rafmacalaba/datause-encoder-data huggingface dataset:
connected), but also favored areas where natural resources were abundant for generating electricity (rivers for hydroelectric power, and coal for thermal power). Connections in dwellings expanded rapidly during the 1980s and 1990s. While some rural households use diesel generators to create electricity. The bulk get their electricity from a wired connection to above-ground power lines, which in turn are connected to village transformers that are linked via a feeder (or medium-voltage) line to power substations further away on the grid.

According to official statistics, more than 90 percent of villages are currently electrified, in that they have a feeder connection to the grid (International Energy Agency, 2002). However, far from all households in these villages are connected to the electricity grid, and the extent of household connections varies widely on a regional basis. Grid management in India has been carried out on a regional basis since the 1960s, with five main regions comprising the grid, namely the North, East, West, Northeast, and South (Pandey 2007). Each state and union territory of India falls in one of these regions; power is also shared across regions.

During the same period there was a pronounced increase (although starting from a low base) in non-agriculture wage and self-employment work (Foster and Rosenzweig 2003). Both men and women experienced a large gain in non-agricultural wage employment over the period, although much of this has been attributed to a rise in rural public expenditure promoting rural nonfarm industries in the 1980s (Sen 1998). The period also experienced rising rural consumption levels and primary school enrollment.

A key question is how India’s dramatic increase in household-level electrification impacted household economic behavior. The India Rural Economic and Demographic Survey (REDS) covers this key period of rising household level electrification and includes detailed information for a panel of households. This appears to be the only long-period household panel data set available for a rural economy that underwent extensive electrification. While providing a unique opportunity for assessing the long-term impacts of electrification, the use of the data for this purpose still requires a number of assumptions, which we outline later.

We use the 1981-82 and 1998-99 (henceforth 1982 and 1999) rounds of the REDS, conducted by the National Council for Applied Economic Research (NCAER).<sup>16</sup> The two

> 16 There was a first round of the REDS (called ARIS) in 1970-71 which was nationally representative of rural areas excluding households residing on the Andaman and Nicobar, and Lakshadwip Islands (Foster and Rosenzweig 2001).  However, its questionnaire was more restricted in scope than that for the subsequent rounds. There is also a

15
starting the plot area measurement (which was then conducted with a Garmin eTrex 30 handheld GPS unit).

For our analysis, the plot records were retained only if they possessed corner point information and had a crop type record for the 2018 _meher_ season. Furthermore, if the location information was duplicated across two or more plots, we dropped all duplicated records, except in cases where one, and only one, of the duplicated records had a high degree of confidence assigned to their location data quality - in these cases, the one record with the high degree of confidence was kept and the remaining records were dropped. Lastly, a small share of plots was dropped due to low confidence in their location data quality, as captured by the Android tablet. We treated plots that were cultivated with any maize plantings as maize plots, and otherwise labeled them as “nonmaize.” Tables 3 and 4 show the breakdown of ESS 2018/19 _meher_ season plots, by georeferenced information availability and by maize cultivation status, respectively.<sup>10</sup> The final analysis sample includes 11,905 ESS 2018/19 plots, originating from 2,090 households. Since the ESS 2018/19 did not capture full plot boundaries, our analysis focuses primarily on Malawi, with the findings from Ethiopia playing a supporting role.

Table 3: ESS 2018/19 _meher_ season plots by <u>georeferenced information availability</u>

|**Plot category**|**ESS 2**<br>|**018/19**<br>|
|---|---|---|
||Obs|%|
|Plots with no geolocation information|1,168|8.7|
|Plots with a corner point, but dropped from analysis|299|2.2|
|Plots with a corner point, used for analysis|11,905|89.0|
|**Total # of Plots**|**13,372**|**100.0**|
|**Total # of Associated Households**|**2,1**|**99**|

Table 4: ESS 2018/19 _meher_ season plots by maize cultivation status, conditional on being used for analysis

|**Crop type**|**ESS 20**<br>|**18/19**<br>|
|---|---|---|
||Obs|%|
|Maize|1,867|15.7|
|Non-maize|10,038|84.3|
|**Total # of Plots**|**11,905**|**100.0**|
|**Total # of Associated Households**|**2,0**|**90**|

10 The share of plots with no geolocation information is significantly lower in the ESS 2018/19 data mainly due to the reliance on resident enumerators, as opposed to mobile survey teams in the context of the IHPS 2019 and the IHS5 2019/20.

