rafmacalaba/datause-encoder-data huggingface dataset:
15 through trimming and weighting. The implication is that it is not just that the households in the sampled non-NCMS counties are quite different in terms of the probit model’s covariates from the enrolled households, but that the probit estimated on NCMS county households is a poor basis on which to match NCMS households with households living in non-NCMS counties. Because of the lack of balancing achieved when the controls are households in non-NCMS counties, the results below are exclusively for the comparison between enrolled households and non-enrolled households living in NCMS counties.

# VI. **IMPACT ESTIMATES**

Table 9 and Table 10 report the household-based estimates of NCMS impacts on health service utilization and household health spending. Table 11 reports selected impacts by income decile. Included in Table 9 and Table 10 are the changes before and after the introduction of NCMS for NCMS counties as a whole, for NCMS and non-NCMS households separately, and the difference in these latter two changes (i.e. the basic difference-in-difference estimator without any adjustment for differences in initial conditions). Also included are the two sets of matching estimates, the first weighting the observations of non-enrolled households by their propensity score, and the second weighting by the kernel weights. The t-statistics are based on bootstrapped standard errors in the case of the kernel weighted results with 100 replications. The decile-specific ATT estimates in Table 11 were obtained using kernel weighting. Table 12 reports the impacts of NCMS on health facilities—separately for general THCs, central THCs, and county hospitals. Matching variables included 2003 (i.e. pre-NCMS) values of ownership (government, public, or other), ‘sponsor’ (small city county, rural county, urban township, rural township), actual number of beds, total number of staff, space owned by the facility, GDP per capita of the county, government health spending of the county (more precisely ‘total operating expenses on public health’), and province (including this encourages but does not force matching by province). The ATT estimates are reported as percentage changes on pre-NCMS averages to facilitate comparisons across outcome measures and facility types. The t-statistics in Table 12 are based on bootstrapped standard errors with 50 replications.<sup>32</sup>

> 32 It could be argued that confidence intervals are meaningless, as the dataset is a census of facilities rather than a sample. However, through the matching process, not all ‘treated’ or ‘untreated’ facilities are used, which may be argued to provide a rationale for computing standard errors.
**Shelter from the Storm? Household-Level Impacts of, and Responses to, the 2015 Floods in Malawi**

Nancy McCarthy<sup>†</sup> , Talip Kilic<sup>‡</sup> , Alejandro de la Fuente<sup>*</sup> , and Josh Brubaker<sup>#1</sup>

**JEL Codes:** D60; I38; Q12; Q54.

**Keywords:** Extreme Weather Shocks; Flood; Household Welfare; Malawi; Sub-Saharan Africa.

1 The senior authorship is shared between Nancy McCarthy and Talip Kilic. †Principal Researcher, LEAD Analytics, Washington, DC.<sup>‡</sup> Senior Economist, Living Standards Measurement Study (LSMS), Survey Unit, Development Data Group, The World Bank, Rome, Italy.<sup>*</sup> Senior Economist, Poverty and Equity Global Practice, The World Bank, Washington, DC.<sup>#</sup> Research Analyst. LEAD Analytics, Washington, DC. The authors would like to thank Heather G. Moylan for her inputs into the survey design and the field staff training, and (in alphabetical order) Simone Balog, Ruth Hill, and the participants of the World Bank Global Disaster Reduction and Recovery Facility (GFDRR) seminar (October, 2016 - Washington, DC), and the World Bank LSMS International Conference: The Use of LSMS Data for Research, Policy, and Development (February, 2017 - Dar es Salaam, Tanzania) for their comments on the earlier versions of this paper.
# **Appendix A: On the Electricity Usage Criterion**

The Factories Act requires registration for enterprises with a) 10 or more workers with power b) 20 or more workers. So, for establishments with power, the 10 workers or more criteria applies, while for establishments without power, 20 or more workers is the threshold. The electricity usage criterion was likely relevant seven decades ago when the Act was passed, but what is the situation today?

Let us start by considering the 2010-11 round of NSSO enterprise survey. After excluding firms that report missing employment numbers, we find that there are an estimated 3,239 firms that report 0 electricity expenses in the NSSO 2010-11 survey. The maximum employment reported by these firms was 4 and the median was 1. With this information, the electricity usage criterion is irrelevant. The “10 workers or more” criterion for identifying compliance is sufficient. However, electricity usage information is missing for about 5.1 million firms. Out of which, nearly 99.4% have employment size of less than 10. The remaining 29,864 firms, account for 11.6% of the firms with 10 or more employees in the NSSO. Of course, if electricity usage was positive for these enterprises then there would be no difference when compared to using just the “10 workers or more” criterion for identifying informality and ignoring electricity usage—we would be back to Table 1 of the main text of the paper.

