance Use and Abuse: An Integration of Econometrics and Behavioral Economics Approaches_ , Chaloupka et al. eds., University of Chicago Press, pp. 133-155.

44