rafmacalaba/datause-encoder-data huggingface dataset:
**VI.3 Stage 2 – Exporter-, Importer, and Year-Fixed Effects OLS Corrected for Heckman Selection Bias and Heterogeneity Bias via y** **_hat_**

||**TELECOMMS**<br>**EQUIPMENT**|**MEDICAL**<br>**DEVICES**<br>**(HR**<br>**1990)**|**MEDICAL**<br>**DEVICES**<br>**(HR**<br>**1993)**|
|---|---|---|---|
|**Dependent variable **|**lntelcoexp**|**lnmedexp**|**lnmedexp**|
|**lngdprep**|0.1106261<br>(0.6104795)|1.75559***<br>(0.225831)|1.692831***<br>(0.2294447)|
|**lngdppcrep**|1.746018***<br>(0.6607502)|0.2826898<br>(0.2559243)|0.3348802<br>(0.2590798)|
|**lngdppar**|0.6503729<br>(0.6119718)|0.5847753**<br>(0.2310453)|0.5174858**<br>(0.2391158)|
|**lngdppcpar**|1.844077***<br>(0.6682847)|0.8184822***<br>(0.2523145)|0.8721921***<br>(0.2576831)|
|**lndistwces**|-1.01317***<br>(0.0340775)|-0.9058929***<br>(0.0193517)|-0.9029701***<br>(0.0193878)|
|**comlang_off**|0.508059***<br>(0.0699714)|0.4443507***<br>(0.0393899)|0.4435004***<br>(0.0396502)|
|**contig**|-0.4813352***<br>(0.1198314)|-0.3288868***<br>(0.0661288)|-0.3309807***<br>(0.06659)|
|**colony**|0.6258515***<br>(0.1101568)|0.632416***<br>(0.0621199)|0.6396563***<br>(0.0625021)|
|**lleither**|-0.7340733***<br>(0.2514461)|-0.62303***<br>(0.1469085)|-0.6297676***<br>(0.1480454)|
|**isleither**|-0.0910346<br>(0.1117298)|-0.1391341**<br>(0.0655817)|-0.1434634**<br>(0.066014)|
|**WTO**|dropped<br>to<br>fulfill<br>exclusion restriction|dropped<br>to<br>fulfill<br>exclusion restriction|dropped<br>to<br>fulfill<br>exclusion restriction|
|**FTA**|0.3468349***<br>(0.0903682)|0.4192014***<br>(0.0594404)|0.4003746***<br>(0.0589595)|
|**MRA(****_sector_) **|-0.0865054<br>(0.0761364)|0.2213371***<br>(0.0656481)|0.1813529***<br>(0.0637033)|
|**HR(****_sector_) **|-0.2676397*<br>(0.1458103)|-0.4336519***<br>(0.0819718)|-0.4285944***<br>(0.0796982)|
|**HR(****_sector_)oecd_sgp**|0.0682216<br>(0.1097871)|0.0734726<br>(0.0631058)|0.09314<br>(0.0624056)|
|**HR(****_sector_)_dev**|0.2721036**<br>(0.1072043)|0.0546173<br>(0.0640355)|0.0250158<br>(0.0631993)|
|**Yhat(****_sector_) **|6.331152***<br>(0.4149608)|3.052422***<br>(0.2058448)|3.064316***<br>(0.2074153)|
|**No. of observations**|28,985|40,608|40,608|

*** significant at the 1% level ** significant at the 5% level * significant at the 10% level

39
|**Table 6 – Milken Institute survey instrument of best**<br>**performing cities in the U.S.**<br> <br>|
|---|
|**Component**<br>**Weight**|
|Job Growth(1999–2004)<br>0.143|
|Job Growth(2003–2004)<br>0.143|
|Wage & Salary Growth(1998–2003)<br>0.143|
|Wage & Salary Growth(2002–2003)<br>0.143|
|Short-Term Job Growth (July 2004–<br>July2005)<br>0.143|
|Relative High-Tech GDP Growth<br>(1999–2004)<br>0.071|
|Relative High-Tech GDP Growth<br>(2003–2004)<br>0.071|
|High-Tech GDP LocationQuotient<br>0.071|
|Number of High-Tech GDP<br>LQ>1<br>0.071|

# **6. ICLEI Sustainable Development Program**

After Member States of the United Nations committed themselves to implement the Habitat Agenda (Istanbul, 1996), local authorities were asked to monitor and evaluate their own performance in working toward sustainable urban development. International Center for Local Environmental Initiatives, ICLEI, is the international association of local governments created by more than 475 cities, towns, and counties. ICLEI undertook a program to develop indicators to monitor how local authorities and national governments are implementing key sustainable development programs consistent with Habitat Agenda.

