rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Sample versus universe of PSA applicants, 2011-2014

||Not sa|mpled|Sam|pled|||
|---|---|---|---|---|---|---|
||(1)<br>Non-ben|(2)<br>Ben|(3)<br>Non-ben|(4)<br>Ben|(5)<br>Norm diff<br>(1) v (3)|(6)<br>Norm diff<br>(2) v (4)|
|Ln(ejido area, ha)|8.41|8.07|8.12|8.28|-0.19|0.14|
|Ln(common property area, ha)|7.87|7.60|7.59|7.98|-0.11|0.17|
|Ln(ejidatarios)|4.63|4.57|4.64|4.56|0.01|-0.00|
|Ln(non-ejidatarios)|1.25|1.65|1.41|1.31|0.07|-0.14|
|Ln(area submitted, ha)|6.76|6.61|6.56|6.80|-0.16|0.16|
|Average slope (deg)|12.22|12.36|11.02|10.98|-0.12|-0.13|
|Average elevation (m)|1410.45|1357.59|1358.32|1271.73|-0.04|-0.06|
|Ln(km to any road)|7.86|7.65|7.61|7.67|-0.17|0.02|
|Ln(km to major city)|4.49|4.34|4.60|4.59|0.13|0.30|
|Ln(km to city>5,000)|3.30|3.12|3.20|3.25|-0.11|0.11|
|Mean canopy cover|41.38|43.96|47.37|53.64|0.14|0.23|
|% indigenous in municipality|0.13|0.21|0.26|0.38|0.26|0.27|
|Deforestation risk (INE)|0.05|0.06|0.06|0.05|0.05|-0.10|
|Obs|1169|808|375|487|2839|2839|

Table shows mean values of variables for communities that were sampled versus those that were not sampled, and breaks these down by beneficiary and non-beneficiary communities. The last two columns show the normalized differences in means between sampled and non-sampled non-beneficiaries (column (5)) and sampled and nonsampled non-beneficiaries (column (6)).

13
<!-- Start of picture text --> Rule of Law 2009 (WGI) Government Effectiveness 2009 (WGI)<br>-15 -5 5 1.5 2.5 -1.5 -5 Re) 1.5 2.5<br>= 90 See oe 90 b ° e%e «6<br>BP os oe? fae 75 *e 8% %. af<br>~E 60 oc.ef ek,Bees 8 60 e +49 aeee 0°<br>2 oS Ct oe 9 Pe °<br>£ 45 “ e 45 e@ Se<br>‘6 e° 8 e<br>< 30 e 30 e<br>E 90 . 7 ° &e 04 ce fo oF 0 %<br>2”0 e are® _ 9 7 oe 852e 2 -275.% et e<br>S 60 o° 60 OL Ad<br>Re5 =e Jfve % e -*° oo 8 e<br>£45 b 45 ><br>5 fe a)<br>a 30 e 30 e<br>-15 -5 5 1.5 2.5 -1.5 -5 Re) 1.5 2.5<br>Control of Corruption 2009 (WGI) Regulatory Quality 2009 (WGI)<br>Source: WGI & IAB<br><!-- End of picture text -->
# **Box  3.1 Pathogens in wastewaters**

_Viruses_ : Numerous viruses may infect the intestinal tract and be passed in the feces, whereupon they may infect new human hosts by ingestion or inhalation. One gram of human feces may contain 10<sup>9</sup> infectious virus particles, regardless of whether the individual is experiencing any discernible illness. Concentrations of 10<sup>5</sup> infectious particles per liter of raw wastewater have been reported. Although threy cannot multiply outside a suitable cell host, the excreted viruses may survive for many weeks in the environment. Five groups of pathogenic excreted viruses are important: adenoviruses, enteroviruses (including poliovirus), hepatitis A virus, reoviruses, and diarrhea-causing viruses (including norovirus and rotavirus).

_Bacteria_ : Human feces contain large numbers (~10<sup>9</sup> −10<sup>11</sup> per gram) of commensal bacteria of many species; these are non-pathogenic and essential for the proper functioning of the intestinal tract. However, some bacteria cause excreta-related diseases—for example, _Campylobacter_ and _Salmonella_ (diarrhea), _Vibrio cholerae_ (cholera), _Shigella_ spp. (bacillary dysentery) and _Salmonella typhi_ (typhoid fever). The numbers of these pathogens can be as high as 10<sup>6</sup> −10<sup>8</sup> per gram of feces and 10<sup>3</sup> −10<sup>5</sup> per liter of wastewater.

