rafmacalaba/datause-encoder-data huggingface dataset:
23 local politicians and administrators (“cadres”) turn over land-lease contracts to developers in industry, retailing, and housing. Although such interventions have speeded up the reallocation of resources from agriculture to other sectors, this has occurred at the expense of farmers’ economic security. Social concerns have been relinquished for other purposes, such as a fast rate of structural change – and the enrichment of local cadres, who often share the capital gains of such reallocations of land-lease contracts. Such expropriation of agriculture land, without full compensation, is only one example of many of the misuses of powers by local cadres – other important examples being irregular payments of wages in connection with public works programs and an arbitrary imposition of levies of various types (Chow, 2006d).

Moreover, in a long-term perspective, we would expect a continuation of extensive corruption to have distinctively negative effects also on economic efficiency and growth in China. First, when the legal system improves, there is no need for having networks as a substitute for the rule of law. Second, when the transition to private ownership of firms and assets has been completed, asset stripping will no longer fulfill the function of helping create a class of private capitalists. Third, in a long-term perspective corruption is likely to contribute to social unrest, since large population groups may start questioning the legitimacy of the economic and political order. What would remain is a basically distorted allocation of resources and the risk of deficient legitimacy of the economic and political system.

There is no lack of official (in particular verbal) commitments to fight corruption in China. Indeed, China has made considerable progress in improving the legal system since the late 1970s. As a result, the legal system already seems to be more developed in China than in a number of countries outside East Asia on a similar level of economic development.<sup>31</sup> Nevertheless, China has a long way to go in this field.<sup>32</sup> In particular, as long as public-sector politicians and public-sector administrators have something to “sell”, such as various types of permits, corruption is difficult to wipe out. This constitutes an additional argument for further deregulation of the Chinese economy – on top of the

> 31 See, for instance, Peerenboom (2002) and Chen (2004), and the World Bank Evaluation Index of Rule of Law (World Bank, 2005). 32 For instance, according to Transparency International Corruption Perceptions Index (2005), China ranks as country 77 (among 159 countries), where the least corrupt country ranks as number one.
- Farmers would be covered for five consecutive years, making the chances of receiving a claim during this period more likely than if they focused on the annual probability of a disaster occurring.

- The multi-year loan would spread the upfront cost of the investment over a number of years. Local authorities, banks or institutions such as the World Bank could provide the loan.

- The amount of protection against a disaster (for example, drought) required by the community or government would be much lower if they invested in a risk reducing measure such as an irrigation system.

- The annual premium reduction of the MYI policy when one invested in a risk reducing measure (for example, an irrigation system) should be greater than the cost of the loan if the measure is a cost-effective one.

Multi-year insurance policies have been examined by very few pilot studies, with the Peruvian initiative discussed in the previous section being a notable exception. Since this initiative is so recent there has been little time to examine the effects of tying multiyear loans and insurance policies. With the program’s planned expansion into multiyear loans for households, small businesses, and the public sector we hope to see an increase in demand, as well as, increases in investments in long term mitigation strategies including irrigation systems (Cavanaugh, Collier and Skees, 2010).

# **_The Role of Risk Transfer Instruments for Covering Catastrophic Losses_**<sup>**_8_**</sup>

A multi-year insurance and loan program should be a win-win for all interested parties. The farmers would be safer and could generate higher revenue, which in turn will help economic growth and further investment in innovative technologies. There would be less need for the government and international charities to provide financial assistance and aid to victims of future disasters since the exposure will be reduced by physical investment in risk reduction measures and by financial protection through insurance.

> 8 This section is based on Michel-Kerjan et al. (2011).

20
Source: World Bank staff calculation with World Input Output Database data.

# **2. Literature and Contribution**

Over the past decade, a significant body of literature has emerged to examine the effects of trade integration on labor markets. Notably, Autor, Dorn, and Hanson (2013) made a seminal contribution by employing a Bartik (1991) approach to estimate the causal impact of the "China Shock" on employment and wages at the community zone level in the United States. Their findings reveal that employment was the primary margin of adjustment to the China Shock, accounting for approximately 25 percent of the decline in US employment. These results prompted further exploration into the effects of trade shocks on labor markets, leading to subsequent studies by Pierre and Schott (2016), Acemoglu et al. (2016), Autor et al. (2014), Dix-Carneiro and Kovak (2017), Kovak (2013), Topalova (2010), and others. This literature has expanded to assess the impacts of various trade shocks on local labor market outcomes in both developed and developing economies. The key finding of this literature is that trade shocks within a country can have substantial and persistent impacts on local labor markets, giving rise to distributional concerns (Engel et al., 2020).