ICLEI participated in the development of the Global Urban Observatory by helping to establish National Urban Observatories (NUOs) and Local Urban Observatories (LUOs) in Uganda, Zimbabwe, Chile, Ecuador, Poland and Bulgaria. Chile’s Urban Observatory has created a well established website in which different urban experiences are listed<sup>19</sup> . More than two hundreds indicators, organized into 10 categories are presented for every Chilean city and town.

# **7. City Mayors**

‘City Mayors’ is an Internet website of international experts committed to promote strong cities and good local government. In its yearly survey, the group examines how initiatives are being developed to achieve solutions to urban problems such as housing, transport, education and employment, as well as environmental, technological, social and security challenges.

The survey analyzes 40 indicators according to five different categories: Stability; Healthcare; Culture & Environment; Education; and Infrastructure. The survey is structured according to quantitative and qualitative data. Each indicator is given a rating between one and five, (one means there is no impact).

> 19 http://www.observatoriourbano.cl

35
- 7 -

analyzing demand side issues: (i) price elasticity of energy demand and (ii) conditional factor demand elasticity with respect to output.

First, suppose that the capacity to supply energy is not sufficient enough to meet the potential demand, as in Albania and Macedonia. Then, any external adverse shock will easily translate into a considerable shift of the supply curve upward. With a highly elastic demand, for instance, greater than 0.5 in absolute terms, the domestic energy price can be kept at a reasonable level, because the shock would be absorbed largely by quantity adjustments. A 10 percent increase in energy tariffs would reduce energy consumption by more than 5 percent. If energy demand is inelastic, for instance, less than 0.1 in absolute term, the economy will have to experience a sizable adjustment in energy prices. These are movements along the demand curve, as illustrated in Figure 1.<sup>6</sup> It is a political decision whether or not to pass the high energy prices realized at the new equilibrium on to end-users. A significant increase in retail energy prices may not be acceptable for industrial and commercial users, let alone for residential customers (affordability issue).<sup>7</sup> In theory, the Ramsey pricing rule suggests that if governments (or operators) can discriminate energy prices among different customers, they should charge more to less elastic customers in order to maximize economic efficiency, because their demand is less likely to react to high tariffs. For price sensitive consumers, prices must be kept lower; otherwise, these consumers would reduce their consumption. Note that there is no consideration of equity or other economic factors, such as competitiveness, in the Ramsey

> 6 The figure is illustrative and may not depict the real situation. Particularly, the supply curve can vary depending on the supply structure of each country. It could be much steep but may be a vertical line because there are some elements that follow the market mechanisms in the international energy markets. In addition, although a certain pressure is surely created, the suggested movement may not necessarily take place because the domestic market is usually regulated.

> 7 In the Europe and Central Asia (ECA) Region, a normal affordability ratio for the power sector may be 10 to 15 percent of total household spending in case electricity is used for heating, cooking and hot water. If other fuels are used for these purposes, a threshold may be 10 percent (World Bank, 2006a).
_5.2_ Before examining the data at the branch level, we note from T'able 2 that within the full sample of 226 enterprises, the dispersion of returns to labor, capital and materials declines monotonically from 1980 to 1985 and from 1985 to 1989. The convergence is mo&L pronounced for capital and least noticeable for materials. The full sample results also show that, at the beginning and end of the 1980s, the dispersion of returns to material inputs is considerably less than for labor and capital.

