rafmacalaba/datause-encoder-data huggingface dataset:
mechanisms on the part of the city residents enhances electoral support for the Moscow mayor as indicated by our two measures of such support (the overall number of votes for the incumbent mayor and the margin of victory). What is the causal mechanism driving these results? Do Moscow authorities rely on crowd-sourced information on road quality to quickly react to road problems and thereby demonstrate high performance? This pathway highlights the information-gathering function of the pothole complaints system and the performance-driven voting results in Moscow city. The alternative causal mechanism might reveal the degree to which these new means of engaging with the local state leave those who participate in the co-production of ‘good roads’ in Moscow feel empowered and in touch with the local government (i.e., with enhanced trust for the government). Studies conducted by the World Bank in Russia have found that participatory local governance projects directly involving the population in identifying problems and their solutions improve relations of local communities with local authorities, increase public trust in local governance mechanisms and satisfaction with local infrastructure and services (Shulga et al. 2019; Shulga, Sukhova, and Khachatryan 2014). The engagement enhancing effects are present not only in projects implemented in rural communities but also in digitally-enabled participatory governance projects in other cities and localities such as participatory budgeting projects in the cities of Tver and Surgut.<sup>21</sup> The Moscow government digital platform might produce the same effects.

These causal pathways appear plausible and might be at work simultaneously in the case considered in this study. However, it is important to highlight that numerous studies have shown that the degree of public participation itself is influenced by government responsiveness (Sjoberg, Mellon, and Peixoto 2015). These findings imply that the mere use of technology without the expected feedback from the government does not work to boost support for the authorities. On the contrary, as the case of San Diego reveals, if roads are bad, the incumbent is punished. Furthermore, our findings show that each pothole complaint increases the number of votes for the incumbent by a considerably larger amount than one vote (specifically, the boost is anywhere between 29-44 votes), thereby implying that the causal mechanism is driven by something other than the impact on the individual who has engaged with the government. We were able to further clarify this issue by using the available data.

We have already noted earlier the efficiency of road maintenance systems in Moscow in terms of the time allocated for fixing the revealed problems. An additional test of the importance of

> 21 - <u>https://bftcom.com/expert bft/8964/ [last accessed: September 2020].</u>

24
# 1. Introduction

Bangladesh is one of the most flood prone countries in the world. Analysis of past flood records indicates that about 21 percent of the country is subject to annual flooding and an additional 42 percent is at risk of floods with varied intensity (Ahmed and Mirza, 2000). Although annual regular flooding has traditionally been beneficial by providing nutrient laden sediments and recharging groundwater aquifers, the country often experiences severe flooding during the monsoon that causes significant damage to crops and properties and has adverse impacts on rural livelihoods and production. A classified list of all floods in Bangladesh reveals the country has experienced 21 _above-normal_ floods, of which 4 were _exceptional_ and 2 _catastrophic_ since 1954.<sup>8</sup> The most recent _exceptional_ flood in 2007 inundated 62,300 sq km of land (42% of the total) and caused severe damage to lives and property. This flood caused 1,110 deaths, submerged 2.1 million ha of standing crop land, completely destroyed 85,000 houses, damaged 31,533 km of roads and affected 14 million people. The estimated loss of assets from this flood alone is US $ 1.1 billion.

In general, the relative severity of the impacts from _above-normal_ floods in Bangladesh has decreased substantially since the 1970s as a result of improved macroeconomic management, increased resilience of the poor, and progress in disaster management and flood protection infrastructure. The 1974 flood was a 1 in 9 years event which resulted in damages of 7.5% of GDP. In comparison, the 1998 flood was a 1 in 90 years event, inundating nearly twice the area but resulting in damages of 4.8% of GDP. The increased resilience of Bangladesh to floods is also apparent when recent GDP and agricultural growth rate trends are examined with respect to the timing of flood events. Until the 1990s, GDP and agricultural growth rates sharply declined following major flood events. However, the relative effects of major floods have diminished

> 8 A flood with inundation area exceeding 21% of the total land area of Bangladesh has been classified as _abovenormal_ . _Above normal_ floods have been further decomposed into four categories: _moderate, severe, exceptional_ and _catastrophic_ depending on the extent of inundation area being 21%-26%, 26% - 34%, 34%-38.5% and greater than 38.5% of the total land area respectively Mirza (2002).

