rafmacalaba/datause-encoder-data huggingface dataset:
**Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS1719 Shifting Responsibility for Social<br>Services As Enterprises Privatize in<br>Belarus|David Sewell|February 1997|C Lawrence<br>32216|
|WPS1720 The Distribution of Foreign Direct<br>Investment in China|Harry G. Broadman<br>Xiaolun Sun|February 1997|J. Grigsby<br>82423|
|WPS1721 EU Accession of Central and<br>Eastern Europe: Bridging the<br>Income Gap|Luca Barbone<br>Juan Zalduendo|February 1997|L. Barbone<br>32556|
|VVPS1722 Uncertainty, Instability, and<br>Irreversible Investment: Theory,<br>Evidence, and Lessons for Africa|Luis Serven|February 1997|E. Khine<br>37471|
|WPS1723 The Productivity Effects of<br>of Decentralized Reforms: An<br>Analysis of the Chinese<br>Industrial Reforms|Lixin Colin Xu|February 1997|P. Sintim-Aboagye<br>38526|
|WPS1724 Debt Maturity and Firm Performance<br>A Panel Study of Indian Companies|Fabio Schiantarelli<br>Vivek Srivastava|February 1997|B. Moore<br>38526|
|WPS1725 Access to Long-Term Debt and<br>Effects on Firms' Performance:<br>Lessons form Ecuador|Fidel Jaramillo<br>Fabio Schiantarelli|February 1997|B. Moore<br>38526|
|WPS1726 Roads, Population Pressures, and<br>Deforestation in Thailand,<br>1976-89|Maureen Cropper<br>Charles Griffiths<br>Muthukumara Mani|February 1997|A. Maranon<br>39074|
|WPS1727 The Economics of the Informal<br>Sector A Simple Model and Some<br>Empirical Evidence from Latin<br>America|Norman A. Loayza|February 1997|R. Martin<br>39026|
|WPS1728 Regional Labor Markets during<br>Deregulation in Indonesia: Have the<br>Outer Islands Been Left Behind?|Chris Manning|February 1997|J. Israel<br>85117|
# FIGURE 5 Potential growth around the global recessions of 2009 and 2020

## A. World: Potential growth

<!-- Start of picture text --> Percent<br>5<br>Average Range<br>4<br>3<br>2<br>1<br>0<br>-1<br>t-2 t-1 t=0 t+1 t+2 t-2 t-1 t=0 t+1 t+2<br>2009 global recession 2020 global recession<br>C. EMDEs: Potential growth<br>Percent<br>10 Average Range<br>8<br>6<br>4<br>2<br>0<br>t-2 t-1 t=0 t+1 t+2 t-2 t-1 t=0 t+1 t+2<br>2009 global recession 2020 global recession<br><!-- End of picture text -->

## B. Advanced economies: Potential growth

<!-- Start of picture text --> Percent<br>4 Average Range<br>3<br>2<br>1<br>0<br>-1<br>-2<br>t-2 t-1 t=0 t+1 t+2 t-2 t-1 t=0 t+1 t+2<br>2009 global recession 2020 global recession<br>D. EMDEs excluding China: Potential growth<br>Percent<br>8 Average Range<br>6<br>4<br>2<br>0<br>-2<br>t-2 t-1 t=0 t+1 t+2 t-2 t-1 t=0 t+1 t+2<br>2009 global recession 2020 global recession<br><!-- End of picture text --> Sources: World Bank; World Economic Outlook.

Note: EMDEs = emerging market and developing economies. “Average” is an unweighted average of seven potential growth measures (excluding expectations). “Range” reflects the maximum and minimum. Figures show potential growth around global recessions in t=2009 and t=2020. Unbalanced sample of 30 advanced economies and 25 EMDEs for 2007-21.

25
management level, and at the distribution level. Data analytics spanning the entirety of water system operations are becoming more common, increasing efficiency and leading to operational improvements. A wholly different, but complementary, set of scientific breakthroughs will affect water purification and sanitation. The techniques will make centralized systems better and more effective, but will also enable decentralized, and disruptive innovations. Many of these technologies and techniques have already started deploying.

For example, in water resource management:

- Drones, satellites and remote sensing technologies are being combined, and used to complement (scarce) ground measurements, to more efficiently measure flow and map topography, to better plan for floods and drought.

- New, “softer” physical techniques such as dry dams and movable, inflatable dams are being considered for flood control, rather than traditional dams or berms and levees.

