rafmacalaba/datause-encoder-data huggingface dataset:
5 control group. S1 Table shows how intervention and control villages and clusters are distributed across provinces. Randomization was done by the research team in Stata.

Due to the participatory and visible nature of the intervention, neither participants nor data collectors were masked to treatment status. However, data collectors did not participate in intervention implementation and were employed by a separate, independent organization.

# Procedures

The intervention, “Healthy Villages & Schools”, was developed by the DRC government and UNICEF. We focused on the village rather than the school component. This program mobilizes communities to become a “Healthy Village” with 3-6 months of support from government health officials and local NGOs, including approximately $2,000 of financing for new or improved water infrastructure, $2,000 for new or improved sanitation infrastructure, and $3,000 for personnel costs, per village. The mean village size in the intervention group was 456 people (median 400; IQR 502). The seven norms to become a Healthy Village are:

1. There is a dynamic village WASH committee.

2. At least 80% of the population has access to safe drinking water.

3. At least 80% of households use a hygienic latrine.

4. At least 80% of households dispose of their household waste hygienically.

5. At least 60% of the population washes their hands before eating and after going to the latrine.

6. At least 70% of the population is aware of fecal-oral disease transmission and how to prevent this.

7. The village is cleaned at least once a month.

<mark>The program is implemented in nine steps (Table 1) [27].</mark> Table 1. The Healthy Village and Schools Program’s Nine Steps

|Step|Description|
|---|---|
|0|The community learns about the program and collectively decides to adopt it before submitting a formal<br>request to the relevant Health Zone. (A Health Zone is a geographic unit of the Congolese health system<br>that contains roughly ten Health Areas and 100,000 residents, run by a Chief Medical Officer (CMO)).<br>Program protocols state that the entire community should be involved in the decision to participate.|
|1|A statement of agreement between the community and the Health Zone is signed.|
|2|Health Zone officials survey 19 households on knowledge, attitudes, and practices (KAP). The community<br>self-evaluates on eight practices, including handwashing, water use, and sanitation.|
|3|The community spends about 11 hours over five days creating calendars and maps, visiting water points,<br>classifying hygienic practices as healthy or unhealthy, discussing fecal-oral disease transmission,<br>calculating medical costs, and assessing which individuals and organizations influence sanitation and<br>hygiene in the community. This includes 1.5-2 hours in a facilitated activity around the question, “What are<br>the hygiene practices that we want to change in our village?”|
Research Development Institute; (2) spatially-formatted Bangladesh Demographic and Health Surveys (DHS) conducted during the same period. The 2004 Bangladesh DHS includes information on 11,440 women age 10-49 and 4,297 men age 15-54 from 10,500 households, covering 122 urban and 239 rural clusters (NIPORT, et al. 2005). The 2007 Bangladesh DHS includes information on 10,996 women age 15-49 and 3,771 men age 15-54 from 10,400 households in 134 urban and 227 rural clusters (NIPORT, et al. 2009).

The Bangladesh DHS surveys do not include hypertension measures for the women surveyed. However, their information on cluster location allows us to construct monthly indicators of drinking water salinity for each surveyed woman during a nine-year period (2001-2009). The surveys also include temporally-specific information on mortality events for children in the surveyed households. The data therefore permit estimation of a reduced-form model that relates mothers’ consumption of saline water during pregnancy to post-natal mortality outcomes for their children.<sup>7</sup>

# _<u>Model Specification</u>_

We focus on the relationship between drinking water salinity during pregnancy and mortality during the first two months of life. Using data for individual children, we estimate equations for mortality events with the following general form: (1) Pijkl = α0 + ∑me=1 βe Ceijkl + ∑nf=1 βf Mfjkl + ∑sg=1 βg Hgkl + ∑Th=1 βh Sl,t−h + εijkl where, for child i, mother j, household k, DHS cluster l and birth month t:

- P = Event probability

- C = Child characteristics

- M = Mother characteristics

- H = Household characteristics

- St-h = Drinking water salinity index, lagged h months from the child’s birth month

- Ɛ = Random error term

> 7 We should emphasize that this is an exercise in statistical control, not direct experimental control.  A quasi-experimental approach might test for significance using an arbitrary segmentation of sample-population mothers into “exposed” and “unexposed” groups residing in “low-salinity” and “high-salinity” areas.  In contrast, our regression-based approach estimates the size and significance of incremental effects using continuous measures of exposure.  Although our sample population resides solely in the coastal region, our salinity monitoring data indicate that salinity exposure for this population varies from negligible to very high.