3
# **C.3 National Estimates: Accounting for FPE and MTI**

Table C.7: National Estimates of Effect of Years of Schooling on Number of Children Born - Census + DHS

||**Number of**|**Years of**|**Number of**|**Number of**|
|---|---|---|---|---|
||**Children Born**<br>(OLS)|**Schooling**<br>(First Stage)|**Children Born**<br>(Reduced Form)|**Children Born**<br>(2SLS)|
||(1)|(2)|(3)|(4)|
|Years of Schoolingizy|-0.149<br>(0.015)<br>[0.000]||||
|Add’l Years of Free||0.115|-0.051||
|Schooling<br>�<br>_I_<sup>_F P E_</sup><br>_zy_<br>�||(0.044)|(0.016)||
|||[0.011]|[0.002]||
|Add’l Year of MTI||0.183|-0.007||
|No Script Change||(0.070)|(0.018)||
|�<br>_I_<sup>_MT I−T_</sup><br>_zy_<br>�||[0.012]|[0.687]||
|Add’l Year of MTI||-0.119|0.064||
|with Script Change||(0.052)|(0.028)||
|�<br>_I_<sup>_MT I_</sup><br>_zy_<br>�||[0.025]|[0.028]||
|_I_<sup>_F P E_</sup><br>_zy_<br>_× I_<sup>_MT I−T_</sup><br>_zy_||-0.008|-0.001||
|||(0.006)|(0.002)||
|||[0.169]|[0.630]||
|_I_<sup>_F P E_</sup><br>_zy_<br>_× I_<sup>_MT I_</sup><br>_zy_||0.001|-0.004||
|||(0.005)|(0.002)||
|||[0.767]|[0.028]||
|�<br>Years of Schoolingizy||||-0.273<br>(0.109)<br>[0.012]|
|First Stage F-Statistic||14.09||14.09|
|Number of Clusters|60|60|60|60|
|N|205,141|205,141|205,141|205,141|

_Source_ : Author’s analysis based on data from the Ethiopian census of 2007 and the Demographic and Health Survey (DHS) in years 2005, 2011, and 2016.

_Note_ : The dependent variable is either years of schooling in column 2 or number of births in the other three columns. � Years of Schooling _izy_ is the reported years of schooling from the data; Years of Schooling _izy_ is the predicted level of schooling, instrumented with the free primary education (FPE) intensity measure _Izy_<sup>FPE</sup> , two mother tongue instruction (MTI) intensity measures _Izy_<sup>MTI-T</sup> and _Izy_<sup>MTI</sup> , which denote the measures for MTI regions without and with script change, respectively, and the interactions for regions in which two interventions occurred. All samples include women in birth cohorts from 1970 to 1988. All regressions include birth year and zone fixed effects, zonespecific linear trends, and a cubic for age. Standard errors are clustered at the zone level and shown in parentheses; _p_ -values are shown in square brackets.

A.23
_Services PTAs_ the first policy area (Structure of agreement), embodying information from four variables, cover 80% of all observations.

3. Within each policy area, we construct sets of ten mutually exclusive binary variables that correspond to the nine most frequently observed policy configurations, plus one residual variable that equals unity for all other combinations, no matter how many of them may occur. It is these indicator variables for distinct policy configurations that we use jointly in estimation.

4. It is important to notice that the joint inclusion of these 10 variables cover all observable policy configurations contained in the PTAs per policy area. As such, there is no scope for omitted variable in the same way that would be a concern if only selected individual variables would be included in estimation.

5. It is interesting and reassuring to note that in many instances, those policy configurations that emerge amongst the most frequent ones per policy area have a natural interpretation and can be evaluated as being ’ambitious’ or ’shallow’ in a straightforward manner. For instance, the most frequent pattern in the first policy area (Structure of agreement) can be described, using the underlying variable information, as an agreement that follows a negative list approach, has two main chapters (one for cross-border trade in services and one for investment), and includes a ratchet clause as well as a standstill clause. Against the backdrop of the four underlying variables, this constitutes the second most ambitious pattern that is possible. The most ambitious configuration, which is similar to the above but the agreement exhibits three separate chapters (per mode of supply), is the eighth most frequent pattern and would therefore also be picked up by a discernible indicator variable in estimation.

6. Because policy configurations can be interpreted as ambitious or shallow from their contents, it should be the case that indicator variables for ambitious patterns return positive and significant coefficient whereas there should be no effects for unambitious provisions. This is indeed what we find.