_Protozoa_ : Several species of protozoa infect humans—for example, _Entamoeba histolytica_ (which causes amebic disentery), and _Giardia lamblia_ , _Cryptosporidium parvum_ and _C. hominis_ (severe diarrhea). Numbers can reach 10<sup>3</sup> −10<sup>5</sup> per gram of feces and 10−10<sup>3</sup> per liter of wastewater.

_Helminths_ : Helminths are worms. In wastewater-irrigated agriculture the most important intestinal worms are _Ascaris lumbricoides_ (the human roundworm), _Trichuris trichiura_ (the human whipworm), _Ancylostoma duodenale_ and _Necator americanus_ (the two human hookworms) (these four are nematode worms), and _Taenia saginata_ and _T. solium_ (the beef and pork tapeworms) (these two are cestode worms). The eggs of all these helminths are voided in the feces. _Ascaris_ is generally taken as the ‘reference’ helminth as it is the most common (ascariasis affects ~1.2 billion people, almost exclusively in low- and low-middle-income countries), the female worms produce vast quantities of eggs (~200,000 per day), and the eggs can survive for long periods of time (many months to several years) in the environment. _Ascaris_ egg numbers in wastewaters are ~10−10<sup>3</sup> per liter.

_Sources:_ Adapted from Feachem _et al_ ., 1983; further information in WHO, 2008b.

23
into both a private and a merit good as it is a necessity for life. In cities, these challenges are compounded by the fact that the cost of building multiple water systems is excessive and thus unlikely. Water must be supplied through a single network, that therefore has a single owner—a monopolist (World Bank, 2017). A natural monopolist accrues unavoidable advantages, regardless of being public or private in its engagement with regulators and customers. Furthermore, investments in infrastructure are lumpy – considerable costs are incurred to make improvements. However, infrastructure investments are not enough. Institutions that generate the right incentives will allow infrastructure to be effective (Ashraf et al., 2016; Allcott et al., 2016). How well economies address these challenges will go a long way in determining the service delivery quality. In the sample for this study, a substantial proportion of the urban population has access to improved water sources, 8 of 12 economies had 90 percent or more urban population access to improved water in 2015.<sup>3</sup> Three of the four remaining economies have at least 80 percent or more urban population access to improved water. Only Angola is the standout with 74 percent access.

The source of the data for the study is surveys of informal firms collected by the World Bank’s Enterprise Analysis unit. Informal firms are defined as firms that are not registered with the relevant authorities. While there are other measures of informality such as size of activity, lack of financial statements, mobility, and access to bank credit (Benjamin and Mbaye, 2012), the broader literature tends to use registration as the criterion for informal firms and this is a convenient definition for data collection (Loayza and Rigolini, 2011; Rand and Torm, 2012). The study finds that the total duration of water shortages has a statistically significant negative effect on the labor productivity of informal firms. A one standard deviation increase in a days’ worth duration of

> 3 Improved water sources include piped water on premises, public taps or standpipes, tube wells or boreholes, protected dug wells, protected springs, and rainwater collection.

5
# **III. Data: The LSMS Methodological Validation Program (MVP)**

In order to address the gaps in the area measurement literature and extend the applicability of studies to the plot conditions common to developing countries, the Living Standard Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) of the World Bank has prioritized land area measurement in its research agenda. The Global Strategy to Improve Agricultural and Rural Statistics has also identified improving the measurement of crop productivity, and by necessity farm area measurement, as a top priority (World Bank et al., 2010). Because agricultural statistics are often marred by controversy over methods and overall quality, stringent validation of the available measurement methodologies is essential. With financial support from UK Aid, the Living Standards Measurement Study (LSMS) has partnered with national statistical offices in the design and implementation of methodological validation studies. The methodological studies completed through the LSMS have a particular focus on the feasibility of implementation in large-scale household surveys, thus the recommendations on best practices are made in consideration for both the highest data quality and practicality of implementation under the constraints common in this type of survey. To date, methodological validation studies on land area measurement have been conducted in Tanzania (Zanzibar), Ethiopia and Nigeria. This paper uses the data collected within these studies, which are briefly described in what follows.