10
||||Use Private||Characteristics<br>o<br>Coatract|f Project||
|---|---|---|---|---|---|---|---|
|Country,<br>Title of Project|Wusder, Status<br>of Activity|Project Summary|Distribution<br>System|Pply<br>Rfelit|witb Private<br>Providera|Tanit.|Evaluation<br>m|
|m<br>M<br>UU/S|MY-SMD<br>S<br>iin<br>(|cmnt'4,||||||
|St.<br>_LiaW1:_<br>Contraceptive<br>DistributionIn<br>Factories<br>In<br>St. rucia|WAID Population<br>Council Opera-<br>tional Reseac<br>Project,<br>1986|lthi ms a dec_med<br>eqeriat<br>comparing factory<br>otivation ad<br>sale of oetaceplve<br>(orals,<br>condams d ftC)<br>by factory<br>workers witb a *m<br>**as**back-p.<br>vran<br>vi1idtiwn m<br>provider.<br>Employe<br>did sinficantly<br>better.|Yes, to obtain<br>contraceptives<br>for "It l<br>t<br>factories|Po|No|Yes. for<br>.otivator/<br>**sles**staff|YTe|
|Medco:<br>F_tly<br>Pl1i:g<br>Delivery Strat_-<br>cle<br>in an<br>ldustrial<br>Setting<br>(Cuidad Juarez)|OSAID Popalation<br>Cucil<br>Opera.<br>tional<br>eserch<br>Project.<br>1987|Althougb a local grup prowie<br>servies<br>already to plans<br>In<br>C.J.,<br>th<br>facilities<br>that<br>ser<br>10,00<br>_ployees are to<br>Include a**promoterprogram**<br>udure<br>employee are selected,<br>traind,<br>_mupervised, ad_mplied<br>by the<br>locl<br>group, NWAC.|Tea|lb|no|Ye, for<br>sotvatore|N<br>aslac|
|Philigpplws<br>r<br>Industry-besad<br>Pa 'ly Plamain<br>Project|Poputloai<br>Canter<br>Founmmation,<br>1965-1987|Ia-plant volunteer<br>(Ilv)**we"**<br>trained to prmote famil_y plannig<br>use.<br>**UIs**referr-d<br>fellow<br>mployees to cmpay<br>**1seiars,**<br>company rars, ad outside clinics<br>**as appropriate.**<br>The total<br>-mber<br>ofacceptorsroe<br>In 90 of the<br>firm.|Yea|Tea|**Yes**|**Yes**of ItV G<br>project ain-<br>otrattonstaff|Oaly tracked<br>mw acptore|
|Peru:<br>Electro Ums<br>**and**HILO<br>Family PluMing|WSAD TIPnS<br>Project,<br>Qngoig|After quick overview of d nad,<br>undlettook a cost/benefit<br>of add-<br>Ing family_p?lis_<br>to caleu<br>health benfit<br>**packges**(15,000<br>employees ad dependents**at**the<br>two firms).<br>Arranged for<br>contract**with loal**<br>PVO to buy<br>family pluaing<br>ervices.|N/A|Yer|tPrepid con-<br>tract vith<br>local FPA|_No_|No|
|Turkey<br>Proviston of<br>Famly Planntng<br>Inforstion<br>A<br>ServIck9 In 15<br>Workplaces|Pathfinder Fund,<br>1986|Services will be provided to<br>20,000 workers to 15 factories.<br>Grantee will psy to equip<br>existing<br>health cinice<br>and<br>personnel, train**a**full-time<br>nursemdwife<br>per site sad MD<br>oversite*<br>ad provide educatton<br>and<br>otivation<br>for employees and<br>their spouses.|No|Yea, but**door**<br>paying for It|No|Yes, for**nurse**<br>and midwives|_NO_|
22 Table 2: Descriptive statistics—HCFP status and determinants