_5.3_ At the branch !evel, we also observe a general pattern of convergence of factor returns, although there are notable exceptions to this pattern. Among the 10 branches, from 1980-19R9 returns to labor zonverge in five of the branches with the extent of convergence exceeding 25 percent in four of the branches while the degree of dispersion in the non-converging branches was trivial in all but two cases. Returns to ca; ital converge in all but three branches, w.th the degree of convergence exceeding 25 percev* in five branches while the degree of dispersion was of a similar magnitude in only one of the three non-converging branches. Finally, returns to material inputs converged in five of the 10 branches. 'Within each period, during 1980-85, convergence occurs for 20 of the 30 possible cases (capital, labor and materials within each branch); during 1985-89, the number falls to 17 of 30.

_5.4_ These changing patterns of factor returns by industrial branch reinforce earlier findings of convergence based on aggregate data, small enterprise samples or undifferentiated production echnologies. Comparing rates of convergence, we see that returns to capital showed the greatest convergence, while reductions in the dispersion of labor and materials were more modest. Because returns to materials were the least dispersed at the, beginning of the reform period, they offer less latitude for convergence than labor and capital whose returns were substantialy more dispersed.

_5.5_ Differences in patterns of dispersion within single years and in patterns of convergence over time suggest important differences in market structures and reform emphasis. The rzlatively low dispersion of returns to materials in 1980 and throughout the 1980s may reflect the fact that, in China durtng the 1970s, a relatively small number of material inputs were allocated under the pl a of the central governmentU3 In addition, during the 1980s, the practice of "gaojin-gaochu' - - planners setting low (high) product prices and plan shares to correspond to low (high) input prices and plan shares--has tended to stabilize the ratio of output to input prices. The dispersion of retnrns to labor relative to capital and materials in 1989 reinforce the impression that, among China's large and medium-size state enterprises, labor markets remain among the least reformed 11 See Naughton (1990).

**_Assasing Gas_** _in_ **_Effickni Podswon Amng China's Ind*Wtrlaprises,_**

by **Gary** H. **Jefeon an** Xu **Wenyi** 8
36 Table 3: Autonomy impacts in multi-product cost function

||A|ll|Ce|ntral|Prov|incial|Di|strict|
|---|---|---|---|---|---|---|---|---|
||coef|t|coef|t|coef|t|coef|t|
|Beds included|0.019|0.49|0.072|0.99|-0.077|-0.78|0.035|0.78|
|Beds excluded|0.019|0.49|0.068|0.92|-0.077|-0.78|0.034|0.78|

Note: *** p<.01, ** p<.05, * p<.1. Numbers shown are estimates of the coefficient on the autonomy variable in eqn (1) and the corresponding t-statistic. Regressions also include inpatient admissions, outpatient visits, their squares, cubes and interaction, beds (except where indicated otherwise), year dummies, a dummy capturing whether the hospital is a specialist hospital, an urban dummy, the number of private hospitals in the district, and the number of general hospitals in the district.
ECA, half of the institutions have been recapitalized, and in East ECA just two institutions out of 11 have not been recapitalized.

# **Figure 14: Capital adequacy ratios and recapitalizations (2007 through 2015)**

<!-- Start of picture text --> 14a. Median CAR<br>By type<br><!-- End of picture text --> <!-- Start of picture text --> 2007 16.16<br>Commercial 2011 15.84<br>2015 16.53<br>2007 20.20<br>Development 2011 23.30<br>2015 27.90<br>0 10 20 30<br>14b. Recapitalization<br>By type<br>Commercial Development<br>100%<br>80%<br>Percent Yes 60%<br>40%<br>20%<br>0%<br><!-- End of picture text --> <!-- Start of picture text --> By granular regions<br><!-- End of picture text --> <!-- Start of picture text --> 2007 11.20<br>Benchmark Europe 2011 15.60<br>2015 24.80<br>2007 25.65<br>WEST ECA 2011 21.65<br>2015 22.38<br>2007 16.03<br>EAST ECA 2011 29.00<br>2015 16.53<br>0 10 20 30<br>By granular regions<br>Benchmark Europe WEST ECA EAST ECA<br>100%<br>80%<br>Percent Yes 60%<br>40%<br>20%<br>0%<br><!-- End of picture text -->

## **By granular regions**

# **_5.5 Corporate governance_**

There is wide variation in the number of Supervisory Board (SB) members, with the smallest boards counting 2 members and the largest up to 15. The highest concentration of the number of SB members is in the range of 5 to 11 members, and there is no difference in the medians of Commercial versus Development SOFIs. In 30 percent of surveyed SOFIs, the SB has no independent member, but, in also 30 percent of the cases, all the members of the SB are independent. This difference does not appear to be driven by the SOFI type or geographical location.