A difference would arise if some of the enterprises in the 10-19 worker range did not use electricity. Table A1 presents the number of firms and workers in categories A, B, C and D when missing electricity is set equal to 0. Let us now compare this with Table 1. It is seen that even with the extreme assumption embodied in Table A1, for category B the discrepancy in the number of firms is 22,469 and the discrepancy in the number of workers is 290,996. That is, by ignoring electricity usage we may be over estimating the number of firms in category B (Evaders) and their total employment by at most by 8.7% and 6.4% respectively. Similarly for category C (Avoiders) we may be overestimating the number of firms and total employment by 8.4% and 7.2% respectively. The discrepancies are quite small. Further, the average characteristics of the different categories are not very different from each other.

Although not presented here, the discrepancies for 2005-6 are 12.5% (firms) and 9.5% (workers), and the average characteristics do not different very much either. For 2000-01 there are no missing values but the discrepancies between the two criteria lead to a discrepancy in the numbers for category I of 15.5% (firms) and 12.0% (workers).

We would argue, therefore, the electricity use criterion, important though it may have been in 1948, is increasingly irrelevant. Ignoring it is unlikely to affect our analysis of compliance greatly.

19
mentioned studies.

In 2006, prior to program rollout, villages were randomly assigned to the program. The process ran as follows. The implementing partner aggregated all villages into 560 village clusters. Clusters were, in turn, aggregated into 83 lottery bin areas, which largely corresponded to Chiefdoms. Next, half of the clusters in each lottery bin were selected for treatment using public lotteries. This approach improves balance between treatment and control by geographic features, including remoteness, poverty, institutions, and social composition. In total, 280 village clusters and 1,250 constituent villages were selected for treatment. The remaining villages were assigned to the control status.

The program was implemented in two phases: a village-level phase and a subsequent village cluster level phase. At the village level, local election teams were established and trained to mobilize and guide village populations. The idea was to ensure a good understanding of the program as well as the subsequent election for newly created village-level management committees. These ten-member strong committees were required to contain five men and five women. Next, these committees, in consultation with the population, decided how to allocate an envelope of $3,000 for a maximum of two projects. The proposed project(s) was then voted on by the whole village. In the two years following project selection, the committees were responsible for project implementation, and were held accountable by village populations via regularly scheduled town hall meetings.

Subsequently, program activities took place at the village cluster level. A new village cluster-level committee was formed by selecting members from the constitutive village-level committees, again ensuring that half of the members were women. Each village cluster received a block grant of $50,000 to $70,000 (depending on population size) to implement infrastructure projects that were chosen by the inhabitants of the constituent villages via an election. Next, the cluster committees were responsible for project implementation and were held accountable by cluster populations.

In total, 2,335 village-level and 315 village cluster-level projects were undertaken. Appendix B gives an overview of implemented projects. The majority of these projects took place in the education and health sectors.

The program was implemented with a variation in treatment related to the gender composition of the management committees. Specifically, in 43 (not randomly selected) lottery 5
# **Oral Democracy and Women’s Oratory Competency in Indian Village Assemblies: A Qualitative Analysis**<sup>**1**</sup>

## **Paromita Sanyal Cornell University**

## **Vijayendra Rao Development Research Group, The World Bank**

## **Umang Prabhakar Cornell University**

**JEL Codes:O12, Z13, J16**

**Keywords: Deliberation, Collective Action, Gender, Participation, India**

1 We are grateful to the South Asia Food and Nutrition Security Initiative (SAFANSI) for their support.
# <u>How Much  Do Courts Matter?</u><sup>7</sup>

Increasing evidence from around the world suggests that when court performance improves, it can support economic development by facilitating market transactions.<sup>8</sup> To take an example, China, in 1979 undertook an economic reform program that provided incentives for new enterprise creation, increased inter-provincial trade, and allowed the entry of foreign investors. With business expanding, the number of cases filed in commercial courts increased dramatically. Between 1979-82, and 1997 there has been more than a 100-fold increase in the average number of commercial disputes filed in the courts. Beginning from around 14,000 a year; by 1997, 1.5 million new cases had been filed. What is telling is that at about the same time, the number of commercial disputes arbitrated by community committees, the traditional mediation mechanism, hardly increased.<sup>9</sup> Liberalisation, more trade within the country and business with foreigners meant an increased demand for formal dispute resolution mechanisms. In this context, community mechanisms were not appropriate for resolving disputes with non-community members.

Poland and Slovakia in the early 1990s also demonstrate how regime change can render existing dispute resolution mechanisms ineffective and how the state can step in to promote business in a new economic environment. During these early years, farmers faced long delays in payments for produce delivered upstream. They had difficulty in enforcing contracts with outside business partners. Responding to this challenge, small farmers, and later, firms processing produce, changed their strategy and integrated their

> 7 See also World Bank, 2001.