||Full s|ample|HCFP e|ligible|
|---|---|---|---|---|
||Mean|Std. Dev.|Mean|Std. Dev.|
|_Insurance status_|||||
|Free health certificate|0.09|0.29|0.22|0.41|
|Insurance for the poor|0.05|0.22|0.15|0.36|
|Insurance for people of merit|0.03|0.18|0.05|0.21|
|Student insurance|0.05|0.23|0.03|0.17|
|Compulsory insurance|0.14|0.35|0.10|0.30|
|Voluntary insurance|0.01|0.10|0.01|0.09|
|Other insurance|0.00|0.06|0.00|0.07|
|_Determinants of insurance status_|||||
|Poor in 2003|0.10|0.30|0.32|0.47|
|Decision 135 beneficiary|0.25|0.43|0.81|0.39|
|Ethnic minority|0.18|0.39|0.42|0.49|
|Ethnic minority x Mountainous province|0.07|0.25|0.21|0.41|
|Per capita consumption|4235.91|3651.33|2805.27|2016.91|
|Male household head|0.80|0.40|0.84|0.37|
|Age of household head|48.60|13.42|46.75|12.94|
|Literate household head|0.91|0.28|0.86|0.35|
|Education grade 1—household head|0.02|0.13|0.02|0.14|
|Education grade 2—household head|0.06|0.23|0.06|0.24|
|Education grade 3—household head|0.07|0.26|0.07|0.26|
|Education grade 4—household head|0.07|0.25|0.08|0.27|
|Education grade 5—household head|0.11|0.32|0.12|0.33|
|Education grade 6—household head|0.06|0.23|0.06|0.23|
|Education grade 7—household head|0.06|0.23|0.06|0.23|
|Education grade 8—household head|0.04|0.20|0.04|0.21|
|Education grade 9—household head|0.23|0.42|0.22|0.42|
|Education grade 10—household head|0.03|0.16|0.02|0.14|
|Education grade 11—household head|0.02|0.15|0.02|0.14|
|Education grade 12—household head|0.16|0.37|0.09|0.28|
|Urban|0.23|0.42|0.12|0.33|
|Northeast|0.14|0.35|0.24|0.43|
|Northwest|0.06|0.23|0.14|0.35|
|North Central Coast|0.11|0.32|0.13|0.33|
|South Central Coast|0.09|0.29|0.09|0.28|
|Central Highlands|0.07|0.26|0.10|0.30|
|Southeast|0.13|0.34|0.07|0.25|
|Mekong Delta|0.20|0.40|0.12|0.32|
|Household size|5.08|1.86|5.48|2.07|
13 some of which are not. Assume we have government spending data at this level. Assume too we have data on outcomes (and non-spending determinants of these outcomes) at the micro level (in the application below the unit is the individual, but the method can be applied to, say, a school or a health facility). We use _i_ to denote the _i_ th individual, and _j_ =0,1,3 to indicate whether we referring to the (observed) baseline scenario ( _j_ =0), the (unobserved) scenario without the project ( _j_ =1), and the (observed) scenario with the project ( _j_ =3). Thus: where _N_ 1 _j_ and _N_ 2 _j_ are the number of individuals in areas 1 and 2 respectively in scenario _j_ , and<sup>_Y_</sup> 1 _j_<sup>and</sup> _Y_ 2 _j_ are the mean values of _Yi_ 1 _j_ and _Yi_ 2 _j_ respectively.

Assume the data are available at two dates: before the project is implemented (which corresponds to point _a_ in Figure 1), and after the project has been implemented or at least after it has begun implementation (which corresponds to point _e_ ). The data need not be panel data. Of course, we have no data corresponding to the counterfactual scenario, point _b_ , since this is never observed. This absence of data is addressed below. Let _t_ =0,1 denote whether the time period is prior to the project’s start or afterwards. We assume that for the two observed sets of data, outcomes are linked to government spending _Xi_ (other determinants are omitted from the equations below for simplicity; they are not excluded from the empirical model) as follows:
# 4.2 Private Returns on SLM

Farmers will adopt a technique only if it demonstrates (public or private) positive returns. Recent observational and experimental evidence documents positive maize yield eects of SLM techniques in southern Africa, as well as substantial labor savings (Beaman et al., 2014; BenYishay and Mobarak, 2014; Haggblade and Tembo, 2003; Thierfelder et al., 2015). We examine private returns to SLM to explain the observed increase in adoption among CFs, beyond their willingness to comply with the training. In practice, we modify (1) to estimate the ITT eects on maize yields and revenue, input use, and on-farm labor allocation.

Table 7 (Panel A) presents maize yields (revenue per hectare) and total revenue accrued from a direct SLM training. Given our low level of statistical power, we present results on the full sample and winsorizing yields at 1% to account for outliers.<sup>32</sup> Results show positive though imprecise point estimates, indicating eect sizes on the order of 0.13-0.24 standard deviations.

Since most SLM practices disseminated have water-conserving properties (Liniger et al., 2011), we further account for the possibility that rainfall patterns in the main growing season may mediate the impacts of the intervention. In practice, we add a control variable to distinguish eects by whether the community experienced a dry spell during the survey round: Dry Year, which takes value one if the cumulative precipitation in a given location fell below the 30-year rst quartile, zero otherwise.<sup>33</sup> In line with recent experimental evidence on pit planting (Beaman et al., 2014), we nd that training CFs on SLM has positive and large, if noisy, eects on maize yields and revenues during drier spells (Table 7, Panel B). The magnitude of the eects, on the order of 0.35 standard deviations, or 37% increase in both yield and total revenue, are in line with ndings from the literature that claim increases of 50 to 100% (Haggblade and Tembo, 2003). The measured yield eects from receiving a SLM training corroborate the notion that farmer adoption of SLM technologies is motivated by short-term yield advantages. In the absence of any statistically signicant dierences in input use as a result of the direct training (Table 8), these yield eects in dry conditions are credibly attributable to SLM adoption.