- New materials are being used for irrigation channels, to prevent and minimize leakage.

- Use of modeling has led to a revolution in “precision irrigation”.

- Drones and other remote sensing techniques are also being deployed for irrigation management.

_The sum effect could be reduced flood and drought risk, reduced water usage in agriculture via precision water delivery, reduced water loss via improved irrigation, and in areas with depleted groundwater, reduced subsidence, and in estuarine areas, reduced salinity._ And for example, in water distribution systems:

- The use of satellite imagery has already been proven for leak detection.

- New hydraulic modeling algorithms enable lower pressures and, in turn, less system stress and energy consumption.

- Sensors with telemetry are already being deployed to monitor pressure and flow, minimizing losses and improving system maintenance.

- Miniaturized robots are being tested for deployment within pipes to identify leaks.

- Machine learning and “digital twins” are being used for system optimization, predictive analytics and preventative maintenance in pumping systems and distribution.

- New metering technology allows for better data analysis with lower labor costs, as well as better monitoring of the status of the meters’ conditions themselves.

- 3D printed water quality meters are being developed to measure pH and conductivity.

- _The sum effect could be reduction in leaks, breaks and “non-revenue” water, thereby improving the financial position of the water utilities and their ability to deliver services._

And for example, in drinking water purification systems:

- Various new filtration techniques have been discovered in recent years. These include advances in materials and nanomaterials such as graphene, titanium dioxide and metal organic frameworks (MOFs, which have the largest internal surface area of any known substance).

9
ment 34 (2), 359–370.

- Remmer, K. L., 2004. Does foreign aid promote the expansion of government? American Journal of Political Science 48 (1), 77–92.

- Rodrik, D., 2008. The New Development Economics: We Shall Experiment, but How Shall We Learn? Harvard Kennedy School Working Paper Series rwp08-055.

- Roodman, D., 2009. A note on the theme of too many instruments. Oxford Bulletin of Economics and Statistics 71 (1), 135–158.

- Rose, A. K., 2006. Size really doesn’t matter: In search of a national scale effect. Journal of the Japanese and International Economies 20 (4), 482 – 507.

- Schneider, B. R., Heredia, B. (Eds.), 2003. Reinventing Leviathan: the Politics of Administrative Reform in Developing Countries. North-South Center Press, University of Miami, Miami.

- Smets, L., Knack, S., 2014. World Bank lending and the Quality of Economic Policy. World Bank Policy Research Working Paper 6924, World Bank, Washington DC.

- Svensson, J., 2000. Foreign aid and rent-seeking. Journal of International Economics 51 (2), 437–461.

- Tommasi, M., 2004. Crisis, political institutions and policy reform: The good, the bad, and the ugly. In: Tungodden, Bertil, N. S., Kolstad, I. (Eds.), Annual World Bank Conference on Development Economics. World Bank and Oxford University Press.

- van de Walle, N., 2003. Presidentialism and clientelism in Africa’s emerging party systems. The Journal of Modern African Studies 41 (02), 297–321.

- Wooldridge, J. M., 1995. Score diagnostics for linear models estimated by two stage least squares. In: Maddala, G. S., Phillips, P. C. B., Srinivasan, T. N. (Eds.), Advances in Econometrics and Quantitative Economics: Essays in Honor of Professor C. R. Rao. Blackwell, Oxford, pp. 66 – 87.

- World Bank, 1998. Assessing aid: What works, what doesn’t, and why. Oxford University Press for the World Bank, Oxford and New York.

- World Bank, 2007. Conditionality in Development Policy Lending. World Bank, Washington, D.C.

- World Bank, 2012. The World Bank’s Approach to Public Sector Management 2011–2020: Better Results from Public Sector Institutions. Poverty Reduction and Economic Management, World Bank, Washington, D.C.

- World Bank, 2013. Development Policy Retrospective 2012: Results, Risks, and Reforms. World Bank, Washington, D.C.