We define seven policy configurations which reflect the contents of 29 different provisions: 26
# **Table 11 OLS: Are school/teacher characteristics correlated with scores?** **<u>Dependent variable is percent correct score, Grade 4, MP</u>**

||**Hindi**|**Math**|**Hindi**|**Math**|**Hindi**|**Math**|
|---|---|---|---|---|---|---|
|**Teacher-pupil ratio**|**1.11**|**0.22**|**0.96**|**0.14**|**1.09**|**0.20**|
||**(0.34)**|**(0.78)**|**(0.41)**|**(0.86)**|**(0.35)**|**(0.80)**|
|Index of infrastructure|0.01|0.00|0.01|0.00|0.01|0.00|
||(0.12)|(0.72)|(0.10)<sup>+</sup>|(0.67)|(0.11)|(0.69)|
|% non-sc teachers|-0.04|-0.04|-0.05|-0.05|-0.04|-0.04|
||(0.26)|(0.21)|(0.18)|(0.16)|(0.33)|(0.22)|
|% male teachers|-0.03|-0.04|-0.03|-0.04|-0.02|-0.04|
||(0.52)|(0.34)|(0.54)|(0.35)|(0.64)|(0.35)|
|% teachers with college deg|0.01|0.02|0.01|0.02|0.01|0.02|
||(0.80)|(0.55)|(0.79)|(0.55)|(0.81)|(0.56)|
|% teachers withgraduate deg|0.03|0.03|0.04|0.04|0.03|0.03|
||(0.57)|(0.40)|(0.51)|(0.35)|(0.60)|(0.43)|
|Av. Years of service|0.00|0.00|0.00|0.00|0.00|0.00|
||(0.29)|(0.37)|(0.29)|(0.37)|(0.27)|(0.29)|
|Av. days of in service lastyear|0.00|0.00|0.00|0.00|0.00|0.00|
||(0.48)|(0.41)|(0.44)|(0.43)|(0.46)|(0.43)|
|% teachers with pre service<br>training|-0.02|-0.04|-0.01|-0.03|-0.02|-0.03|
||(0.63)|(0.23)|(0.72)|(0.26)|(0.65)|(0.27)|
|% teachers doingmultigrade|-0.09|-0.05|-0.09|-0.06|-0.09|-0.05|
||(0.14)|(0.24)|(0.12)|(0.22)|(0.13)|(0.24)|
|**% teachers actively engaged in**<br>**teaching (mean over 4 visits)**|**0.15**|**0.18**|**0.19**|**0.20**|**0.15**|**0.18**|
||**(0.03)***|**(0.00)****|**(0.01)****|**(0.00)****|**(0.03)***|**(0.00)****|
|% teacherspresent in school|-|-|-0.09|-0.05|-|-|
||-|-|(0.19)|(0.42)|-|-|
|% contract teachers(samvida)|-|-|-|-|-0.02|0.03|
||-|-|-|-|(0.74)|(0.59)|
|% shiksha karmi teachers|-|-|-|-|0.03|0.01|
||-|-|-|-|(0.50)|(0.76)|
|Block fixed effect|Yes|Yes|Yes|Yes|Yes|Yes|
|n|2142|2142|2142|2142|2142|2142|

Robust p values in parentheses clustered at GP level, * significant at 5%; ** significant at 1%;<sup>+</sup> significant at 10%. Other controls include all student family background characteristics that are in table 8 and block fixed effects.

26
**Figure 10: Employment in exports (million), by broad sector and income category, 1995-2018**

<!-- Start of picture text --> High-income countries (as of 1995) Low- and middle-income countries (as of 1995)<br>100 350<br>90<br>300<br>80<br>70 250<br>60<br>200<br>50<br>40 150<br>30 100<br>20<br>50<br>10<br>0 0<br>Agriculture Mining Manufacturing Agriculture Mining Manufacturing<br>Business services Other services Business services Other services<br>China India<br>180 80<br>160 70<br>140<br>60<br>120<br>50<br>100<br>40<br>80<br>30<br>60<br>40 20<br>20 10<br>0 0<br>Agriculture Mining Manufacturing Agriculture Mining Manufacturing<br>Business services Other services Business services Other services<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br><!-- End of picture text --> Source: Own computations. Data: OECD TiE data, covering 51 countries and 45 sectors for the period 1995-2018. Income categories as of 1995 are used to classify countries into high- and low- and middle-income countries (see Appendix 8).