> 32Winsorizing yields at 1% does not aect the control group mean as the entire top 1% of the distribution is in the treatment group.

> 33For these computations, we use NASA 1/2x 1/21981/2013 Precipitation data available at http://power.larc.nasa.gov/cgi-bin/cgiwrap/solar/hirestimeser.cgi?email=daily@larc.nasa.gov. This measure of weather event is used by others in the literature, see for instance Jayachandran (2006).

18
suggests a 10% increase in employment is associated with about a 0.012 output increase. The second row would suggest a 10% increase in capital is associated with about a 0.001 output increase. The linear role for counts of distinct material inputs into the establishment is thus bracketed by these other inputs into the firm production.<sup>8</sup> Column 3 introduces the district average value of this indicator variable for above median inputs, finding a positive effect that is almost statistically significant. This coefficient suggests that moving 10% of local plants from below median values to above median values is associated with a 0.013 gain in log output for the plant. Column 4 introduces both measures and finds that the coefficients are comparable to their individual estimations.

Column 5 shows similar results when controlling for log manufacturing employment in district per square kilometer, log manufacturing employment in a plant’s district-industry per square kilometer, log share of local manufacturing employment in the unorganized sector, and log share of district-industry manufacturing employment in the unorganized sector. The first two of these controls are often associated with the urbanization and agglomeration premiums of dense locations, and we use a per square kilometer normalization as Indian districts vary in spatial size.<sup>9</sup> Columns 6 and 7 split the sample by traditional versus modern groups. The plant-specific input count effect is present in both groups, with some emphasis for traditional industries, while the local area affect is more closely associated with the traditional industries.

Table 9 repeats the analysis in Table 8 for establishments in the unorganized sector. In this sector, we generally find a more modest connection between higher input counts and plant output levels after conditioning on the base inputs. Compared to the organized sector, the 8 Since Olley and Pakes (1996), there has been a substantial amount of work attempting to address the potential simultaneity bias between input usage (broadly defined to include labor, materials, and so on) and plant productivity. Our current analysis cannot estimate these more complicated models given that it considers a crosssection of data. Looking forward, there is some scope for these models despite the fact that the general Indian data are repeated cross-sections rather than panel data for firms (e.g., Sivadasan 2009). The first step is to prepare consistent measures of input counts across Indian data surveys for many years. We are currently working on this task to provide a longitudinal dimension to this project.

9 See, for example, Ciccone and Hall (1996), Duranton and Puga (2004), and Rosenthal and Strange (2004). Indian studies include Lall, Shalizi, and Deichmann (2004), Lall and Mengistae (2005), Deichmann et al. (2008), and Fernandes and Sharma (2011).

17
<!-- Start of picture text --> Singapore<br>8.1% 16.0%<br>75.9%<br>< 15 years 15-64 years > 64 years<br><!-- End of picture text --> Source: Singapore Statistic (Sing-Stat)

**Figure 3: Economic Structure of IDR and Singapore (2005) – GDP by Major Economic Sectors**

<!-- Start of picture text --> GDP by Major Sectors in IDR, 2005<br>Manufacturing<br>47%<br>Services<br>50%<br>Agricultural<br>3%<br>Value Added of Manufacturing Industries in IDR, 2005 Value Added of Services Industries in IDR, 2005<br>Professional & Business<br>Chemical and Chemical products Food and Beverages 15%<br>12.1% 11.2% Wholesale and Retail42% Services Sector7%<br>Electronics and Electrical<br>products32.1% Others44.6% Tourism & Hospitality17%<br>Transport related13% Medical Educational6%<br><!-- End of picture text --> Source: South Johor Economic Region: Comprehensive Development Plan, 2006-25.

