rafmacalaba/datause-encoder-data huggingface dataset:
8 includes imputed rent, but excludes housing investment<sup>8</sup> . (1) and (2) are different from the usual semi-log budget share equation employed in earlier applications of the Rothbarth model to child gender bias in two respects. With the sample restricted to nuclear households, each observation contains two adults, the head and the spouse, so the proportion of adults does not appear above since it is no longer a variable, that is, variation in household size comes entirely from the change in the total number of children across households, hence demographic proportions are defined over _nc_ rather than _nh_ . I also note that (1) and (2) employ separate demographic proportions for child age, and child gender, combined into the same indices in earlier studies; with child gender defined over the number of children. I employ five different age groups of children, under 2, 3-6, 7-9, and 10-12 years, omitting those 13-15 as the missing group to avoid collinearity; though I also check my results with age groups under 13. Similarly, there are two gender proportions of children, for boys and girls; the one included above is the proportion of girls. I include, in the vector of variables in z, controls for urban/rural, and for the latter, whether the household resides in a rural area under the safety net system run by the government, and if so, whether in addition the area has a free school feeding program<sup>9</sup> . I also include controls for regions. For the 10 regions of the survey, 9 regional dummies are employed in (1) and (2), with Addis Ababa acting as the missing, reference region.

An issue arising here is whether to estimate equations (1) and (2) for households with positive purchases of adult goods, or over all households, inclusive of those with non-purchase. Household demographics affect whether a good is purchased at all; and, if so, additionally, its amount of purchase. If one is interested in whether girls receive fewer resources than boys, conditional on the household having a positive purchase, then a twostep estimation process is required, with the second stage based on positive adult goods

> <sup>8</sup> As a check, this produces average annual budged shares for tobacco, and clothing and footwear of 0.53, and 6.12 respectively, similar to those reported in the latest available Household Income and Consumption Expenditure Survey (HICES) for the 1999/2000 (2001, p. 37, table III.13). However, the average per capita total expenditure, reported separately in HICES 2005-06, differs somewhat from that based on the above definition, and the documentation available to this study cannot shed light on this difference.

> 9 The list of rural weredas in safety net regions, some with school feeding programmes, was separately available. This was then matched with the survey regions/weredas to construct safety net and school feeding dummies for the analysis.
Figure 7 Backcasted poverty rates series using different variables to map sectoral GDP data to the 2018/19 NLSS <!-- Start of picture text --> 47 46.2<br>46.3<br>45 46.1<br>43<br>41<br>39 39.1<br>37<br>35<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br>Household head's sector of employment<br>Oldest working member of the household<br>Household member closest to 40 in age<br>Poverty rate (percent)<br><!-- End of picture text --> Note: the figure compares different backasted series that use different ways to map the sectoral GDP data to household consumption data from the 2018/19 NLSS. It compares the main specification that uses household head’s sector of employment, to one using the sector of employment of the oldest working member of the household or the member of the household closest to 40 in age.

Table 12 Annualize change in average annual household consumption for different combinations of imputed GHS data, by decile

||Annualized Difference|Annualized Difference|Annualized Difference<br>2015/16 - 2018/19,|
|---|---|---|---|
|Deciles|2010/11 - 2018/19, percent|2010/11 - 2015/16, percent|percent|
|1|0.4|0.7|-0.2|
|2|0.4|0.8|-0.3|
|3|0.4|0.8|-0.4|
|4|0.4|0.9|-0.5|
|5|0.3|0.9|-0.7|
|6|0.3|1.0|-0.9|
|7|0.2|1.0|-1.1|
|8|0.1|1.0|-1.3|
|9|0.0|1.0|-1.7|
|10|-0.1|1.1|-2.0|

Note: the table shows the annualize change in average annual consumption at the decile level between different waves of imputed GHS data. Annualized changes are calculated using the compound annual growth rate. Household consumption data for different GHS waves is imputed using the main specification presented in this paper (also referred to as “Original Model”).

37
Several important questions can be addressed with these data. The first relates to locational stability: What are the permanent and transient components of household and community responses to long- and short-run cyclone clustering, and to the power of recent cyclones? Outright relocation will be part of the response, but so will relocation of prime-age household members to less-impacted inland areas where their earnings and residence will offer financial insurance and a potential safe harbor from future cyclones. The size and speed of the response will depend critically on expectations about future cyclone strikes, which can be inferred from the highlyvaried data in our cyclone panel.