5
- 15 -

**<u>Table 3. Endogenous treatment effects model</u>**

|<br>Dependent var.|<br>ln_W_|<br>|<br>_D_||ln_W_||_D_||
|---|---|---|---|---|---|---|---|---|
||Coef.|Std.Err.<br>|Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|
|_D_|0.691|(0.151) ***|||0.674|(0.158) ***|||
|ln_TAXIBE_|||-0.076|(0.037) **|||-0.075|(0.034) **|
|ln_SUBUBUS_|||0.027|(0.040)|||0.042|(0.037)|
|ln_MINCBD_|||0.037|(0.177)|||0.049|(0.103)|
|ln_RENT_|||0.046|(0.012) ***|||0.046|(0.012) ***|
|ln_AGE_|0.182|(0.088) **|-0.324|(0.151) **|0.176|(0.089) **|-0.337|(0.149) **|
|_MALE_|0.495|(0.056) ***|-0.171|(0.091) *|0.493|(0.056) ***|-0.171|(0.091) *|
|_PRIM_|0.194|(0.085) **|0.283|(0.167) *|0.194|(0.085) **|0.302|(0.169) *|
|_SECO_|0.286|(0.088) ***|0.592|(0.170) ***|0.292|(0.088) ***|0.606|(0.171) ***|
|_TERT_|0.622|(0.097) ***|0.678|(0.170) ***|0.634|(0.097) ***|0.676|(0.170) ***|
|_Q1_|-0.715|(0.132) ***|-0.695|(0.215) ***|-0.694|(0.131) ***|-0.649|(0.210) ***|
|_Q2_|-0.526|(0.124) ***|-0.433|(0.189) **|-0.504|(0.122) ***|-0.359|(0.188) *|
|_Q3_|-0.530|(0.110) ***|-0.063|(0.178)|<sup>-0.501</sup>|<sup>(0.109) ***</sup>|<sup>-0.006</sup>|<sup>(0.176)</sup>|
|_Q4_|-0.386|(0.101) ***|0.022|(0.170)|<sup>-0.374</sup>|<sup>(0.102) ***</sup>|0.044|(0.170)|
|ln_HHS_|-0.061|(0.070)<br>|<sup>-0.123</sup>|<sup>(0.124)</sup>|<sup>-0.062</sup>|<sup>(0.072)</sup><br>|<sup>-0.118</sup>|<sup>(0.119)</sup>|
|ln_MINCSB_|-0.011|(0.027)|0.019|(0.048)|<sup>-0.007</sup>|<sup>(0.025)</sup>|0.014|(0.047)|
|ln_ROOM_|0.180|(0.068) ***|0.190|(0.116) *|0.194|(0.066) ***|0.204|(0.115) *|
|_HOME_|-0.074|(0.063)|0.155|(0.132)|<sup>-0.073</sup>|<sup>(0.062)</sup>|0.162|(0.133)|
|_POWR_|0.206|(0.112) *|-0.116|(0.196)|0.199|(0.111) *|-0.100|(0.191)|
|_TAPW_|0.190|(0.105) *|-0.147|(0.191)|0.180|(0.102) *|-0.141|(0.188)|
|_TOIL_|0.050|(0.075)|-0.058|(0.134)|0.044|(0.074)|-0.067|(0.131)|
|Constant|10.986|(0.380)***|0.601|(0.742)|<sup>10.970</sup>|<sup>(0.379)***</sup>|0.375|(0.700)|
|Obs.|858||||858||||
|No. of district|8|||8|0|||0|
|dummies|||||||||
|Wald chi2|580.29||||528.90||||
|Inter-equation c|orrelation||||||||
|parameters:|||||||||
|_λ_|-0.426|(0.101) ***|||-0.402|(0.108) ***|||
|_ρ_|0.766|(0.039) ***|||0.766|(0.039) ***|||
|Endogenous sele|ction||||||||
|test:|||||||||
|Wald chi2|13.56|***|||10.94|***|||

34. One unexpected result may be that _RENT_ has a positive, not negative, coefficient, which is statistically significant. This contradicts economic theory: In a simply monocentric urban model, there must be negative correlation between travel time and housing rents. Our results imply that those who pay more for housing are more likely to use public transit to commute to their workplaces.
**Table 5.2 Manufacturing GDP, 1996 and 2000 (%)**

||**Shares**||**Growth**|
|---|---|---|---|
||**1996**|**2000**|**Average YoY**|
|State Sector|58|50|8|
|Non-state||||
|- Domestic Non-State|28|29|12|
|**_- Formal Private_**|**_10_**|**_9_**|**_9_**|
|- Collectives|1|1|-2|
|- Foreign Invested|13|21|29|
|**Total**|**100.0**|**100.0**|**11**|