<!-- Start of picture text --> Value Added of Manufacturing Industries in Singapore, 2005<br>General Manufacturing<br>Industries<br>9.2%<br>Transport Engineering Electronics<br>10.4% 34.0%<br>Precision Engineering<br>12.7%<br>Biomedical Manufacturing Chemicals<br>18.8% 14.8%<br><!-- End of picture text --> Source: Economic Development Board 11
50

**Table A.6. Economic value and geographic data used to calculate boundary cases of wealth distribution across districts.**<sup>**3031**</sup>

||Percent of Total<br>Ho Chi Minh<br>City GDP|Percent of Total<br>Ho Chi Minh City<br>Area_(Equitable_<br>_wealth_<br>_distribution)_|Wealth Factor<br>(Percent GDP/<br>Percent Area)|Normalized<br>Wealth Factor<br>_(Inequitable wealth_<br>_distribution)_|
|---|---|---|---|---|
|Binh Thanh|4.5%|1.00%|4.47|7%|
|District 1|6.9%|0.38%|18.12|28%|
|District 10|2.9%|0.29%|10.03|15%|
|District 3|2.7%|0.24%|11.14|17%|
|Go Vap|5.4%|0.95%|5.67|9%|
|Phu Nhua|2.3%|0.24%|9.69|15%|
|Tan Binh|5.9%|1.05%|5.61|9%|
|Other Districts|69.5%|96%|0.72|1%|

# **_Uncertainties Associated with Future Vulnerability_**

The next factors relate to the future population and economic vulnerability to different flood depths.

## **Population Vulnerability**

We consider a range of population vulnerability curves, varying the percent of the population that is affected as a function of depth. Highest vulnerability occurs when 100% of those living on the ground floor are affected by inundation of only 0.1m and 100% those living on other stories are affected by an inundation of 1.1m (1m higher than the ground floor threshold). Lowest vulnerability occurs when 2% of those living on the ground floor are affected by inundation of 1m and, correspondingly, 2% of those living on other stories are affected by an inundation of 2.1m.

## **Economic Vulnerability**

For demonstration, we use a different approach to examining a range of vulnerabilities for economic assets. Here, we assume that 0m of depth corresponds to 0% loss, while a depth of 5.5m results in 80% loss, based on initial data in the RoyalHaskoning interim study report (2012). We vary the shape of this curve, i.e. how changes in depth affect loss between these two endpoints. In the highest vulnerability curve, 22% of economic value is lost at a depth of 10cm; in the lowest vulnerability curve, essentially 0% of economic value is lost at a depth of 10cm.

_________

> 30 Tax revenue data are from the Ho Chi Minh City Statistics Book, Table 3.11.

> 31 Geographic data are from the Ho Chi Minh City Statistics Book, Table 2.01.
example if the construction of new transport links or electricity connections only leads to new business development with a significant lag, growth rate themselves have a distributed lag structure, and we are often dealing with relatively small samples, which casts doubt on the asymptotic properties of the IV-estimators (Holtz-Eakin, 1994). A related question may therefore concern the power of the tests involved. A closely related strategy is employed by Devarajan et al. (1996), who use a forward lag structure for the dependent variable. Alternatively, Easterly and Rebelo (1993) use as instruments continent dummies, as well as country level structural characteristics such as population size and share of agriculture in GDP. It is unclear, however, whether these last two are plausibly excluded from the growth regression as they claim.

Table 3 shows that the use of instruments makes a significant difference to the results derived in the context of cross-country regressions, where endogeneity problems are likely to the more acute, while very little difference is observed in the context of panel data studies.<sup>20</sup> Table 3

|||-1|0|1|
|---|---|---|---|---|
|Panel|||||
||IV||||
|Yes (51)|No (34)|2.94%|35.29%|61.76%|
||Yes (17)|5.88%|35.29%|58.82%|
|No (26)|No (20)|15.00%|45.00%|40.00%|
||Yes (6)|0.00%|33.33%|66.67%|

An alternative strategy to define instrumental variables is to make use of geographical or industry-level correlations. Examples are found in Holtz-Eakin (1994), who instruments US state level public capital by using other neighboring states' average levels of public

> 20 Garcia-Milà et al. (1996) are indeed unable to reject exogeneity in a panel of US states.

20
<!-- Start of picture text --> Capacity is mostly allocated to passengers Freight income cross subsidises passengers (data<br>for India)<br>100% = , 4000<br>seihda| | 3.500 180%<br>20%|-. |F 3000 160%san +| — ee<br>60% - F 2300 30% 4<br>50% + + 2000 400% 4<br><!-- End of picture text -->

<!-- Start of picture text --> 40% +; + 1500 8 go% 4+<br>20% + F 1000 60% | SE<br>10% + - 500 40% +}<br>oe 4 . r = + O 20% +<br>China United States Russia India Canada 0% | = =<br>mums Tonne-km per person mmm Passenger km per person 1999-2000 2008-2009 2009-2010 2010-2011 2011-2012 2012-2013<br>—t— Passenger + freight tonne-km ——Passengercost recovery 9 ——Freight cost recovery<br><!-- End of picture text -->