Several underlying factors could explain the decline of export-linked employment in emerging markets over the past decades. While country experiences differ and the data sample does not cover all LMICs, the trend of declining jobs in exports has been linked to increased technology-intensity of production especially within GVCs because new technologies are related to higher-quality standards and high-skilled labor, raising the barriers for low-skilled labor in LMICs to participate in GVCs (Rodrik 2018). As part of their own upgrading efforts, some countries have started to outsource labor-intensive production themselves, most notably China, to lower-cost locations within the region, also increasing intra-regional trade. In fact, the number of jobs sustained by manufacturing exports in China plummeted after the global financial crisis, plateaued at the new lower level until 2015 and declined further (Figure 10, bottom left panel). The emergence of new low-wage countries on the global trade landscape, not included in the dataset, such as Vietnam has further helped this development.

By contrast, the number of jobs in exports increased steadily in high-income countries, driven by business services exports, showing only a brief dip after the global financial crisis. The number of jobs in 14
where _Vadj_ – Exposed value adjusted; _Vcal_ – Exposed value calculated from exposure grid surfaces; _CTmea_ – Country total obtained based on statistics; _CTcal_ – Country total calculated from exposure grid surface.

**II.2.6** Conducting data quality assurance and control

Quality control was conducted to adjust for errors caused by overlaying grid surfaces of different resolutions, such as the 90-meter resolution inundation zone with 1-kilometer or 5-kilometer exposure grid surfaces. The following procedure was employed:

- 1) Calculate the country total from the grid surface using the country boundary;

- 2) Calculate the aspect exposure that is under 5-meter SLR;

- 3) Calculate the aspect exposure that is over 5-meter SLR;

- 4) Compare the country total with the sum of both aspect exposures. If the difference is less that 5%, the calculated aspect exposure was considered within the error tolerance. If not, the exposure calculation was reviewed and estimates revised until the 5% difference threshold was reached.

A more detailed description of each dataset is provided in Appendix 1.

# **III. Results**

In the first sub-section below, we present results at the global level for the 84 developing countries included in this analysis. In sub-section III.2, we present the results for each of the 5 regions and, individually, for each of the 84 countries. Our results indicate that for a number of countries, even a 1-meter SLR would have a very significant impact.

# **III.1 Global results**

Table 3 indicates that approximately 0.3% (194,000 km<sup>2</sup> ) of the territory of the 84 developing countries would be impacted by a 1-meter SLR. This would increase to 1.2% 9
promotion and preventive care outreach in poor neighborhoods.<sup>16</sup> The government and international donor agencies have supported NGO provision of RMNCH services through a number of initiatives, such as the Bangladesh Smiling Sun Franchise Program (BSSFP), started in 2007, and the Bangladesh Urban Primary Health Care Project (BUPHCP), started in 1998. The NGO BRAC also provides RMNCH services through its _Manoshi_ initiative, started in 2007.

Private providers also offer RMNCH services. They are fee-based and operate for profit, and include hospitals, clinics, nursing homes, diagnostic centers, and pharmacies, as well as private practices by medical doctors and nonformal or traditional healthcare practitioners. Private health facilities tend to have wider service coverage and longer, more convenient service hours than public or NGO health facilities. A large share of private providers charge low fees, making them affordable to poor households (Adams, Islam, and Ahmed 2015; Afsana and Wahid 2013). However, private providers tend to operate without required licenses, and their staff often lack required academic and professional qualifications (ICDDRB 2015).<sup>17</sup> Compared to staff at public or NGO health facilities, private providers are less likely to be familiar with government guidelines for healthcare practice management (ICDDRB 2015).

A census of health service providers located in, or near, a sample of slum settlements in Dhaka city found that more than 80% were private, 12% were public (but contracted out to NGOs to operate), and 6% were NGO. Thirty-eight percent of private providers were pharmacies, while 35% were nonformal or traditional healthcare practitioners. (Pharmacies not only sell drugs but also provide diagnostic and treatment advice.) More than 60% of private healthcare staff were unqualified, compared to 2% of public healthcare staff. Fifty percent of NGO healthcare staff were community health workers (Adams, Islam, and Ahmed 2015).