34
<!-- Start of picture text --> 0.50<br>0.45<br>0.40 YL ONS<br>/ N<br>0.35 / oN<br>/ a oN<br>2 0.30 / y \ _<br>2 0.25 / / \\ \<br>6 / 4 ‘\ ‘\<br>0.20 , / \ \.<br>0.15 / N aN<br>0.10 vA a \S SA<br>y;ML 7 eo ~ x SS ‘<br>0.054 2% ~ ‘ —<br>0.00 Sa<br>17 18 19 20 21 22<br>Log of Assets 2010-12<br>——-—-—- SZN Unconnected — _SZN Connected<br>—-—--—- SHG Unconnected — SHG Connected<br><!-- End of picture text --> <!-- Start of picture text --> 0.50<br>0.45<br>0.40 ia<br>0.35 ff ‘y<br>7 Me<br>>, 0.30U. / ‘ 7a N ~S N<br>= / 7 N sy<br>2 0.25 / / \ \<br>a / / \<br>0.20 / / \ .<br>0.15 “) / \\ \<br>. v7 XN “N<br>0.10 Z yo NL \<br>y y \ \<br>0.055 ~7 4 a<br>—<br>0.00<br>17 18 19 20 21 22<br>Log of Assets 2010-12<br>—-—-—-—- SZN Unconnected - PSM —— _ SZN Connected - PSM<br>—-—-—-—- SHG Unconnected - PSM ——— SHG Connected - PSM<br><!-- End of picture text -->
**Figure 9. Reforms in Oil Exporters and Export Concentration**

# **A. Number of reforms implemented B. Export concentration, 2016 in oil-exporting EMDEs**

<!-- Start of picture text --> Number of reforms<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> <!-- Start of picture text --> Index, 1=most concentrated<br>0.6<br>0.4<br>0.2<br>0<br>EMDE oil EMDE oil Advanced<br>exporters importers economies<br><!-- End of picture text --> Sources: United Nations Conference on Trade and Development (UNCTAD), World Bank Doing Business. A. Number of reforms reported in Doing Business in the following areas: making it easier to start a business, making it easier to deal with construction permits, making it easier to get electricity, making it easier to register property, making it easier to get credit, making it easier to protect minority investors, making it easier to pay taxes, making it easier to trade across borders, making it easier to enforce contracts, and making it easier to resolve insolvency. Sample includes 35 oil-exporting EMDEs. B. Orange diamonds denote the median and blue bars represent the interquartile range of individual country groups. Sample includes 34 oil-exporting EMDEs (excludes South Sudan), 116 oil-importing EMDEs, and 36 advanced economies. Concentration index measures the degree of product concentration, where values closer to 1 indicate that a country’s exports are highly concentrated on a few products.

23
**Table 2 U.S. Patent Applications by Origin of First-Named Inventor, 1995-2004**<sup>41</sup>

|**Year**|**Total**|**United**<br>**States**|**Foreign **|**Indian**|**U.S as**<br>**% of**<br>**Total**|**Foreign**<br>**as % of**<br>**Total**|**Indian**<br>**as %**<br>**of**<br>**Total**|**Indian**<br>**as % of**<br>**Foreign **|
|---|---|---|---|---|---|---|---|---|
|1995|212,377|123,958|88,419|91|58.37|41.63|0.04|0.10|
|1996|195,187|106,892|88,295|115|54.76|45.24|0.06|0.13|
|1997|215,257|120,445|94,812|137|55.95|44.05|0.06|0.14|
|1998|243,062|135,483|107,579|180|55.74|44.26|0.07|0.17|
|1999|270,187|149,825|120,362|271|55.45|44.55|0.10|0.23|
|2000|295,926|164,795|131,131|438|55.69|44.31|0.15|0.33|
|2001|326,508|177,511|148,997|643|54.37|45.63|0.20|0.43|
|2002|334,445|184,245|150,200|919|55.09|44.91|0.27|0.61|
|2003|342,441|188,941|153,500|1,164|55.17|44.83|0.34|0.76|
|2004|356,943|189,536|167,407|1,303|53.10|46.90|0.37|0.78|

The years 1995-2004 are particularly significant, and not merely because they are the most recent ones for which data are available. India was hardly the only country to undertake a major rethinking of its IP laws upon joining the WTO. Nearly every country—including the United States—had to revise its IP laws upon WTO accession. Many of these countries, like India, took the opportunity to promote reassessments of their innovation systems. The numbers for U.S. patent applications suggest that India has been more successful than most; its share of a growing pie has expanded during a period of intense international competition.