> 8 In this paper, I focus on the effectiveness of the judicial system in the commercial function of courtsenforcing contracts between private parties.

> 9 WDR 2002, “Building Institutions for Markets”, World Bank, 2001; and Pie (2001).

6
and locations/countries.<sup>5</sup> NEA and IEA (2005) calculate levelized costs of electricity generation technologies for various countries using data collected from a survey of system operators and power producers. Table 3 presents the range of levelized costs for various technologies. As can be seen from the table, the cost ranges are very wide for most of the technologies as electricity generation costs vary across countries. The table also presents the weights of different components of the levelized costs for different technologies. While capital is the main component for non-fossil fuel technologies, fuel costs account for more than half of the total costs with most fossil fuel based technologies.

The comparisons of levelized costs of electricity generation technologies significantly vary across existing studies for reasons related to fuel price projections, differences in material and labor costs across regions and studies, employment of different discount rates, and differences in exchange rates. In many instances, costs are not comparable across the studies due to a large divergence in their underlying assumptions and data. For example, cost estimates in the NEA and IEA (2005) study are expressed in 2003 values, whereas the cost estimates of CUPC (2008) and Lazard (2008) are expressed in 2008 dollars. Moreover, while the NEA and IEA study uses fuel price forecasts, CUPC (2008) and Lazard (2008) use current fuel prices and keep the price fixed over the life of the technology. Moreover, some studies follow financial analysis (e.g., CPUC, 2008; Lazard, 2008), whereas others (e.g., NEA & IEA (2005) follow economic analysis and hence the costs are not comparable across the studies.

The levelized costs presented in Table 3 do not include externality costs. Thus, the costs reported in Table 3 are not the total costs to the society. Moreover, if costs of externalities, such as costs of local air pollution, GHG emissions and other externalities (e.g., impacts of wind power on land use), are taken into appropriate account, the total costs of electricity generation technology also would change. Using a life cycle cost approach, some studies (e.g., Owen, 2004; Roth and Ambs, 2004) show that wind power could be as competitive as fossil fuels if environmental externalities are appropriately accounted for in calculating true social costs.

> _5 However, the ESMAP & WB (2008) study does not calculate levelized costs of electricity generation._

10
**Table 6:  Bills Related to Intergovernmental Transfers (House/Senate)**

|Congress (years)|IRA|LGU share in the national<br>wealth|Total|
|---|---|---|---|
|8(87-91)|4/7|7/1|11/8|
|9(92-95)|7/7|2/2|9/9|
|10(96-98)|20/19|7/0|27/19|
|11(99-01)|22/10|11/0|33/19|
|12(02-04)|22/11|7/0|29/11|
|13(05-07)|19/9|9/5|28/14|
|14(08-10)|23/5|7/4|29/9|
|**Total**|117/68|50/12|167/80W|

Although the vast majority of these legislative initiatives have failed to culminate in legislation as shown above, the distribution of the types of bills filed serve as an indication of the aspects of the LGC about which legislators hold a degree of discontent. Broadly speaking, most of the bills touch on those fundamental aspects of the LGC that we as well as numerous other analysts have identified as problematic. This can be interpreted as latent demand for change in the LGC, although it is not clear from the data whether there is anything resembling a degree of consensus about the direction and the extent of changes sought.

_Prospect of an IRA reform:_ In the 14<sup>th</sup> Congress (2007-10), a number of bills for reforming the IRA were filed in both chambers, some aimed at increasing the LGU shares and others proposing a new distribution formula.<sup>20</sup> Of these, two Senate bills and five House bills aimed at increasing the LGUs’ IRA share from the current 40 percent of the internal revenues to either 50 or 60 percent. Two Senate bills (including one of the ones for increasing the LGUs’ IRA share) proposed automatic retention of IRA transfers by LGUs. One Senate bill and one House bill each called for deduction of costs of devolved services from the IRA calculation so that those LGUs which have received costly devolved functions (e.g., tertiary hospital) could offset these costs _and_ receive an IRA transfer following the existing formula. Finally, several House bills were filed to change the IRA distribution formula in a variety of ways, including some proposals that appeared to be in line with technically ideal formula (e.g., greater considerations of poverty or LGUs’ revenue capacity) presumably to improve horizontal equity of the distribution formula.

Those bills that aimed to change the IRA distribution either by deducting the cost of devolved functions or by explicitly changing the formula itself were likely to face resistance from those LGUs that stood to lose their IRA shares since any change in the distribution within the existing IRA envelope would imply a zero-sum situation among the LGUs. Since most members of the Lower House who were elected from specific geographic districts were likely to face conflicting interests among the LGUs in their jurisdictions, with some losing and others gaining from the redistribution of the constant IRA resources, the dominant strategy (in a game theoretic sense) among these legislators is likely to be one of inaction. In contrast the bills aimed to increase the LGUs’ share in the IRA envelope could garner stronger support since such

> 20 Bills related to other aspects of the LGC may also have been filed but we did not track these specifically.