- Xavier, J. A., 2013. The world bank approach to public sector management 20112020: lessons from the

29
# **TABLE 6**

||Residential<br>Conn.per<br>Employee|Distribut.<br>Losses|Coverage|Energy Sold<br>per Conn.|Duration<br>of<br>interrupt's|Frequency<br>of<br>interrupt's|OPEX per<br>Connection<br>(in dollars)|OPEX per<br>MWH sold<br>(in dollars)|Avg Resid.<br>Tariff<br>(in dollars)|Avg Indust.<br>Tariff<br>(in dollars)|Cost<br>Recovery<br>Ratio|
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|Dummy Transition of|0.175***|0.030***|-0.013***|0.062***|-0.022|0.044|-0.463**|-0.451**|0.053***|0.027|0.043|
|PSP|(0.014)|(0.011)|(0.002)|(0.005)|(0.027)|(0.033)|(0.220)|(0.228)|(0.016)|(0.019)|(0.059)|
|Dummy Post Transition|0.101***|-0.091***|0.006***|0.024***|-0.112***|-0.167***|-0.152***|-0.158***|-0.089***|-0.058***|0.157***|
|of PSP|(0.008)|(0.012)|(0.002)|(0.006)|(0.025)|(0.024)|(0.036)|(0.046)|(0.011)|(0.021)|(0.049)|
|Duration of the Regulat.|-0.014***|-0.018***|0.004***|-0.018***|-0.094***|-0.094***|-0.057***|-0.016***|0.026***|-0.013***|0.040***|
|Agency|(0.003)|(0.002)|(0.001)|(0.001)|(0.008)|(0.007)|(0.005)|(0.004)|(0.004)|(0.003)|(0.005)|
|Duration of the Regulat.|-0.000|0.001***|-0.001***|0.001***|0.004***|0.004***|0.003***|0.001***|0.001***|0.002***|-0.001***|
|Agency (Sq.)|(0.000)|(0.000)|(0.000)|(0.000)|(0.001)|(0.001)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Utility FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Utility Specific<br>Time trend|Yes|No|Yes|No|No|No|No|No|No|No|No|
|Observations|2000|2073|1323|2515|1056|947|864|873|1728|840|669|
|Number of utilities|199|190|144|213|144|132|131|131|175|90|103|

Standard errors in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1%

- 19 -
Column 1 of Table 2 shows results for the effects of the intervention on the total number of SP shots. We find an additional 12.72 units in the treatment group per month and health center. Given a post-intervention quantity in the control group of 18.47 (10.22 + 8.25), our point estimate indicates an increase in SP injections of roughly 70% (12.72/18.47). Results are robust across Poisson and Negative Binomial regression specifications.

<u>Table 2: Efect on the quantity of Sayana Press and overall contraceptives dispensed</u>

||(1)|(2)<br>Sayana P|(3)<br>ress|(4)|(5)<br>Any|
|---|---|---|---|---|---|
||Total|Health Center|Home|visit|contraceptive|
||||By HPO|By CHW|dispensed|
|**_Treat x Post_**|**12.72*****|**9.38*****|**0.18**|**3.16*****|**8.42**|
||**(2.78)**|**(2.53)**|**(0.19)**|**(0.59)**|**(36.27)**|
|_Post_|10.22**|9.85***|0.18**|0.19*|-4.30|
||(1.25)|(1.26)|(0.07)|(0.11)|(28.29)|
|_Treat_|0.62|0.58|-0.06|0.10|-7.02|
||(2.16)|(2.13)|(0.09)|(0.11)|(41.96)|
|Observations|6,139|6,139|6,139|6,139|6,139|
|Baseline mean|8.25|8.16|0.08|0.00|450.68|

Notes: The table presents results based on the estimation of Equation 1, where the outcome concerns quantities of Sayana Press injections as described in the column titles. Standard errors, presented in parentheses, are clustered at the health center level. The stars next to the estimated coefficients follow the usual convention (*** p<0.01, ** p<0.05, * p<0.1).

Columns 2 to 4 of Table 2 break down the total quantity of SP injections into shots delivered by the health center and (renewal) shots by HPO and CHW, respectively. We observe that the effects are driven in large part by shots in health centers and to some extent by CHWs. Shots provided by HPO, on the contrary, were not affected by the intervention. While the initial shots were always required to be provided by the health center, we note that about half of the shots added as a result of the intervention were still administered by the centers during the eligibility phase (Appendix Table A2).<sup>12</sup> The last column of Table 2 displays the estimated impact of the intervention on the total number of contraceptives dispensed, which is statistically insignificant. The remaining part of this section explores the possibility that the intervention may have caused substitution away from other contraceptive methods.