It is crucial to note that trade affects not only tradable sectors, but also non-tradable sectors within the same local labor markets. While increased import competition or market access directly impacts specific tradable sectors, there are indirect effects on non-tradable sectors such as retail, health care, or hospitality in the same region. For instance, Autor et al. (2013) find that wages in non-tradable sectors in areas most exposed to Chinese imports decreased, while preliminary evidence by Wang et al. (2018) suggests that these sectors experienced employment expansion. Dauth et al. (2012) also report wage gains in the service sectors of export-oriented regions in Germany, and Menezes-Filho and Muendler (2011) show that the service sector and the less trade-exposed informal sector absorbed initially displaced workers after Brazil's trade liberalization.

A recent and growing body of literature has emerged to address these indirect effects of import shocks on local labor markets by incorporating input-output linkages in production. For example, Wang, Wei, Yu, and Zhu (2018) estimate the direct and indirect impacts of the "China Shock" on local employment and wages in the United States, considering upstream and downstream channels. Their findings indicate a positive overall impact on employment, in contrast to the direct effect captured by Autor, Dorn, and Hanson (2013). Goutam (2018) also
# Mortality at Later Ages

An additional argument advanced for tetanus immunization is the protection it provides against tetanus beyond the neonatal period. The available empirical evidence on this issue, however, suggests that the magnitude of this additional mortality effect may be quite modest. For example, a study of causes of childhood death in the Matlab comparison area during 1986-87-- where tetanus immunization coverage levels were extremely low-indicated that tetanus accounted for only 3 percent of postneonatal and <1 percent of 1-4 year deaths (Koenig, et al. 1991a). Similarly, a population-based study of 1037 deaths to adult women in the Matlab study area during 1976-85 found that only 6 were attributable to tetanus, representing less than 1 percent of all such deaths (Fauveau, et al. 1987). These findings indicate that from the standpoint of mortality impact, the benefits from tetanus immunization will derive largely from a reduction in neonatal tetanus mortality.

# ESTIMATES OF POTENTIAL MORTALITY REDUCTIONS FROM MEASLES **AND** TETANUS IMMUNIZATION

The recent study by Koenig, et al. (1991a) allows the estimation of the mortality impact of measles and tetanus immunization for rural Bangladeshi children. In this study, the survival status of a cohort of 8161 births, representing all births within the Matlab comparison area during 1982-83, was followed through the end of 1987 to estimate <5 year mortality levels in a largely unimmunized population. The authors then applied the previously reported findings from Matlab on cause of death and vaccination impact, in addition to data on the age distribution of mortality, applying the most liberal estimate of mortality reduction for each case, to estimate reductions in mortality based upon the elimination of deaths from these **causes.** The results of this exercise for measles and tetanus mortality are shown in Figure 9.

For this population of children, the elimination **of** tetanus and measles through immunization could **be** expected to **reduce** infant mortality levels from the previous level of 116 per 1000 live births to 91 per 1000 live births. It is apparent that most of this reduction would occur through a reduction in neonatal mortality levels (from 69 to 50 per 1000 live births) **as a result** of tetanus immunization; the reduction in post-neonatal mortality levels will be quite modest (from 47 to **41 per 1000 live births).** A marked decline in 1-4 year mortality is **also evident as a** result of large effect of measles vaccination, with mortality between **these** ages declining from **90 per 1000** to **50 per 1000 live** 30
-9-

# Table 2: MACROECONOMIC AND HUMAN RESOURCE INDICATORS

# FOR COTE D'IVOIRE, 1984

|Population|9.9 million|
|---|---|
|Average Annual Growth, 1973-1984|4.5%|
|Average Annual Projected Growth, 1980-2000|3.7%|
|GNP Per Capita (1984 dollars)|$610|
|ktverage Annual Growth, 1965-1984|0.2%|
|Life Expectancy at Birth|52 years|
|Infant Mortality Rate (per thousand)|106|
|Percent of Labor Forcel/ in<br>-<br>Agriculture<br>-<br>Industry<br>-<br>Services|65%<br>8%<br>27%|
|Percent of Population in Urban Areas|46%|