Data for Zanzibar, Tanzania come from the Measuring Cassava Productivity (MCP) study. The MCP focused on testing several methods for measuring cassava production, including crop-cutting, harvest diary, various means of assisted harvest diary, and various recall periods. To complement the measurement of cassava production, cassava plots were also measured using three methods of area measurement. Fieldwork extended from June 2013 to May 2014, with area measurement completed from August 2013 to January 2014. The study was conducted in two districts, one on Unguja and one on Pemba Island, Zanzibar. The sample consisted of 1,247 households, with 1,932 cassava plots measured for area. Partners in the study included the Ministry of Agriculture and Natural Resources, Zanzibar, the Office of the Chief Government Statistician, Zanzibar, and the World Bank. The handheld GPS Unit used in the study was a Garmin eTrex 30.

The second data set used in this paper comes from the Ethiopia Land and Soil Experimental Research (LASER) study. The LASER study involved methodological validation of plot area measurement, soil fertility testing, and measurement of maize production. Area measurement and soil fertility testing was conducted on up to two randomly selected plots per household (where applicable, one pure-stand maize plot was selected for crop-cutting). The questionnaires were administered using computer-assisted personal interviewing. Fieldwork was conducted in multiple waves. Post-planting activities were 12
43

# _Figure 3._ Sampling process for at-scale professional development programs

<!-- Start of picture text --> Emailed World Bank Practice Managers<br>21 countries identified; 4 lost due to<br>across 5 regions; Identified interested<br>inappropriate timing or lack of Task<br>countries and Task Team Leaders<br>Team Leader interest<br>Sought Ministry of Education approval Failed to obtain Ministry of<br>Education approval in 2 countries<br>Data collected in 15 countries<br>Data collection completed in 14 countries<br>Identification<br>Country approval<br>Implementation<br>Included<br><!-- End of picture text --> <!-- Start of picture text --> 21 countries identified; 4 lost due to<br>inappropriate timing or lack of Task<br>Team Leader interest<br><!-- End of picture text -->
**Table 5: Household Composition, Rice Prices, and the type of Activity**

_limited to children 6-15 in households with 3-15 members_

||Participa|tion||||Hours -|OLS|||
|---|---|---|---|---|---|---|---|---|---|
||-1|-2|-3|-4<br>Within|-5|-6|-7|-8|-9<br>Within|
|Dependent Variable:|Wage<br>Work|Within<br>Hh Work|Within<br>Hh Agr|Hh Home<br>Ent|<br>Within Hh<br>Home Prod|Wage<br>Work|Within<br>Hh Work|Within<br>Hh Agr|Hh Home<br>Ent|
|Rice Price|0|0.05|0.053|-0.015|-0.05|-1.639|-7.894|-0.168|-1.704|
||[0.061]|[0.134]|[0.129]|[0.053]|[0.158]|[1.748]|[3.475]**|[2.493]|[1.163]|
|Rice Price * # People 0-5|0.004<br>[0.009]|-0.06<br>[0.034]*|0.058<br>[0.029]**|0.014<br> [0.016]|-0.076<br>[0.037]**|0.119<br>[0.229]|-1.463<br>[0.851]*|0.612<br>[0.647]|-0.177<br>[0.335]|
|Rice Price * # People 6-15|0|0.036|0.005|0.005|0.007|0.087|0.138|-0.345|0.044|
||[0.008]|[0.024]|[0.021]|[0.011]|[0.028]|[0.207]|[0.647]|[0.463]|[0.189]|
|Rice Price * # People 16-24|-0.006|-0.048|0.021|-0.006|-0.039|-0.369|-1.246|-0.185|-0.453|
||[0.009]|[0.026]*|[0.023]|[0.014]|[0.030]|[0.286]|[0.642]*|[0.522]|[0.261]*|
|Rice Price * # People 25-64|-0.01|-0.012|-0.046|-0.021|0.09|0.77|1.272|-0.853|0.381|
||[0.028]|[0.054]|[0.043]|[0.019]|[0.062]|[0.721]|[1.229]|[1.092]|[0.398]|
|Rice Price * # People 65+|0.003|0.045|-0.064|0.041|0.033|0.385|1.609|-1.579|1.081|
||[0.012]|[0.046]|[0.042]|[0.025]|[0.046]|[0.378]|[1.424]|[1.198]|[0.465]**|
|# People 0-5|-0.001|0.099|-0.046|-0.017|0.116|-0.076|2.483|-0.36|0.127|
||[0.011]|[0.037]**|[0.031]|[0.018]|[0.040]**|[0.261]|[0.935]**|[0.704]|[0.386]|
|# People 6-15|-0.001|-0.052|0.008|-0.002|-0.024|-0.1|-0.221|0.527|-0.024|
||[0.009]|[0.026]**|[0.023]|[0.013]|[0.030]|[0.228]|[0.729]|[0.503]|[0.220]|
|# People 16-24|0.007|0.01|-0.03|0.002|-0.018|0.354|0.54|0.123|0.445|
||[0.011]|[0.030]|[0.025]|[0.016]|[0.033]|[0.322]|[0.739]|[0.573]|[0.309]|
|# People 25-64|0.004|-0.011|0.05|0.011|-0.127|-1.135|-2.044|1.065|-0.579|
||[0.032]|[0.060]|[0.048]|[0.020]|[0.069]*|[0.816]|[1.249]|[1.134]|[0.421]|
|# People 65+|-0.009|-0.075|0.079|-0.047|-0.068|-0.582|-1.981|1.96|-1.248|
||[0.014]|[0.050]|[0.047]*|[0.029]*|[0.052]|[0.433]|[1.604]|[1.383]|[0.528]**|
|Observations|9510|9510|9510|9510|9465|9503|9457|9503|9503|
|R-squared|0.07|0.39|0.35|0.12|0.32|0.07|0.44|0.36|0.1|