> 12 We attribute the increase in SP injections at health centers in the later phases of the intervention to several possible factors. First, shots at health centers remained a feasible alternative for renewal administration after CHWs were granted eligibility. Second, the intervention’s training increased health center staff’s awareness and knowledge of SP. Third, CHWs administered several shots under supervision at health centers. We describe plausible mechanisms in more detail in the mechanism section of the Appendix.

14
enrollment in grades 6–8 in government schools by an average of 6 girls per school (or by 9 percent relative to initial enrollment) over the period 2003–05. Using government school census data and difference-in-differences estimation,<sup>4</sup> Hasan (2010a) finds that the program increased female enrollment in grades 6–8 in government schools by an average of 11 girls per school over the period 2003–08. Hasan also finds that the program increased corresponding male enrollment by an average of 10 boys per school. In another study, Hasan (2010b) uses nonrepresentative panel household sample survey data for three districts (one program district, two nonprogram districts) with adult literacy rates some distance from the cutoff and applies within-family difference-in-differences estimation.<sup>5</sup> In this study, Hasan finds that the program had no effect on the likelihood of government school participation by girls ages 10–14 in 2005 or 2006 but had a significant positive effect of 3 percentage points on this outcome in 2007. Finally, using government school census data from 2003–09 and parametric RD estimation, Alam, Baez, and Del Carpio (2011) find that the program had significant positive effects on girls’ enrollment in grades 6–8 from 2005 to 2009. They find average gains in girls’ enrollment by grade ranging from 10 to 32 percent.

# **3. Program context**

Pakistan, a lower-middle-income economy with a per capita income in 2018 of $4,928 (in 2011 purchasing power parity U.S. dollars), is the sixth most populous country in the world (World Bank 2019). The country is divided into four large provinces (Balochistan, Khyber Pakhtunkhwa, Punjab, and Sindh) as well as small territories.

Punjab is a major province. It accounted for 53 percent of Pakistan’s population of almost 208 million in 2017 (Pakistan Bureau of Statistics 2017a) and for 55 percent of the

> 4 The difference-in-differences approach compares outcomes before and after program onset and between program and nonprogram districts.

> 5 The difference-in-differences approach compares outcomes before and after program onset and between program and nonprogram districts. The within-family component compares siblings of different genders.

8
Cardenas et.al. (2011) suggests, however, that labor market insertion for these generations declined at these ages.

A more detailed picture for the age group that is normally expected to be attending Upper Secondary education –usually between 15 to 18 years of age- is presented in Panel B in Figure 1. As can be observed, the proportion of individuals attending school in different generations increased over the period, but especially during the 2000-2011 period where shares attending school increased by 7 points. Under constant labor force insertion rates, this would have yielded a drop in the Osow youth share in the course of a decade, even greater in value than that observed in the entire 20 year period.

_Stylized facts derived from cohort analysis_ Although informative, these snap shots refer to the static comparisons of different generations observed at a particular age in a particular year, so strictly speaking they do not allow observing the life-cycle dynamics discussed in Behrman et.al. (2014). However, this is possible when using the same source for identifying representative groups of individuals born during the same year – or year span- and observed repeatedly in later surveys at other points in their life cycle, since the representativeness of samples is constant over time for the same country.

An example of time choices made by two different generations followed through this synthetic panel approach is presented in Figure 2. In all cases, cohorts are aggregated in three age intervals, which facilitates observing shifts during Lower and Upper Secondary, and Higher Education. The Figure includes the averages for the 18 LA countries in our database for illustration.<sup>11</sup> Take for instance the case of cohort 1 in Figure 2, which was born between 1977 and 1979, and therefore reached ages 15 to 17 between 1992 and 1994. This is the first cohort for which a trajectory can be constructed by starting at school entry age 6 (which is the average in the region). According to Panel B in Figure 1, around 58 percent of 15-17 year olds were attending Upper Secondary Education around 1980, and this is reflected in Figure 2 by a similar proportion in cohort 1 at age 15 attending school (the numbers are not identical since Figure 1 considers an average time span of 5 years in its aggregation). Figure 1, however, only presents the snapshot at this age and turns to the following cohort, while Figure 2 follows the same group from age 6 all the way into the future, including the 15-17 “window” included in Figure 1.

> 11 The aggregation of regional averages is performed in a similar way as in Figure 1 -by interpolating values for each country between each two points in time for which there is data for constructing yearly series. The averages are not population weighted in this case either.