Source: GSO, 2001 43. The year 2000 clearly saw a major increase in Vietnam’s total number of operating private companies. The increase in actual operating firms appears, however, to have been significantly less than the record number of registrations. The exact amount of the increase is not clear due to inconsistent data collection methodology by GSO. If GSO 1999 numbers are to be believed, 2000 saw an increase of 6,182 companies—a one year record. However, alternate figures estimated by GSO officials suggest that 1999 figures were underestimated by as much as 2,000 firms, thereby reducing the one-year increase to about 4,000.<sup>29</sup> Even at 4,000, though, 2000 witnessed one of the largest annual increases in private companies since their legalization at the start of the 1990s. The increase is particularly impressive in light of the preceding slow down which saw the number of private companies grow by only four percent in 1998 and possibly even fall in 1999.

**Table 5.3 Total Registrations and Existing Companies, 1998-2000**

||**1998**|**2000**|**1998-2000 Growth**|
|---|---|---|---|
|Total Registrations|37,050|55,120|48.8%|
|ExistingCompanies|26,021|31,593|21.4%|

Source: GSO and MPI, 2001 44. Using more reliable 1998 numbers for comparison, the difference in the discrepancy between total registrations and existing companies remains striking: from 30% in 1998 to 43% in 2000. This discrepancy becomes more dramatic when one focuses in on company forms. While 8,276 new sole proprietorships registered in 1999 and 2000, the overall number of firms only increased by 654. In manufacturing, the number of sole proprietorships actually fell by two percent. Over the same period, limited liability firms increased their number by 64 percent and shareholding companies by 231 percent.

> Webster and Taussig study that showed exports to be the main source of revenues for 82% of private firms with 100 or more employees), given the substantial depreciation of the Vietnamese Dong over the period in question. 29 1999 figures are considered faulty because they were not double-checked by GSO through use of a national firmlevel survey.  In 1998 and in 2000, such surveys were conducted.  In both cases, original numbers provided by local statistics offices were increased.  The fall in firms shown by GSO is particularly worthy of distrust when seen in this light.  Furthermore, GSO experts confirmed that this figure was likely an underestimation of about 2,000 firms. Interestingly, in 1999, the fall in total number of firms is entirely accounted for by the disappearance of some 2,000 sole proprietorships.

22
# **References**

Alexandre, Claire, Ignacio Mas and Daniel Radcliffe (2011), “Regulating New Banking Models that Can Bring Financial Services to All”, in _Challenge_ March/April (forthcoming) Basel Committee on Banking Supervision (2010), “Microfinance activities and the Core Principles for Effective Banking Supervision”, Basel, Bank for International Settlements, August Bellis, James and Lasse Nagel (2010), “Interoperability of Mobile Money Services”, 2009 Mobile Money for the Unbanked Report, London, GSM Association Chatain, Pierre-Laurent, Andrew Zerzan, Wameek Noor, Najah Dannaoui and Louis de Koker (2011), “Protecting Mobile Money Against Financial Crimes: Global Policy Challenges and Solutions”, World Bank, Washington D.C.

Collins, Daryl, Niketa Kulkarni and Shana Gavron (2009), “Portfolios of the Poor: How the World’s Poor Live on $2/day”, Princeton, New Jersey, Princeton University Press Dermish, Ahmed, Christoph Kneiding, Paul Leishman and Ignacio Mas (forthcoming), “Branchless and Mobile Banking Solutions for the Poor: A Survey”, mimeo Dias, Denise and Katharine McKee (2010), “Protecting Branchless Banking Consumers: Policy Objectives and Regulatory Options”, CGAP Focus Note 64, Washington D.C.

Eijkman, Frederik, Jake Kendall and Ignacio Mas (2011), “Bridging the Cash: The Retail End of M-PESA” _Savings & Development_ (forthcoming) Holthausen, Cornelia and Jean-Charles Rochet (2003), “Efficient Pricing of Large Value Interbank Payment Systems”, Center for Economic Policy Research, London 29
# **Deregulation and Firm Investment: Evidence from the Dismantling of the License System in India**<sup></sup>

Ivan T. Kandilov Aslı Leblebicioğlu Ruchita Manghnani North Carolina State University University of Texas at Dallas World Bank JEL Classification: E22, O20, O25 Key Words: Deregulation; Investment; India