Statistics on U.S. patents granted are a bit harder to decipher because different applications move through the application process at different speeds. USPTO data, for example, continue to show patents granted to citizens of Czechoslovakia and the Soviet Union—long after the demise of those two countries. As a result patents issued in any given calendar year necessarily lag changes in national cultures, IP laws, and innovation systems. Nevertheless, the data remain instructive.

Table 3 shows that India remains a small but growing recipient of U.S. patents. A ranking of U.S. patents issued by country from 1995–2004 places India 24<sup>th</sup> of 36 enumerated. Most of the top 23 economies are OECD members with fully developed patent systems. Among non-OECD members, only Taiwan (China), Israel, Singapore, Hong Kong (China), China, and Russia place ahead of India—and of these six, all but China have had significant advanced scientific, technological, and industrial bases in place for at least several decades.

> 41 _See_ ftp://ftp.uspto.gov/pub/taf/appl_yr.pdf.

24
worldwide reported that the most recent loan or line of credit required some form of collateral; this number was highest in low income countries at 85% and was above 90% in 31 of the 135 countries in the sample. A related issue is the type of collateral that firms have versus that which banks can accept. For instance, Fleisig, Safavian, and Steinbuks (2006) argue that nearly 90% of movable property that could serve as collateral for a loan in the United States would be unacceptable to a lender in Nigeria where until recently movable assets could not be used as collateral for bank loans. Alvarez de la Campa (2011) reported that in developing countries, nearly 78% of the capital stock of enterprises was in the form of movable assets such as machinery, equipment, or receivables; however, inadequate legal and regulatory environments in developing countries make banks reluctant to accept movable assets as collateral. This is also seen in the data from the Enterprise Surveys where firms reported land and buildings as being the most widely used form of collateral compared to accounts receivables, inventories or machinery, equipment, and other movable assets. Liberti and Mian (2010) investigate how financial development affects costs and types of collateral and show that not only do firms in more financially developed economies use more movable assets as collateral, but also that the cost of collateral declines with improved financial development.

Since the pioneering work by La Porta et al. (1998) showing that law is an important determinant of credit market development, a number of studies have focused on reforms in creditor rights and collateral laws and their impact on access to finance. Love et. al. (2014) compare 7 countries that introduced movable collateral registries since 2002 with 59 countries that did not introduce such 15
- b. Husband Quality.

- i. Husband’s highest grade completed, highest certificate attained. S25 Q2,4

- ii. Husband’s wage rate  S26 Q5

- iii. Currently employed S26 Q6..

- iv. Husband’s score on cognitive test

- v. Husband HIV status.

- vi. Husband marital fidelity.  Partners ever:  S32 Q2, Partners 12 mo.  S32 Q3.  Concurrence:  S32 Q15 answer for spouse (column 1)

- vii. Husband’s mental health (constructed in same manner as CR) and then standardized.

# **Super-index of husband quality:  i-vii.**

- c. Husband Gender Empowerment.

- i. Husband Index of GE:  S30 Q1-9

- ii. Husband Index of wife’s autonomy:  S30 Q13-21.

- iii. Husband Index of justification for abuse S30 Q26-28.

- iv. Husband Index of divorce prospects:  S31 Q10-11, 15,16

- v. Husband desired fertility.  S30 Q49

# **Super-index of husband gender empowerment:  i-v.**

3. <u>Unmarried Core Respondent Outcomes: to be analyzed within the sample of unmarried</u> core respondents.

- a. Empowerment unmarried:

- i. Index of autonomy:  S17 Q2a-d, Q4, Q6

- ii. Index of abuse:  S17 Q14,16,17a-e.