See notes to table 4 for other notes about the regressions. See table 2 for description of dependent variables. Robust standard errors in brackets are corrected for psu-time clustering. * significant at 10%. ** Significant at 5%.
# **7 Policy Implications**

Our paper studies the effect of weather shocks in 2018 on consumption in Afghanistan, a country affected by conflict that is also witnessing extreme weather events driven by climate change. We combine a geo-coded cross-sectional household survey (IE-LFS 201920) with satellite-based precipitation, temperature, and vegetation measures. The 2018 drought measured by positive temperature anomalies and negative vegetation anomalies decreased consumption and increased poverty rates.

Working in a data scarce environment, we are able to deliver important insights on the relationship between weather shocks and welfare in a conflict affected country, showcasing techniques that can be used in similar context where only cross-sectional data is available. We find evidence that households decreased non-food consumption to save themselves from food insecurity. While both food and non-food consumption expenditures decreased with adverse vegetation anomalies, households were able to maintain their food consumption by reducing non-food monthly per capita consumption when faced with bad temperature anomalies.

In terms of resilience, households owning land could mitigate the adverse effects of droughts, whereas livestock ownership could not protect households against welfare losses due to severe droughts. This confirms that asset ownership may offer a means of insuring against weather shocks by allowing households to either sell or borrow against the asset; however, this coping mechanism is often imperfect (Dercon, 2002; Fafchamps et al., 1998). Given the ongoing conflict at the time in Afghanistan, we also find suggestive evidence that consumption was disproportionately lower in Taliban districts in 2018 that experienced bad vegetation anomalies.

In terms of vulnerability, while 47 percent of households were classified as poor in the survey, a simulation exercise based on historical weather data suggests that 62.5 percent of individuals have a greater than 1 in 4 chance of falling into poverty due to weather shocks. This is likely a lower bound, given that the methodology does not account for displaced households or adaptive strategies. It highlights the country’s heightened vulnerability to drought, which climate change will only exacerbate.

These results highlight the importance of policies to mitigate the impact of climate change on the most vulnerable in Afghanistan. A warming climate means more droughts, higher temperature, and increased risk of flash floods. These shocks risk exacerbating an already dire humanitarian situation. In 2021, the country faced one of the worst droughts in 19
-24 -

**Historical Excess of Loss Reinsurance Rates**

<!-- Start of picture text --> 8.0% = _ _ _ _ _ _ - - - - . .- . . . . . .-......<br>7.0% - __ _<br>6.0% -_ __ _ _ _ _ _ _<br>60%<br>80 D ° h- __ _ ~ ~ ~ .- < t -- ,__ _ - - _ . __ . . ---<br>E 4.0% / ,' -.--- __> _. _ -Caribbean Mid-% |<br>2 / / '-_ < |-4- OECS Mid-50%/ l<br>3.0% _____<br>2.D% x'<br>0.0%<br>1991 1992 1993 1994 1995 1996 1997 1998<br><!-- End of picture text -->