>  Ivan T. Kandilov: North Carolina State University, Department of Agricultural and Resource Economics, Box 8109, Raleigh, NC 27695 (E-mail: _<u>ivan_kandilov@ncsu.edu</u>_ ). Aslı Leblebicioğlu: University of Texas at Dallas, Department of Economics, 800 West Campbell Road, Richardson, TX 75080 (E-mail: _<u>asli.leblebicioglu@utdallas.edu</u>_ ). Ruchita Manghnani: The World Bank, 70 Lodhi Estate, New Delhi 110003 (Email _: rmanghnani@worldbank.org_ )
17 Jobs and welfare transfers may also provide larger vote returns than investments in broad public goods for lower ranges of public spending. Nooruddin and Chhibber (2008) argue that Indian state governments indeed struggle with substantial restrictions on the fiscal space available to them for discretionary public spending, and that declining fiscal space within states over time is associated with increasing electoral volatility. Providing infrastructure as a broad public good to sufficiently large numbers of voters is costly in budget terms, especially if recourse to user charges for financing is politically difficult. If prior committed government spending (such as on unionized public sector jobs accumulated over past years) reduces the amount of public resources over which new incumbents have discretion, and raising taxes is costly, populist spending on private transfers may be the dominant electoral strategy compared to the low levels of public goods that can be provided with limited fiscal resources.

Khemani and Wane (2008) provide a model of costly taxation (limitations to fiscal space) and rent-seeking political parties which predicts that when poor swing voters become too demanding, parties can give-up re-election altogether and follow predatory fiscal policies, or come together in coalition governments to continue to serve the vested interests of organized groups at the expense of benefits to (unorganized) voters at large. The section below adapts this Khemani-Wane model to the choice of spending on public goods versus private transfers to swing voters, to provide some theoretical notation for the intuition of this discussion.

The effort here is to find an explanation for the observed correlation between voter turnout and budget composition that does not hinge upon assuming different preferences for the median voter, as in the work of Ghate (2008) and Dasgupta and Kanbur (2007), for example. This is because explanations based on median voter preferences would be hard-pressed to address the challenge of the reported household survey evidence that citizens across caste and
<mark>R4D, Heathsystemsevidence.org, Healthevidence.org and the 3ie database of systematic reviews. Most of these databases are based on regular searches of academic databases and relevant websites, and provide a comprehensive coverage of reviews in their fields.</mark><sup>18</sup> <mark>Nevertheless, authors should supplement this with subject-specific searches in academic databases, relevant websites and the use of list serves. Other techniques such as snowballing</mark><sup>19</sup> <mark>and citation tracking can also be useful in ensuring the search captures all relevant systematic reviews.</mark> <mark>Potentially relevant studies should be downloaded into reference management software and assessed against substantive and methodological inclusion criteria. In the gap maps we have produced so far we have not consistently double screened studies for inclusion, but we sought a second opinion if in doubt, and have recorded the reasons for exclusion. All systematic reviews deemed eligible for inclusion have been assessed a second time before being included in the gap map. In line with good synthesis practice we recommend future gap maps consider double screening, at least for a sample of studies to avoid introducing any researcher biases (Chandler et al., 2011). Gap maps should be updated regularly, at least every two years, to ensure the latest evidence is included.</mark>

# _Coding and critical appraisal_

The next step in developing the gap map is to systematically code and extract data using a structured format, and to assess the quality of included systematic reviews or impact evaluations.<sup>20</sup> Studies are coded according to relevant intervention and outcome categories 18 The 3ie database of systematic reviews is based on regular searches of a range of resources, including libraries of systematic reviews, grey literature and academic databases. Researchers use a systematic review filter developed by an information specialist. This can be combined with subject specific keywords and applied to a broader range of databases than those included in the regular searches, including subject specific databases of particular relevance to the thematic focus of the gap map.

19 Snowballing refers to tracking back and pursuing references of relevant papers from both primary studies and reviews to increase the body of evidence - see Greenhalgh and Peacock (2005).

20 As noted above we recommend that only evidence from systematic reviews is presented as evidence for informing policy and suggest the inclusion of impact evaluations should primarily be for informing commissioning of future primary studies and evidence syntheses. However, in some cases, for instance if the 13
revenues net of tax sharing transfers in 2011. Twenty percent of the total pool is allocated to provinces and the remaining 80% to all cities and districts. The DAU provides a basic allocation to cover wages of provinces cities and districts. The remaining funds are allocated by formula that determines fiscal gap based upon the differences between fiscal needs and fiscal capacity. Formula factors for both provinces and cities are the same but receive differential weights due to the peculiar application to DAU allocation of the weighted coefficient of variation- the so called Williamson‘s Index.