17
WPS3808

# Network Effects of the Productivity of Infrastructure in Developing Countries*

## Christophe Hurlin **

### **Abstract**

Using panel data models we examine the threshold effects of the productivity of infrastructure investment in developing countries. We consider various specifications of an augmented production function that allow for endogenous thresholds. More precisely, these specifications are tested in a panel threshold regression. Our main robust result is the presence of strong threshold effects in the relationship between output and private and public inputs. Whatever the transition mechanism used, the testing procedures lead to strong rejection of the linearity of this relationship. In particular, the productivity of infrastructure investment generally exhibits some network effects. When the available stock of infrastructure is very low, investment in this sector has the same productivity as non-infrastructure investment. On the contrary, when a minimum network is available, the marginal productivity of infrastructure investment is generally largely greater than the productivity of other investments. Finally, when the main network is achieved, its marginal productivity becomes similar to the productivity of other investment.

Keywords : Infrastructure, Threshold Panel Regression Models. J.E.L Classi.cation : C82, E22, E62.

## World Bank Policy Research Working Paper 3808, January 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> *I would like to thank Santiago Herrera for his support and his comments on a previous version of this work. I also thank Mohamed Belkir for his comments.

> **LEO, University of Orléans. Rue de Blois. BP 6739. 45067 Orléans Cedex 2. France. Email address: christophe.hurlin@univ-orleans.fr.
Swain, R.B. and Varghese, A., 2009. Does self help group participation lead to asset creation? _World Development_ , 37(10), pp.1674-1682.

Tesoriero, F. 2006. Strengthening communities through women’s self help groups in Southern India, _Community Development Journal,_ 41 (3), 321-33 Tiwari, M. and Ibrahim, S., 2012. Sustainable human development at the grass roots: Different contexts, similar ingredients?. _Oxford Development Studies_ , _40_ (1), pp.69-85.

Trommlerová, S.K., Klasen, S. and Leßmann, O., 2015. Determinants of empowerment in a capability-based poverty approach: Evidence from the Gambia. _World Development_ , 66, pp.1-15.

Vandewalle, L., 2017. The Role of Accountants in Indian Self-Help Groups: A Tradeoff between Financial and Non-Financial Benefits. _World Development_ , 93, pp.177192.

Van Kempen, L., 2009. The ‘downside’ of women empowerment in India: An experimental inquiry into the role of expectations. _Social Indicators Research_ , 94(3), p.465.

Van Ootegem, L. and Verhofstadt, E., 2012. Using capabilities as an alternative indicator for well-being. _Social Indicators Research_ , 106(1), pp.133-152.

33
sampled villages. The survey firm translated the questionnaires into Khmer and an independent third party back-translated them into English. The research team participated in the interviewer training conducted by the local firm’s fieldwork manager. Field staff was organized into six groups, each comprising of four interviewers, one supervisor and one field editor. All supervisors had several years of data collection experience in Cambodia and were responsible for household sampling and quality control procedures. Editors supported supervisors doing spot-checks, interviewer observations, and conducted independent re-interviews of at least 20% of interviews in each village.<sup>6</sup>

**_Household survey_ .** The household survey includes information about family structure and socio-economic background. We constructed a dwelling quality index and household assets index.<sup>7</sup> Both indexes were then standardized and summed up to create a wealth index. We use quintiles and quartiles of this index to analyze the cognitive gap in rural Cambodia. To construct a binary measurement of household wealth, we define low living standard as belonging to the lowest 2 quintiles of the wealth index. We use household size ��� 4.48, ��� 1.68� and the binary measure of wealth ��� 0.40� as measures of socio-economic background at the household level.

**_Caregiver survey_ .** The educational background of the caregiver is measured by years of schooling ���3.48, ���3.15�. For better interpretability, we use a binary indicator for completion of primary school (6 years) ���.266� in our regressions. Caregiver non-verbal

> 6 Initial re-interviews were limited to a set of 20 questions to reduce the burden to respondents. In case of inconsistencies between the initial interview and the re-interview, households were called or visited again to obtain clarity.

> 7 The direction of each categorical variable for dwelling quality (quality of the floor, quality of the roof, electricity, toilet...) was first assessed using a Multiple Correspondence analysis. Then, each variable was coded using the sign of the coordinate of the first MCA dimension. We then took the average of the standardized version of each coded variable. The Cronbach alpha is 49%. For the household asset, we simply standardized each asset and took the standardized version of the sum. The Cronbach alpha is 65%.

9