Other questions of interest can also be explored once cyclone-related controls are introduced. For example, how much coastal outmigration reflects the “pull” of urban opportunities, and how much reflects the “push” of cyclone-related hazards? How do disaster mitigation policies affect responses to these hazards? Some coastal zones in our study area invest significant resources in damage prevention via polder construction and other measures, while others offer compensation once cyclone-related damage has occurred. Incorporation of controls from our cyclone panel database may permit more accurate estimation of the timing and magnitude of responses to these differences.

To conclude, we believe that the coastal cyclone experiences of Bangladesh, West Bengal and Odisha also offer potentially-valuable lessons for the global community. Our overall findings for the Indian Ocean are consistent with the view that ocean warming from climate change has increased the frequency and average power of regional cyclone storms. Since 1960, a composite index of cyclone frequency and power has increased by about 50%. Since our focal study area is one of the world’s most cyclone-prone, its experience can provide important clues for understanding the potential future of other cyclone-affected areas. In this context, studies that 22
as a result, which are usually used for consumption for this population. Overall, treatment households are more likely to have recovered from their shock, at least partially. This suggests that the TUP program has helped households reduce the impact of external shocks, along with helping them incur less debt that could affect their longer-term financial sustainability.

Diversification of income and revenue sources over time is one of the mechanisms through which the TUP program has been able to achieve lasting impacts and help treatment households mitigate the impacts of external shocks. Similar to the long-term 10-year and 11- and 7-year results observed in more stable settings in India and Bangladesh (Banerjee et al., 2021; Bandiera et al., 2017), treatment households are able to diversify over time as a result of the program, relative to the control group—a critical mechanisms for the lasting impacts found in these settings. Treatment households shift to other sources of income including paid labor—among others—with both men and women participating more in market activities than their control counterparts.

There are valid concerns that development research focused on poverty alleviation often lacks diversity. Relatively few studies are conducted in poor and fragile locations due to conflict and political instability, and this site selection can generate important biases in our understanding of program effectiveness (Tollefson, 2022; Allcott, 2015). At the same time, increasing fragility in some of the harshest conditions in the world, compounded with intensifying climate shocks makes it particularly difficult to improve the lives of those most in need. The enduring impacts of a one-off support package amidst multiple climate, health and security shocks highlights the promise, as well as the limitations, of multi-faceted interventions like the TUP program to support the most vulnerable populations. In particular, these results suggests a potential role for the TUP approach and similar multi-faceted programs as a way of integrating humanitarian relief and development efforts. Such programs could be leveraged by both humanitarian and development stakeholders to address acute short-term humanitarian needs, while providing longer-term development solutions.

40
**Table 10: General Sources of Technologies for firms in Penang**

||most impor<br>Number of<br>firms|tant<br>%|second m<br>importa<br>Number of<br>firms|ost<br>nt<br>%|third<br>impor<br>Number<br>of firms|most<br>tant<br>%|
|---|---|---|---|---|---|---|
|Embodied in new local machinery or<br>equipment|29|21.32|12|10.17|7|6.73|
|Embodied in new Imported<br>machinery or equipment|30|22.06|29|24.58|8|7.69|
|By hiring key personnel|15|11.03|16|13.56|21|20.19|
|Licensing or turnkey operations from<br>international sources|6|4.41|4|3.39|3|2.88|
|Licensing or turnkey operations from<br>domestic sources|2|1.47|1|0.85|5|4.81|
|Developed or adapted within the<br>establishment locally|11|8.09|13|11.02|10|9.62|
|Transferred from parent company|14|10.29|2|1.69|2|1.92|
|Developed in cooperation with client|||||||
|firms|13|9.56|14|11.86|11|10.58|
|Developed with equipment or<br>machinery supplier|8|5.88|12|10.17|12|11.54|
|From a business or industry<br>association|3|2.21|6|5.08|10|9.62|
|Consultants|3|2.21|6|5.08|10|9.62|
|From universities, public institutions|0|0|1|0.85|4|3.85|
|Mergers and acquisitions|1|0.74|1|0.85|1|0.96|
|Other|1|0.74|1|0.85|0|0|

Source: Authors’ calculations based on investment climate survey data

**Table 11: Collaboration partners when developing technologies locally**

||Yes|No|
|---|---|---|
|with Other firms|52|86|
||37.7%|62.3%|
|with Universities|22|116|
||15.9%|84.1%|
|with Research institutions|30|108|
||21.7%|78.3%|