<!-- Start of picture text --> Source: Benfield Greig<br><!-- End of picture text --> Excess of loss reinsurance rates (rates-on-line)' followed the same pattern both in the Caribbean as a whole and in the OECS countries as seen in the above chart. High volatility was experienced in excess of loss premiums at the middle (50%) layer of reinsurance cover, that is, at the midpoint in the range of excess-of-loss coverage layers. The period between 1993-95 was the peak with rates on line reaching 7%. At the lower layers of cover which would be accessed more often (e.g.: first 5%), rates were much higher during 1993-95, in some countries reaching up to 16% of loss amounts insured.

> 8 The term "rate on line" refers to the premium dollar price as a percentage of the loss level (in $ damages) to be covered under an excess of loss contract. In this sense it is akin to cost of capital or the interest cost of capital. Rate on line should not be confused with the premium as a % of total insured value. Since rate on line is the price of compensation for exact losses measured in dollar terms, such loss levels are generally a sub-set of the total insured value (and based on a probabilistic application to expected and maximum losses).
regulatory measures. We control for these factors using private credit to GDP ratio (from Djankov et al 2007) and number of procedures required to start a business, number of procedures required to enforce a contract and an index of the difficulty of hiring labor (from World Bank’s Doing Business project, 2004).

Regression results with these controls are reported in columns 1 and 2 of Table 2. English common law continues to show a positive and significant effect on the dependent variable. Private credit to GDP and all the Doing Business indicators show a significant relationship with the dependent variable in the expected direction.

Next, we control for the index of creditor rights from Djankov et al 2007 and measures of institutional quality as perceived by the firms (from WBES) which include: the level of confidence in the legal system for upholding contract and property rights in a dispute ( _Property rights_ ), efficiency of courts in enforcing their decisions in business disputes ( _Enforcement_ ), quality of power supply ( _Power_ ), and the severity of business regulations, high interest rates and labor regulations as obstacles for the firm’s operations. Each of the obstacles is ranked by firms on a 1-4 scale with a higher value indicating a more severe obstacle. _Property rights_ , _Enforcement_ and _Power_ take values 1-6 with a higher value implying better quality. The motivation for the firm-level measures is to capture possible heterogeneity in their enforcement and how they affect firms.

Regression results in columns 3 and 4 of Table 2 show that controlling for the variables discussed above does not change our main results much. Most of the firm-level measures show significant effects on government helpfulness in the expected direction: better quality of institutions and less severe obstacle implies a more helpful government.

5
**Table 1.  Kenya’s Financial System in Regional Comparison**

||Private Credit to GDP|Liquid Liabilities to GDP|Net interest margin|
|---|---|---|---|
|Kenya|23.7%|36.0%|6.6%|
|Tanzania|12.3%|26.3%|6.6%|
|Uganda|7.2%|20.7%|11.7%|
|Sub-Saharan Africa (median)|12.3%|23.0%|6.6%|

Source: Beck, Demirguc-Kunt and Levine (2009) Kenya’s sector faced major crises in the 1980s and 1990s, due to under-capitalization, high levels of non-performing loans and weaknesses in corporate governance. NBFIs were most affected, but the number of failing commercial banks increased as well in the 1990s. The crisis culminated in 1992, when - according to Honohan and Laeven (2005) - Kenya suffered formally a systemic banking crisis.