I/ 1980 Source: World Bank (1986)
23 applications with a total value of DM 9.3 billion for projects in 91 countries were approved (see Treuarbeit (1989)). Only few applications<sup>are finally</sup> rejected. The amount of new approved projects, which peaked in 1988 at<sup>DM 0.6</sup> billion, is strongly influenced by a few big protjects. According to the Ministry of Economic Affairs roughly 20 percent of German FDI in developing<sup>countries is</sup> covered by a guarantee.13 The regional pattern of guarantees shows a much heavier<sup>weight of African</sup> and Asian countries than does the general regional pattern<sup>of German FDI. This</sup> is related to three factors. First most bilateral investment<sup>treaties are</sup> established with countries of these areas. Second guarantees mostly cover equity capital; some countries, however, rather attract FDI related loans<sup>due to their</sup> repatriation practices, for example Brazil. Third the<sup>regional pattern is</sup> affected by the age structure of German FDI; in Asia, where<sup>projects on average</sup> are much younger than for example in Latin America, the average size<sup>of a</sup> guaranteed project is markedly higher than of those in other regions.

13see von Wurzen (1989); it is not quite clear how this number was calculated. Most likely it relates guarantee approvals to accumulated net FDI outflows.
# **<u>Introduction</u>**

In this paper we revisit the potential for carrying out welfare analysis in the absence of the typically used measure of household economic status, per capita household expenditures. In many situations this preferred proxy for income is not available. While various solutions to overcoming this problem have been proposed, Filmer and Pritchett (1999, 2001) popularized an approach using an aggregate index based on consumer durable assets owned by household members, along with a set of housing characteristics, to rank households.

Filmer and Pritchett (1999, 2001) developed their index in the context of analyzing the associations between household economic status and schooling outcomes when using data sets without information on household expenditures. The approach has since been used for a variety of purposes. For example, researchers have used asset indices to explain inequalities in health outcomes and behaviors (Gwatkin et al. 2000; Bollen et al. 2002; Schellenberg et al. 2003), in particular those related to fever and malaria (Filmer 2005; Njau et al.. 2006), child nutrition (Sahn and Stifel 2003; Tarozzi and Mahajan 2005), child mortality (Fay et al 2005; Sastry 2004), and early child development (Ghuman et al.. 2005). The approach has also been used to analyze socio-economic inequalities in schooling in sub-populations, such as orphans (Ainsworth and Filmer 2006; Bicego, Rutstein and Johnston 2003; Case, Paxon, and Ableidinger 2004; Evans and Miguel 2004) and children with disabilities (Filmer 2008). Others have used asset indices to analyze poverty change (Sahn and Stifel 2000; Stifel and Christiaensen 2007), inequality (McKenzie 2005), or to control for economic status in program evaluation when expenditures data are not available (Rao and Ibanez 2005). The asset index approach has frequently been used to describe inequalities in health and education outcomes in international databases such as those in the World Bank’s _World Development Reports_ (World Bank 2003, 2005a, 2006).

A similar approach of using an index of observable household characteristics has been used in a different context for the purpose of targeting public programs. For example, Ecuador uses an index of assets and other household characteristics (based on a census of households) to target cash transfers to poor households (Schady and Araujo 2006). Analogous proxy-means targeting approaches have been applied in other countries such as Armenia, Brazil, Colombia and Indonesia (see discussion in Coady, Grosh and Hoddinott 2004).

2
<!-- Start of picture text --> Qi 144,582<br>145,815<br>Q2 204,743<br>204,881<br>Q3 255,298<br>255,855<br>a4 331,001<br>330,485<br>Qs 89,330<br>518,771<br>0 200,000 400,000 600,000<br>Consumption per capita (monthly, riels)<br>(= Non-GarmentHHs [RD Garment HHs<br><!-- End of picture text --> <!-- Start of picture text --> 60 55.04<br>55.03<br>> 47.68<br>zx 43.16 4524 ageg<br>a] 40.44<br>2= 40 a5.a9 6 3F0e<br>°<br>= 2E.71<br>a<br>Aud<br>S<br>2 20<br>a<br>aQ2 Q3 a4 a5<br>GE) Non-GarmentHHs (Garment HHs<br><!-- End of picture text --> <!-- Start of picture text --> 3 om<br>5 :<br>g<br>3<br>a<br>5 O22<br>=£2<br>=<br>=<br>3 O44<br>o O12<br>3 od<br>g.1 0.08<br>=<br>3<br>2 0.04 0.05<br>o 0.03<br>001<br>Qi a2 Q3 Q4 a5<br>GE) sNon-GarmentHHs [Garment HHs<br><!-- End of picture text -->
Table 4. OLS regressions (dep = log of fertility rate) Developing countries