Source: Authors’ calculations based on investment climate survey data Among those firms that have not collaborated with universities and research institutes in recent years, the major reasons seems to be that firms do not feel that universities and research institutes can render services that are relevant to their operations. This compounded by a lack of knowledge of these institutions, the services they offer, and the people to contact (see Table 12). The lack of knowledge by firms on 30
12 produces 13 million cubic meters per year for a population of 142,000 through a network of 166 kilometres. In Colombia, the average WSS utility produces 22 million cubic meters and serves 229,000 persons (network length is 322 km on average). Finally, regional WSS companies in Brazil produce on average 390 million cubic meters per year, serve 3,784,000 persons through a network length of 10,715 km.

Population coverage (i.e., the share of the total population of the area under utility’s responsibility which is supplied by the utility) is 49 percent in Vietnam, 63 percent in Moldova, 88 percent in Brazil, and reaches 95 percent in Colombia. The lowest density of customers is observed in Moldova (on average 275 customers per kilometre of network). Density of customers is slightly higher in Brazil (on average 376 customers per kilometre of network) and reaches 664 and 788 customers per kilometre of distribution network in Colombia and Vietnam, respectively. Energy costs represent about one-third of total operational expenses in Colombia, Moldova, and Vietnam, while it is much lower in Brazil (11 percent). Labor costs represent between 31 percent (in Vietnam) and 40 percent (in Brazil) of total operational expenses in the four countries.

# **5. Estimation results**

Because the economic and regulatory environments in which utilities operate can be very different from one country to the other, we estimate the system combining the Translog cost function and the cost share equations separately for the four countries.

For Brazil, Moldova, and Vietnam, we specify time-specific and utility-specific unobservable effects. Utility-specific effects, which capture all unobservable time-constant utility characteristics (e.g., efficiency of the utility manager), cannot be considered when estimating the model on the Colombian data because utilities have been surveyed only twice. We estimate the Translog cost function along with the input cost share equations using Zellner’s (1962) technique for estimation of a system of seemingly unrelated equations, under the assumption of fixed utility- and time-specific effects.<sup>19</sup> To overcome the problem of singularity of the covariance matrix, we delete one of the share equations. The regressors are all normalized by removing their sample mean. This mean-scaling transformation, which is 19 The system has been estimated using the (iterated) SUREG procedure from STATA software.
||spending on public<br>capital.|||
|---|---|---|---|
|Engagement of local<br>actors in overseeing<br>budget implementation<br>(e.g., citizen oversight<br>of local development<br>projects)|Qualitative case<br>studies (e.g., could<br>include review of local<br>development plans,<br>meeting minutes,<br>surveys of<br>stakeholders,<br>observations,etc.)|Participatory<br>accountability<br>mechanisms are in place<br>to respond to citizen<br>concerns related to project<br>implementation|Blore, Devas and<br>Staler (2004)|

The findings signaling how institutional changes can contribute to higher-level outcomes in certain contexts highlight the importance of measuring budget transparency, accountability, and participation and tracing their incremental outcomes along a results chain. Documenting these change processes over time would help practitioners to plan for and assess the effectiveness of knowledge initiatives. Overall, this review serves as a starting point and underscores the need for further investigation to establish effective measurement practices of institutional change and build an evidence base for understanding the relative robustness of institutional change paths and the context in which they are likely to matter. We recommend that WBI begin to collect such data with its Open Budgeting program to advance our understanding of institutional impact on poverty in this area.

25
- Wire room activity report—daily and periodically, as appropriate.  **

- Daylight OD numbers.

- Stock quote.

- CD rate survey.  #

- Funds flow analysis (see form).  +

- Fed funds summary (see form).  +

- Net intercompany funding positions report (see form).  **, +

- Total deposit trends (see form).  +

- Damage assessment reports (see form).  **, +

- Summary balance sheets on all banks and affiliates.  **

- Meet with management to discuss reports.  **

- Prepare daily e-mail.  +

- Sources and uses analysis (see form).

- Narrative of (1) sources and uses analysis and (2) any important events.

- Close of business estimates.