A few studies have examined the effects of RMNCH service provision on use, interpreting the results as causal. While the effects are not well identified, the studies tend to find that provision promotes use. For example, comparing slum settlements in Dhaka metropolitan area covered by the Manoshi program to those in the outskirts of Dhaka city corporation, and before and four years after the program was initiated, Alam et al. (2011) find that the program

> 16 Community health workers in villages have been credited for the notable declines in fertility and maternal and child mortality, and gains in child vaccination rates and uptake of oral rehydration therapy in Bangladesh (El Arifeen et al. 2013).

> 17 Private healthcare staff report that they often obtain their health knowledge through training courses offered by NGOs, or through learning on the job under the guidance of other staff (Adams, Islam, and Ahmed 2015).

12
**Figure 1. The relationship between ICDS program participation and states’ economic and malnutrition levels** (State Domestic Product per capita,<sup>34</sup> and prevalence of stunting and underweight amongst children, non-parametric Lowess-type regression estimates)

<!-- Start of picture text --> 1992 1998<br>1 1<br>.9 .9<br>.8 .8<br>.7 .7<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>0 5000 10000 15000 20000 0 10000 20000 30000 40000<br>per capita SDP per capita SDP<br>bandwidth = .99 bandwidth = .99<br>1 1<br>.9 .9<br>.8 .8<br>.7 .7<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>.1 .2 .3 .4 .5 .6 .1 .2 .3 .4 .5 .6<br>share of stunted (HAZ<-2) children under age 3 share of stunted (HAZ<-2) children under age 3<br>bandwidth = .99 bandwidth = .99<br>1 1<br>.9 .9<br>.8 .8<br>.7 .7<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>.1 .2 .3 .4 .5 .6 .1 .2 .3 .4 .5 .6<br>share of underweight (WAZ<-2) children under age 3 share of underweight (WAZ<-2) children under age 3<br>bandwidth = .99 bandwidth = .99<br>share of villages with ICDS<br>share of villages with ICDS<br>share of villages with ICDS<br><!-- End of picture text --> _Source_ : NFHS I and II, State Domestic Product data from the Government of India, _Economic Survey_ 2003-04.

34 The State Domestic Product per capita are for the years 1993-94, and 1998-99, to correspond roughly to the two survey years.

23
**Table 3.** Effect of the Policy on Completed Years of Schooling at Age 15 and Age 17 by Gender and Rural/Urban Residence

|||Age 15||Age|17|
|---|---|---|---|---|---|
|Birth Cohorts:|1978-1|984,|1983-1984,|1980-1984,|1983-1984,|
||1987-|1993|1987-1988|1987-1991|1987-1988|
|Time trend (Splines):|Linear|Quadratic|None|Linear|None|
|||A) Gende|r differences in ur|ban areas||
|Policy|0.349**|0.511|0.535**|0.803**|0.910**|
||[0.150]|[0.344]|[0.130]|[0.272]|[0.177]|
|Policy * female|0.067|-0.058|-0.019|-0.072|-0.075|
||[0.147]|[0.455]|[0.114]|[0.343]|[0.207]|
|Composite female|0.417***|0.454|0.517***|0.731**|0.835***|
||[0.081]|[0.450]|[0.016]|[0.236]|[0.087]|
|N|8,755|8,755|2,947|5,734|2,292|
|R-squared|0.145|0.145|0.140|0.145|0.144|
|||B) Gende|r differences in ru|ral areas||
|Policy|0.797***|1.051**|0.993***|1.391***|1.327***|
||[0.092]|[0.350]|[0.046]|[0.239]|[0.071]|
|Policy * female|0.329|-0.042|0.272|0.080|0.294|
||[0.210]|[0.458]|[0.122]|[0.243]|[0.162]|
|Composite female|1.125***|1.009***|1.265***|1.471***|1.621***|
||[0.152]|[0.295]|[0.120]|[0.178]|[0.168]|
|N|5,518|5,518|1,818|3,565|1,426|
|R-squared|0.332|0.333|0.335|0.333|0.327|
|||C) Rural|/urban differences|for men||
|Policy|0.331**|0.480|0.512**|0.786***|0.885***|
||[0.127]|[0.295]|[0.110]|[0.238]|[0.149]|
|Policy * female|0.463**|0.459|0.466**|0.526*|0.433***|
||[0.173]|[0.457]|[0.120]|[0.242]|[0.068]|
|Composite female|0.795***|0.939**|0.979***|1.312***|1.318***|
||[0.081]|[0.313]|[0.077]|[0.240]|[0.086]|
|N|5,185|5,185|1,488|3,512|1,415|

48