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Constant|1.270|1.267|1.587|1.525|
||(5.40)***|(6.20)***|(5.17)***|(6.29)***|
|Log of fertility at dest|0.337<br>(3.28)***|0.338<br>(3.65)***|0.267<br>(2.26)**|0.261<br>(2.34)**|
|p0:Log of fertility at dest|-0.181<br>(0.60)||-0.368<br>(1.21)||
|log of p0|-0.019|-0.028|-0.009|-0.034|
||(0.54)|(1.17)|(0.28)|(1.49)|
|Selection ratio (sec+tert)|-0.001<br>(0.02)||-0.013<br>(0.41)||
|Log of remittances|||0.008|0.016|
||||(0.52)|(1.14)|
|Urbanization|-0.004|-0.003|-0.005|-0.004|
||(1.97)*|(1.84)*|(2.87)***|(2.38)**|
|GDP per capita|-0.088<br>(2.67)***|-0.094<br>(3.25)***|-0.112<br>(2.85)***|-0.116<br>(3.81)***|
|Adult’s education|-0.375|-0.422|-0.350|-0.383|
||(2.30)**|(2.67)***|(1.91)*|(2.15)**|
|East Asia & Paci...c|0.406<br>(3.50)***|0.387<br>(3.40)***|0.407<br>(3.98)***|0.363<br>(3.66)***|
|Sub-Saharan Africa|0.428|0.387|0.519|0.439|
||(4.55)***|(4.34)***|(4.52)***|(4.40)***|
|Latin Am. & Carib|0.335<br>(4.81)***|0.298<br>(4.38)***|0.414<br>(6.15)***|0.344<br>(5.23)***|
|Mena|0.115||0.192||
||(1.11)||(1.40)||
|Muslims (% of pop)|0.004<br>(4.51)***|0.005<br>(5.44)***|0.004<br>(3.11)***|0.004<br>(4.93)***|
|Catholic (% of pop)|0.002<br>(2.62)***|0.002<br>(2.86)***|0.002<br>(3.03)***|0.003<br>(3.30)***|
|Observations|143|143|119|119|
|R-squared|0.77|0.76|0.84|0.83|

Robust t statistics in parentheses * signi...cant at 10%; ** signi...cant at 5%; *** signi...cant at 1% 25
<!-- Start of picture text --> Canada<br>105 Output 105 Labor<br>100 100<br>95 95<br>90 90<br>1982 1983 1984 1985 1982 1983 1984 1985<br>30 Investment Net Exports<br>10<br>25<br>5<br>0<br>20<br>−5<br>15<br>−10<br>1982 1983 1984 1985 1982 1983 1984 1985<br>Model with Efficiency Wedge Model with Labor Wedge Data<br>Canada<br>105 Output 105 Labor<br>100 100<br>95 95<br>90 90<br>1982 1983 1984 1985 1982 1983 1984 1985<br>30 Investment Net Exports<br>10<br>25<br>5<br>0<br>20<br>−5<br>15<br>−10<br>1982 1983 1984 1985 1982 1983 1984 1985<br>Model with Investment Wedge Model with Risk−Premium Wedge Data<br><!-- End of picture text --> Figure 4: Data and counterfactual models with one wedge.

31
Survey and Uganda’s 2011-12 National Panel Survey. It is not possible to determine whether a woman is pregnant at the time of measurement in the Tanzania survey.

**Table 2: Summary statistics for nutritional indicators using DHS**

||Underweight women|Stunted children|Wasted children|
|---|---|---|---|
|Benin|0.064|0.407|0.144|
|Burkina Faso|0.154|0.298|0.139|
|Burundi|0.160|0.516|0.051|
|Cameroon|0.068|0.279|0.050|
|Congo, Rep.|0.144|0.187|0.051|
|Côte d'Ivoire<br>Congo, Dem.|0.078|0.238|0.071|
|Rep.|0.144|0.366|0.072|
|Ethiopia|0.266|0.388|0.086|
|Gabon|0.074|0.131|0.032|
|Gambia|0.167|0.200|0.110|
|Ghana|0.061|0.134|0.053|
|Guinea|0.122|0.269|0.099|
|Kenya|0.122|0.293|0.058|
|Lesotho|0.058|0.302|0.030|
|Liberia|0.073|0.257|0.058|
|Malawi|0.087|0.413|0.038|
|Mali|0.114|0.337|0.119|
|Mozambique|0.086|0.371|0.049|
|Namibia|0.140|0.176|0.081|
|Niger|0.154|0.355|0.153|
|Nigeria|0.111|0.326|0.163|
|Rwanda|0.070|0.365|0.025|
|Senegal|0.222|0.159|0.090|
|Sierra Leone|0.090|0.327|0.082|
|Swaziland|0.033|0.226|0.020|
|Tanzania|0.113|0.355|0.040|
|Togo|0.070|0.214|0.062|
|Uganda|0.117|0.281|0.039|
|Zambia|0.102|0.343|0.055|
|Zimbabwe|0.070|0.256|0.028|
|Mean|0.114|0.321|0.086|