# **Super-index of unmarried empowerment:  i-ii.**

4. <u>Child-Level Outcomes: Can be analyzed two different ways. First, unconditionally</u> examining simple comparison between treatment and control. Secondly, conditionally including dummies for the age of child and the age of the mother so as to drop out effects coming from delayed fertility.

- a. Health Outcomes:

- i. Birthweight:  S21 Q20

- ii. Vaccinations:  S22 Q5, 7, 8, 10, 11 (index)

- **iii. Neonatal/Infant/Child mortality:  S18 Q16-21; S21 Q10 (use Round 3 data for children who had already passed away by Round 3)**

- iv. Bednets:  S22 Q 13

- b. Parental Practices:

- i. **Breastfeeding (6 months exclusive or to death is died younger than 6 months):  S21 Q22, Q23, Q25**

- ii. **Parenting:  S23 Q3-16, 21, 24, 27 (index)**

57
Table S.10: Impacts on children’s educational achievement - Islam studies: heterogeneity <u>(OLS)</u>

|Dependent variable:||||Islam stud|ies: GPA z|-scores||||
|---|---|---|---|---|---|---|---|---|---|
|_X_:|Age|Education|Male|Household<br>size|No. of<br>male<br>members|Head<br>age|Head<br>education|Head<br>male|Land<br>size|
||(1a)|(1b)|(1c)|(1d)|(1e)|(1f)|(1g)|(1h)|(1i)|
|**December in 2013**||||||||||
|All products|0.093|0.234|-0.048|0.304|0.181|0.358|0.038|0.288|0.062|
||(0.699)|(0.290)|(0.112)|(0.280)|(0.177)|(0.402)|(0.088)|(0.237)|(0.087)|
|One product|0.888|0.560*|-0.072|0.144|-0.020|-0.295|-0.004|0.265|0.076|
||(0.667)|(0.279)|(0.098)|(0.347)|(0.171)|(0.397)|(0.082)|(0.203)|(0.077)|
|All products _× X_|-0.003|-0.040|0.239|-0.050|-0.052|-0.007|0.013|-0.256|-0.001|
||(0.055)|(0.057)|(0.156)|(0.050)|(0.063)|(0.009)|(0.028)|(0.242)|(0.001)|
|One product _× X_|-0.070|-0.118*|0.209|-0.025|0.018|0.008|0.019|-0.268|-0.005|
||(0.054)|(0.062)|(0.140)|(0.071)|(0.072)|(0.009)|(0.039)|(0.200)|(0.003)|
|R-squared|0.259|0.261|0.260|0.258|0.258|0.259|0.258|0.258|0.259|
|No. of obs.|850|850|850|850|850|850|850|850|850|
||(2a)|(2b)|(2c)|(2d)|(2e)|(2f)|(2g)|(2h)|(2i)|
|**December in 2014**||||||||||
|All products|-0.928*|-0.225|0.059|-0.568|0.040|0.380|-0.085|-0.387|-0.085|
||(0.527)|(0.274)|(0.095)|(0.355)|(0.172)|(0.326)|(0.105)|(0.280)|(0.097)|
|One product|0.353|0.309|0.041|0.236|0.101|0.329|0.033|-0.340|0.013|
||(0.611)|(0.307)|(0.117)|(0.337)|(0.200)|(0.469)|(0.115)|(0.345)|(0.116)|
|All products _× X_|0.070|0.036|-0.278|0.103|-0.041|-0.011|0.020|0.361|0.003|
||(0.043)|(0.054)|(0.169)|(0.065)|(0.062)|(0.008)|(0.029)|(0.280)|(0.003)|
|One product _× X_|-0.025|-0.058|0.006|-0.041|-0.025|-0.007|0.005|0.416|0.003|
||(0.048)|(0.060)|(0.195)|(0.065)|(0.081)|(0.011)|(0.034)|(0.371)|(0.005)|
|R-squared|0.110|0.109|0.111|0.112|0.108|0.109|0.108|0.110|0.108|
|No. of obs.|846|846|846|846|846|846|846|846|846|
|Baseline control|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade. (3) The baseline controls include age (years); education (years); gender (dummy); household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number).

55