**Table 2. Growth and Structure of the Banking Sector, 2000 to 2007**

||2000|2001|2002|2003|2004|2005|2006|2007|
|---|---|---|---|---|---|---|---|---|
|Private Credit/GDP|25.6%|24.1%|23.5%|23.1%|23.2%|23.8%|24.3%|22.4%|
|Loans (Net of Provisions)/GDP|20.9%|20.2%|20.9%|20.5%|22.8%|23.5%|23.6%|24.8%|
|Real GDP Growth|0.5%|4.5%|0.5%|2.9%|5.1%|5.7%|6.1%|6.9%|
|**_Share of Banking Sector Assets_**|||||||||
|Foreign|44.3%|46.3%|48.3%|48.7%|45.3%|43.4%|43.8%|43.5%|
|Private Domestic|21.9%|22.7%|22.6%|24.1%|25.7%|28.7%|29.9%|31.0%|
|Government|7.1%|7.1%|6.6%|6.0%|6.2%|5.6%|5.3%|4.8%|
|NBK|6.1%|5.9%|5.7%|5.2%|5.5%|5.2%|4.8%|4.4%|
|Government-influenced|26.7%|23.9%|22.5%|21.2%|22.7%|22.2%|21.1%|20.7%|
|KCB|16.6%|15.0%|12.8%|11.7%|11.9%|11.8%|11.5%|11.9%|
|Cooperative|5.7%|5.2%|6.5%|6.5%|8.3%|8.2%|7.7%|6.9%|
|**_Share of Total Loans (Net of_**<br>**_Provisions)_**|||||||||
|Foreign|39.9%|40.8%|41.9%|43.7%|42.5%|42.3%|41.0%|44.5%|
|Private Domestic|22.1%|22.0%|22.6%|24.4%|25.4%|28.8%|31.4%|31.5%|
|Government|10.0%|9.8%|10.0%|9.7%|8.4%|7.7%|7.3%|2.0%|
|NBK|9.2%|8.9%|9.0%|8.7%|7.6%|7.3%|6.8%|1.6%|
|Government-influenced|28.0%|27.4%|25.5%|22.3%|23.6%|21.3%|20.3%|22.0%|
|KCB|16.5%|15.8%|12.8%|10.7%|11.5%|9.9%|10.5%|11.5%|
|Cooperative|5.8%|6.3%|8.3%|7.7%|9.2%|8.7%|7.2%|7.7%|

Sources: For Private Credit/GDP, Beck, Demirguc-Kunt and Levine (2009). For real GDP Growth and nominal GDP, World Development Indicators. Data for all other calculations are from the Central Bank of Kenya.

3
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# **Table A6. Variables and Their Description**

||**Variable**|**Coding**|
|---|---|---|
||Child aged 0-5|At least one household head’s child between 0 and 5years old(inclusively)= 1|
|Household<br>structure|Females and Males by age group:<br>6–9, 10–15, 50 and over|At least one other female in the household in one of the three age groups = 1<br>At least one male in the household in one of the three age groups = 1|
|Husband’s<br>Employment type|Regular<br>Self-employed<br>Casual<br>Unemployed or not in the LF|Regular wage employment = 1<br>Self-employed = 1<br>Casual labor = 1<br>Reference: unemployed or not in the labor force|
|Education|No education (reference)<br>Below primary<br>Primary completed<br>Secondary<br>Post-secondary (andabove)|5 Dummies for education categories<br>Reference: no education|
|Social Group|Scheduled Caste (=1)<br>Scheduled Tribe (=1)<br>Other Backward Caste (=1)<br>Others (General) (reference)|4 Dummies for social group<br>Reference: Others<br>In the NSS, data for OBCs were not available before 2004.|
|Religion|Muslim<br>Hindu<br>Other religion|Muslim = 1<br>Other religion = 1<br>Reference: Hindu|
|Age|Age<br>Age squared|Age in years<br>Age squared as continuous variable|
|Household size|Number of familymembers|Continuous|
|Region|North<br>East<br>West<br>South<br>NE<br>Central|6 Dummies:<br>**East**= 1 if West Bengal, Orissa, Andaman and Nicobar Islands<br>**West**= 1 if Gujarat, Maharashtra, Goa, Dadra and Nagar Haveli, Daman and Diu<br>**South**= 1 if Tamil Nadu, Karnataka, Kerala, Andhra Pradesh, Lakshadweep, Pondicherry<br>**North-East**= 1 if Manipur, Tripura, Arunachal Pradesh, Sikkim, Assam, Meghalaya, Mizoram, Nagaland<br>**Central (reference)**= 1 if Bihar, Jharkhand, Uttar Pradesh, Uttaranchal, Madhya Pradesh, Chattisgarh|
|NSS Round/<br>Year|38<sup>th</sup>round (1983)<br>50<sup>th</sup>round (1993-94)<br>61<sup>st</sup>round (2004-05)<br>64<sup>th</sup>round (2007-08)<br>66<sup>th</sup>round (2009-10)<br>68<sup>th</sup>round(2011-12)|6 Dummies for NSS survey rounds<br>Reference: 38<sup>th</sup>round|

25