- Unfunded commitment report **

- Maturities report

- Consolidated maturities summary +

- Summary lead banks balance sheets with significant changes explained **

- Due from/to correspondent bank summary **

- Trust balance (secured portion) **

- ALCO packet

- Fed discount window collateral (see form) +

- TT&L collateral (see form) +

- Attend bank ALCO **

62
<!-- Start of picture text --> Dakar<br>—— Minibus Routes<br>Access Percentage:<br>| High : 70<br>- Low:o ,<br>=<br>wit a<br>,<br>.<br>So = =.<br>TaN ae<br>a ~&a < .<br>NY<br>W<br>\ A<br>~~" E<br>N<br>s<br>0 215) 5) 10<br>aa<br>Kilometers Esri, HERE, Garmin, © OpenStreetMap contributors, and the GIS user community<br><!-- End of picture text -->
**Table 1: Key variables with descriptive statistics**

||||Flood-|
|---|---|---|---|
|Description Mean(Standard Errors)|Overall|Non Flood-prone|prone|
|**Occupational Focus of Members Relative to the Head**||||
|“Same Occupation as Head” takes the value 1 if the member’s<br>occupation matches that of the head of the household and 0<br>otherwise. Detailed occupational classification in the survey was<br>used to define occupation matches.|0.35<br>(0.008)|0.39<br>(0.011)|0.30<br>(0.011)|
|“Same sector as Head” takes the value 1 if the member’s sector<br>matches that of the head of the household and 0 otherwise. Six<br>sectors are defined as, agriculture, construction, transport and<br>public utilities, manufacturing, trade and business, services, and<br>others.|0.39<br>(0.008)|0.43<br>(0.011)|0.35<br>(0.011)|
|“Both in agriculture” takes the value 1 if both the member and<br>the head of the household report to be engaged in agriculture<br>and 0 otherwise.|0.33<br>(0.009)|0.36<br>(0.012)|0.29<br>(0.013)|
|“Both self employed” takes the value 1 if both the member and<br>the head of the household report to be self employed and 0<br>otherwise.|0.55<br>(0.013)|0.57<br>(0.017)|0.54<br>(0.018)|
|“Both in the same sector and self employed” takes the value 1 if<br>both the member and the head of the household report to be<br>self employed in the same sector and 0 otherwise.|0.17<br>(0.006)|0.18<br>(0.009)|0.16<br>(0.009)|
|“Both self employed in agriculture” takes the value 1 if both the<br>member and the head of the household report to be self<br>employed in agriculture and 0 otherwise.|0.13<br>(0.006)|0.14<br>(0.008)|0.11<br>(0.007)|
|**Household Characteristics**||||
|Monthly per capita Consumption in Taka|2,175<br>(17.09)|2,173<br>(23.81)|2,176<br>(24.55)|
|“Access to credit” takes the value 1 if household has taken any<br>loan in past 12 months and 0 otherwise|0.35<br>(0.005)|0.37<br>(0.008)|0.34<br>(0.008)|
|“access to Safety-net” takes the value 1 if any member of the<br>household availed of safety-net programs in last 12 months and<br>0 otherwise|0.30<br>(0.005)|0.32<br>(0.007)|0.29<br>(0.008)|
|**Upazila Characteristics**||||
|Access to market index|0.38<br>(0.014)|0.40<br>(0.018)|0.36<br>(0.021)|
|Monsoon Coefficient of Variation (Based on Stations)|0.21<br>(0.002)|0.22<br>(0.002)|0.21<br>(0.002)|
|Monsoon Coefficient of Variation (Based on CRU 3.1)|0.18<br>(0.002)|0.18<br>(0.002)|0.18<br>(0.002)|
|“Flood prone Upazilas” are historically Flooded Upazilas of 1998|0.51<br>(0.027)|||

Full list of variables is in the annex Table A1 Household per capita monthly consumption expenditure measure is derived from the HIES 2010 survey as part of the poverty assessment. The consumption expenditure is deflated by rural Dhaka Division prices to account for regional price variations. We control for seasonal differences in consumption expenditure with three seasonal indicators; winter takes the value 1 for households surveyed in 5
# **2.3 Publication bias**