Fiscal capacity of a province is determined by summing up 50% of own source revenues, 80% of non-resource tax sharing and 95% of resource and mining tax sharing. Fiscal capacity of a city or district government on the other hand is based upon 93% of own source revenues, 100% of nonresource tax revenue sharing and 63% of resources and mining tax revenue sharing. The weights for individual revenue sources to determine fiscal capacity varies from year to year as weights are picked up to achieve a given numerical value for the Williamson‘s index for each year. Table 7 provides these choices for numerical values of the index for the past few years.

**<u>Table 7. Williamson’s Index as Equalization Standard in Indonesia: 2005 -2011</u>**

||2005|2006|2007|2008|2009|2010|2011|
|---|---|---|---|---|---|---|---|
|Provinces|0.941|0.769|0.975|0.793|0.802|0.836|0.801|
|Cities and<br>Districts|0.630|0.678|0.699|0.710|0.690|0.718|0.694|

Source: Ministry of Finance, Government of Indonesia Fiscal needs of provinces and cities/districts are determined separately for each of this group by developing a composite index based upon relative population, relative area, relative construction price index, inverse of human development index (comprising arbitrary weights for life expectancy, literacy rate, mean years of schooling and purchasing power adjusted relative real GDP per capita) and inverse of relative nominal per capita GDP. The weights for the above mentioned factors vary for provinces and districts/cities and over time for each group based upon the specified value to be achieved for the Williamson‘s index (see Table 8). The resulting indexes are multiplied by the per capita aggregate spending for the past year to arrive at numerical values of the expenditure need component. DAU allocation for each jurisdiction is then determined as follows: DAU= Basic Allocation + Fiscal Gap (Fiscal needs minus Fiscal Capacity) The DAU is a gross program and compensates a jurisdiction for excess needs but does not tax regions with excess fiscal capacity. The jurisdictions displaying negative fiscal gap (surplus fiscal capacity) e.g. Jakarta metropolitan region receive only the basic allocation and the negative fiscal gap is ignored.

15
25 Republic above which puntive tariffs (from 25 to 30 percent) will be imposed." In addition, in November of 1992 temporary anti-dunping duties were imposed on seamless steel and iron tubes imported from the CEE-3 countries.0 These developments under the new ITA regime show that the Agreements have not deprived the SC of trade management instruments. The removal of quantitative restrictions have exposed CEE-5 steel producers to other, equally potent trade-restraining measues. Given the political clout of steel industries in the EC as well as the dramatic increase in imports from the CEE-3--fueled largely by the redirection of sales from domestic markets and the CMEA-the EC's recourse to these measures comes as no surprise.

**_Cetk_** This group, consisting of 14 eight digit CN items, embraces coal products, iron ore and concentrates, iron and steel wastes (slags and scalings), and manganese ores and concenrates. Except for the FCSK and Poland, other CEE-5 countries are not significant net exporters of these products. There were no EC-wide NTBs affecting CEE imports in 1990, but tariffs were significantly larger than the average tariff on industrial products (see Table 9).

Concessions granted by the EC vary among the CEE-5 countries and they have not been granted by all EC member countries. Except for Germany and Spain, EC duties on imports from the FCSK and Poland are to be eliminated within a year, whereas those on inports from Bulgaria, Hungary and Romania will be reduced by 50 percent on Jamnary 1, 1994 and abolished by the end of 1995. Germany and Spain will maintain duties until the end of the fourth year of the ITAs. As can be seen from Table 9, this differentiation in treatment affects mainly imports from the FCSK (the share of Germany is almost 100 percent) and to a lesser extent Poland (55 percent of EC inports goes to Germany and Spain).

39 Te quotas, set for the 1993-95 period, will change in terms of 1991 imports (1991 =100) according to the schedule: 1993=135; 1994=145; 1995=160. Thus, prohibitive tariffs will not apply annual increases in imports of less than 7 percent in 1993, of less than 8 percent in 1994, and of less thanl 10pernt in 1994. In the case of coils, 1991 levels may be exceeded by 100 percent in the 1993-95 period (Intemational Trade Reporter, May 1993, p.<sup>8</sup> 31).

40 The investigation was initiated in December 1991. The rates varied between 30.4 percent on these imports from the FCSK, 21.7 percent on imports from Hungary and 10.8 percent on imports from Poland. See Eleventh Annual Report from the Commission to the Euroeam Parliament on the Community's Anti-Dumping and AntiSubsidy Activities (1992), Commission of the European Communities, Brussels, 28 October 1993.