Note: Observations with missing values and pregnant women are dropped. Means are population weighted. Women between 15 and 49 years of age and children between 0 and 5 years of age are included. A woman is underweight if she has a BMI less than or equal to 18.5. A child is stunted if she is two standard deviations below median heightfor-age and wasted if she is two standard deviations below median weight-for-height. GDP and literacy are taken from the World Bank’s <u>World Development Indicators. Literacy rate for 2011 or closest available year to 2011 in</u> 2007-15; more recent year for ties.

39
Table 8: Difference in Differences Test (OLS)

||(1)<br>Risk Aversion|(2)<br>Risk Aversion|
|---|---|---|
|Migration Group (1=never migrated)|-0.105***|-0.0873**|
|Confict (1=some confict)|-0.049|-0.0501|
|did|-0.0124|-0.00969|
|**Social and Economic Variables**|||
|Household Size||0.000533|
|Share of Dependent||0.0426|
|HH Gender = Male||-0.0172|
|HH Age||-0.000869|
|HH Years of Education||-0.00298|
|HH Employed||-0.023|
|HH Married||-0.055|
|HH Gender * HH Married||0.0738|
|**Quality of Dwelling**|||
|Number of Rooms||-0.0146***|
|Quality of Walls|||
|(baseline=Low)|||
|Medium||0.0649*|
|High||0.0128|
|**Assets**|||
|TV||0.0187|
|Fridge||0.0147|
|Stove||-0.0222|
|Bicycle||-0.0406*|
|Car||0.00511|
|Iron||0.0198|
|Constant|0.820***|0.905***|
|Observations|4,577|4,577|
|R-squared|0.006|0.016|

Robust standard errors (not reported) *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Source: Authors’ estimations based on GHS and ACLED Data 38
**Table** **<u>30: IV Estimation (5)</u>**

||Car for pop<br>IVProbit|ulated area|Car for mun<br>capital<br>IVProbit|icipal|Car for hos<br>IVProbit|pital|Car for sch<br>IVProbit|ool|
|---|---|---|---|---|---|---|---|---|
|D|1.365|(0.796) *|0.170|(0.530)|-0.427|(0.518)|1.707|(0.928) *|
|lnHH size|-0.016|(0.219)|0.124|(0.164)|0.094|(0.164)|0.145|(0.245)|
|Adult share|0.599|(0.457)|0.929|(0.344) ***|0.570|(0.342) *|1.543|(0.561) ***|
|HH head sex: Male|||-0.136|(0.206)|-0.071|(0.198)|0.120|(0.299)|
|HH head education: Elementary|0.414|(0.206) **|0.138|(0.148)|0.068|(0.143)|0.132|(0.229)|
|HH head education: Secondary|0.473|(0.299)|0.440|(0.215) **|0.426|(0.213) **|0.196|(0.352)|
|HH head education: University|0.893|(0.474) *|0.409|(0.419)|0.349|(0.423)|1.034|(0.491) **|
|Appliance ownership: Refrigerator|0.275|(0.341)|0.124|(0.237)|-0.018|(0.218)|0.482|(0.393)|
|Appliance ownership: Color TV|-0.240|(0.282)|0.025|(0.217)|-0.111|(0.211)|-0.003|(0.328)|
|Appliance ownership: Radio|0.344|(0.185) *|0.153|(0.135)|0.214|(0.134)|0.058|(0.207)|
|Appliance ownership: Gas stove|0.719|(0.439) *|0.127|(0.256)|-0.058|(0.231)|||
|Appliance ownership: Washing machine|-0.025|(0.191)|0.307|(0.141) **|0.252|(0.143) *|0.073|(0.225)|
|Gas use for cooking|0.164|(0.183)|0.172|(0.141)|0.049|(0.141)|0.205|(0.227)|
|Power use for lighting|0.061|(0.327)|0.303|(0.261)|0.205|(0.243)|0.142|(0.386)|
|Tap water for cooking|0.521|(0.201) ***|0.031|(0.143)|0.104|(0.141)|0.338|(0.233)|
|Constant|-4.742|(1.061)***|-3.046|(0.818)***|-1.910|(0.777)**|-5.486|(1.334)***|
|Obs.|921||911||911||911||
|Wald-stat|22.77||32.23||19.91||18.40||
|Amemiya-Lee-Newey overidentifying restriction|||||||||
|test|3.30|*|0.97||0.18||1.06||
|Hausmanexogeneity test|11.56|***|19.33|***|12.90|***|8.55|***|