As documented by Ioannidis et al. (2017) and Andrews and Kasy (2019), null effects tend to disappear from the literature because of the tendency of researchers and journal editors to prefer studies with statistically significant results. To limit such issues, as mentioned above, we did not restrict our literature search to published articles. However, a remaining concern is that researchers may not have written up papers for studies with null findings because they anticipate a harder publication process or because of their own preferences to not pursue the publication of null results. We take advantage of the AEA randomized controlled trial registry to check whether there are studies that meet our inclusion criteria but whose results have not been reported in the literature.<sup>15</sup> In February 2022, we conducted a simple search on the registry using the keyword “index insurance”. After reviewing the description of each project, we found a total of 15 studies that met our eligibility criteria, seven of which had been identified through our search process. Among the remaining eight studies, three have results publicly released in academic papers but not available when we registered our study (Boucher et al., 2021; Mishra et al., 2021),<sup>16</sup> one analyzes an intervention that was completed only recently (Cilliers et al., 2020), and four explore interventions that are still ongoing (Kazianga and Wahhaj, 2021; Kramer et al., 2021; Vieider et al., 2021a,b). If anything, this provides some reassurance that concerns over publication bias are not too severe in this literature.

# **2.4 Data requests**

Before the preparation and the registration of our pre-analysis plan, in which we specified the different variables we would use, we contacted the authors of each paper to present our project and to ask whether they would accept to share the underlying data.<sup>17</sup> We received principle agreements for seven studies. These studies are: Mobarak and Rosenzweig (2013), Elabed and Carter (2014), Karlan et al. (2014), Cole et al. (2017), Bulte et al. (2019), Hill et al. (2019), and Stoeffler et al. (2022). For three studies (Berhane et al., 2015; Belissa and Marr, 2018; Ahmed et al., 2020), either data were not shareable or we received no answer. Since our methodology requires original data, we do not include these studies. Importantly, at this stage, although the authors already agreed to share their data for our study, we made clear that our first step

> 15The AEA RCT registry provides details on a study’s characteristics irrespective of whether or not it is ultimately published.

> 16Boucher et al. (2021) report the results from two registered projects. Boucher et al. (2021) and Mishra et al.

> (2021) do not report impacts on ex-ante production decisions.

> 17 The email we sent is reproduced in Appendix C.

8
# **_INVYSTMENTS_** IN SOLID WASTE MANAGEMENT: OPPORTUNITIES FOR ENVIRONMENTAL IMPROVEMENT

# Table of Contents

# Page No.

# EXECUTIVE SUMMARY

|I.|INT|RODUCTION . . . . . . . . . . . . . . . . . .|. . . . . . . . <sup>.</sup><br>1|
|---|---|---|---|
||A.|PurRose and Organization of the Paper . . .|. . . . . . . . .<br>I|
||B.|<br>Met.todclogv..<br>...............|_2_|
||C.|Definitions.<br>. . .................|.<br>_2_|
||D.|<br>Benefits of Municial Solid Waste Management|.<br>. . . . . . .<br>3|
|II.|IBR|D/IDA FINANCING FOR SOLID WASTE MANAGEMENT . .|. . . . . . . . .<br>6|
||A.|Overview of Lendine Program . . . . . . . .|. . . . . . . . .<br>6|
||B.|<br>Regional Lending Programs . . . . . . . . .|. . . . . . . . .<br>7|
|||AFRICA Region . . . . . . . . . . . . . . .|. . . . . . . . .<br>8|
|||ASIA Region . . . . . . . . . . . . . . . .|. . . . . . . . . 10|
|||EMENA Region<br>. . . . . . . . . . I. . . .|. . . . . . . . . 12|
|||LAC Region<br>. . . . . I. . . . . . . . . .|. . . . . . . . . 14|
|III.|MSW|M COMPONENTS IN BANK PROJECTS . . . . . . . .|. . . . . . . . . 17|
||A.|Component Design and Implementation . . . .|. . . . . . . . . 17|
|||Storage and Collection . . . . . . . . . .|. . . . . . . . . 18|
|||Transfer/Transport . . . . . . . . . . . .|. . . . . . . . . 19|
|||Disposal . . . . . . . . . . . . . . . I.|. I. . . . . . . 19|
|||Resource Recovery . . . . . . . . . . . . .|. . . . . . . . . 20|
|||Industrial Waste Management . . . . . . . .|. . . . . . . . . 21|
|||Institutional Strengthening .21||
||B.|Project Issues .23||
|||Size and Scope of Investments . . . . . . .|. . . . . . . . . 23|
|||Level of Borrowing . . . . . . . . . . . . .|. . . . . . . . . 24|
|||Financial Considerations . . . . . . . . . .|. . . . . . . . . 25|
|||Private Sector Participation . . . . . . . .|. . . . . . . . . 26|