7
# **Instances of Non-Compliance**

Perhaps the simplest approach for evaluating the effect of the press conference would involve comparing compliance rates prior to and after the event. In so far as the CJC compliance team indicates, for each order, whether there has been compliance, partial compliance, or noncompliance, this would appear straightforward. An unfortunate data management strategy makes the interpretation results derived from such a strategy difficult. Consider Table 1, which summarizes the compliance record for the Caja in October and November of 2009. The second column presents the frequencies of compliance outcomes as reported in March 2010 (compare to the second row of the table in Figure 1). The third column shows the same information as reported by the CJC compliance team in their cleaned data set published in November 2011. Critically, whereas the Sala IV reported a 67% certain compliance record in March 2010, by November 2011, their cleaned dataset suggested that it was 100%.

|Level of Compliance|Press Conference<br>(March, 2010)|Cleaned Dataset<br>(November, 2011)|
|---|---|---|
|Compliance|70|106|
|Partial Compliance|2|0|
|Non-Compliance|3|0|
|Unknown|27|0|
|Total|104|106|
|Percentage certain compliance|67|100|

Table 1: _Two Pictures of the Caja’s Compliance Record for October-November, 2009._ Why explains difference? A part of the story has to do with the “Unknown” category. Between March 2010 and November 2011, the CJC was able to track down information about the _Caja’s_ reactions. But this is not the entire story, as it does not explain what happened to the five orders that were coded as _partial compliance_ or _non-compliance._ The answer lies in data management software. The CJC was using MS Excel to manage their 26
# **Economywide and Sectoral Impacts on Workers of Brazil’s Internet Rollout**

Mark A. Dutz, Lucas Ferreira Mation, Stephen D. O’Connell and Robert D. Willig<sup>1</sup>

**Keywords:** Internet, technology, labor demand, wage inequality, Brazil. **JEL codes:** J23, J31, O14

1 Affiliations: Dutz (World Bank), Mation (IPEA), O’Connell (MIT and IZA), Willig (Princeton). This work has benefitted from funding by the World Bank’s Latin America and Caribbean’s Chief Economist Office, under the regional study “Digital technology adoption, skills, productivity and jobs in Latin America.” The authors are grateful to participants for helpful comments and suggestions at the World Bank’s Workshop on Digital Technology Adoption and Jobs in Latin America held on October 31-November 1, 2016. We thank Joao Bastos, Nicolas Pinto and Pui Shen Yoong for excellent research assistance.
risk effects prevail in years 1 and 2, leading students to work harder (Figure 13 panel a) and accumulate more classes (panel b) than in the baseline. Nonetheless, this greater accumulation of classes, together with the loss-of-urgency effect, leads students to work less and accumulate fewer classes than in the baseline in years 3-5. To compensate for their slower pace and graduate, students raise effort in years 6-8. Thus, students conduct an intertemporal re-allocation of effort which, on average, delivers almost the same graduation rate as the baseline yet a lower on-timegraduation rate.

**Figure 13: Free college: Changes in effort and cumulative classes completed**

<!-- Start of picture text --> 1<br>.04<br>.02 .5<br>0 0<br>−.02 −.5<br>−.04 −1<br>1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8<br>Year Year<br>Universal free college Performance−base free college Universal free college Performance−base free college<br>a. Change in effort w.r.t. to baseline b. Change in number of cumulative classes<br>Change in effort classes completed<br>Change in number of cumul.<br><!-- End of picture text --> _Source_ : Model’s simulations.

_Notes_ : For existing students in the counterfactuals, panel a shows average effort in the corresponding counterfactual minus average effort in the baseline. Panel b does the same for average number of cumulative classes completed.

In contrast, under performance-based free college students frontload effort. Absent the lossof-urgency effect, the substitution and risk effect produce greater effort than in the baseline in years 1-3. Since students accumulate more classes than in the baseline in those years, they can work less later while still accumulating more classes than in the baseline. These effort changes imply not just an intertemporal reallocation of effort but also a net effort increase that raises both graduation and on-time graduation rates relative to the baseline.

# **8.4 Outcomes by student type**

The aggregate outcomes shown above mask great heterogeneity among student types. For a policymaker interested in promoting enrollment or graduation for specific students, this heterogeneity is critical. To analyze it more easily, we aggregate our fifty student types into nine income- and ability-based groups. Income groups include high, middle and low-income— corresponding to the top, two middle, and two bottom income brackets respectively. Ability groups include high, middle, and low-ability—corresponding to the top, two middle, and two bottom ability quintiles respectively.

35