Note: Standard errors are shown in parentheses. *, **, *** indicate the statistical significance at 10%, 5% and 1%, respectively.

49
area level and label it _ai50k2000_ . Roberts et al. (2017) provide an updated AI circa 2010 using the newer travel time model and the population model from Landscan 2012 ( _ai50k2010)_ . We construct the variable of the total population in the AI for each period and, subsequently, the share of population in an AI out of the total population of the administrative area, respectively _ai50k2000_ _~~l~~ pop04_ and _ai50k2010_ _~~l~~ pop12_ .

# **Appendix 3: ELL (log) consumption model**

||Coefcient|Std. Err.|
|---|---|---|
|Intercept|10.5881|(0.1237)|
|EA<br>M<br>~~H~~HD<br>~~H~~AS<br>BICYCLE|-0.1779|(0.0595)|
|EA<br>M<br>~~H~~HD<br>~~H~~AS<br>CAR|1.2011|(0.2212)|
|EA<br>M<br>~~H~~HD<br>~~H~~AS<br>PHONE|0.2678|(0.0836)|
|EA<br>M<br>~~H~~HD<br>~~H~~AS<br>TV|-0.4604|(0.1471)|
|EA<br>M<br>~~H~~OH<br>~~R~~ESID<br>5YR|0.1522|(0.0797)|
|EA<br>M<br>~~H~~OH<br>~~T~~R<br>~~S~~ENA|-0.3658|(0.0513)|
|EA<br>M<br>~~H~~OH<br>~~T~~R<br>~~T~~UMBUKA|0.1242|(0.0555)|
|EA<br>R<br>~~C~~HLDBRN<br>LSTYR<br>~~F~~EM<br>~~3~~0<br>~~4~~5|0.2403|(0.0688)|
|HHD<br>~~A~~GE<br>00<br>05<br>~~A~~LL<br>~~0~~|0.1749|(0.0199)|
|HHD<br>~~A~~GE<br>00<br>05<br>~~A~~LL<br>~~1~~|0.0722|(0.0155)|
|HHD<br>~~A~~GE<br>06<br>15<br>~~A~~LL<br>~~0~~0|0.0715|(0.0207)|
|HHD<br>~~A~~GE<br>06<br>15<br>~~A~~LL<br>~~0~~1|0.0649|(0.0167)|
|HHD<br>~~A~~GE<br>65<br>ALL<br>~~0~~|0.0352|(0.0214)|
|HHD<br>~~C~~OOK<br>WOOD<br>~~O~~THER<br>1|-0.1877|(0.0276)|
|HHD<br>~~E~~DU<br>SECONDARY<br>~~0~~|-0.0814|(0.0261)|
|HHD<br>~~F~~LOOR<br>~~S~~OFT<br>~~1~~|-0.1754|(0.0203)|
|HHD<br>~~H~~AS<br>~~B~~ICYCLE<br>1|0.098|(0.0136)|
|HHD<br>~~H~~AS<br>~~C~~OOKER<br>~~1~~|0.1738|(0.0467)|
|HHD<br>~~H~~AS<br>~~F~~RIDGE<br>1|0.2897|(0.0444)|
|HHD<br>~~H~~AS<br>~~I~~TN<br>1|0.1469|(0.0126)|
|HHD<br>~~H~~AS<br>~~R~~ADIO<br>~~1~~|0.1934|(0.0134)|
|HHD<br>~~H~~AS<br>~~T~~V<br>~~1~~|0.2631|(0.027)|
|HHD<br>~~O~~CCUPIED<br>~~0~~6<br>~~1~~5<br>~~0~~|0.034|(0.0154)|
|HHD<br>~~R~~OOF<br>~~G~~RASS<br>1|-0.1072|(0.0181)|
|HHD<br>~~R~~OOMS<br>01|-0.2334|(0.0236)|
|HHD<br>~~R~~OOMS<br>02|-0.1608|(0.019)|

50