rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 2. Summary Statistics**

||**Mean**|**S.D.**|**# Obs**|
|---|---|---|---|
|**A. Proportion of migrant workers in**<br>|**total rural labor**<br>|**force**<br>||
|Out-province|0.037|(0.07)|403|
|Out-village, within-county|0.052|(0.07)|403|
|Immigrants within village|0.129|(0.41)|403|
|**B. Village character**<br>|**istics**<br>|||
|Average income per capita (Yuan)|2119.494|(1364.52)|403|
|No. of households|488.913|(362.00)|403|
|Land area (Km<sup>2</sup>)|7.234|(11.19)|403|
|Proportion of villages located in mountain areas|0.303|(0.46)|403|
|Sexratio (Male/Female)|1.029|(0.11)|403|
|Government’ budget surplus (100 Yuan)|1737.623|(14019.87)|403|
|No. of newspapers per household|0.234|(0.27)|403|
|Proportion of household having TV|0.809|(0.24)|403|
|Proportion of household using electricity|0.979|(0.11)|403|
|**C. Distance between villa**|**ges (km)**|||
|Distance between every two villages|262.730|(663.98)|1653|
|Distance between one treatment village and one control<br>village|240.868|(122.73)|777|

31
as clusters of land holdings without separately identifying individual holdings. There might be some public land in Gamtals and Walled City but it has not been covered in this study, due to lack of readily available data and the fact that such public land is likely to be small and already committed to local community use having very little market potential if any. Walled City and Gamtals that together account for only 3.94 % of the study area are therefore treated as entirely private.

The basic property rights records and records of rights over land are maintained by the Revenue Department of the State Government. These are amended once the Town Planning Scheme is sanctioned. Since TPS provide map-based records of ownership, in the present study wherever TPS are available they have been used as the source of data for public land. In non-TPS areas the extracts from the record of rights from the Revenue Department have been used. (These are called 7/12 extracts.) Thus public land and its use in the study area were identified by using following data sources and survey methods:

- (a) Public land indicated on maps and data of TPS,

- (b) In case of non- TPS areas, cadastral records (called 7/12 extracts of record of rights) were used to identify public land.

- (c) The use of such public land, including the public agency that owns the land, was identified through field surveys.

These details were then incorporated in the GIS database. Based on the shape files in GIS, a geographical distribution of public land owned by Central Government and State Government is derived and shown Figure 4.

10
27 affordability of LPG in Guatemala, Albania, Brazil, and Peru was comparable, but the selection rate ranged from 51 percent to 88 percent. These differences may be for a variety of reasons, including the share of unpurchased food in total household expenditure, the importance of excluded expenditures (large durable goods), differences in household income (in contrast to measured expenditures), supply conditions of LPG and other fuels, upfront costs of LPG use, and cultural acceptance of different fuels. Nevertheless, the differences in the selection rate among countries with comparable affordability of LPG suggest that there is potential to increase its use further and help reduce reliance on solid fuels with their attendant problems.

Given that income and LPG prices are crucial determinants of LPG selection and consumption and that international prices of LPG have seen far greater volatility than household income in recent years, it is informative to examine recent price trends. Figure 4 provides estimates of minimum income levels needed to use LPG regularly for cooking, expressed in 2011 U.S. dollars. Although based on historical LPG prices, the calculations use a number of simplifying assumptions, resulting in large uncertainties. As such, the estimates should be seen as being indicative. One assumption is that LPG’s share of household spending is 4 percent, which is near the upper end of the range observed in Table 4. The calculations show that total household expenditure of between US$150 and US$200 would have enabled regular use of LPG up to 2005, but with recent price rises on the international market, households would need to be earning nearly double that today, more than US$300 a month, to purchase 10 kg of LPG per month while limiting the expenditure to 4 percent of total household spending. Because these calculations do not take expenditures on large durable goods or the start-up costs of LPG into account, household income would likely need to be even higher.

Figure 4: World LPG price and daily per capita and monthly household expenditure needed for regular use of LPG <!-- Start of picture text --> 3.5 350<br>3.0 300<br>2.5 250<br>2.0 200<br>1.5 150<br>1.0 100<br>0.5 50<br>0.0 0<br>Jan-00 Jan-01 Jan-02 Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11<br>2011 U.S. dollars 2011 U.S. dollars<br><!-- End of picture text --> LPG price in $/kg (left axis) Minimum per capita daily expenditure required (left axis) Minimum monthly household expenditure required (right axis) _Sources_ : Reuters for Saudi Aramco contract prices for propane and butane and authors’ calculations. _Notes:_ LPG is assumed to be an equal mixture of propane and butane. Calculations assume transportation, bottling, distribution, and retailing costs and taxes totaling $350 a tonne of propane in 2011 U.S. dollars; monthly
Center: The SIGI index of discrimination in the family (Discriminatory Family) is based on laws on child marriage, household responsibilities, inheritance, and divorce; the SIGI Physical Integrity Index (Restricted Physical Integrity) includes laws on violence against women and reproductive autonomy, attitudes towards and prevalence of female genital mutilation (FGM) and domestic violence, missing women, and access to family planning.<sup>20</sup> In addition, we obtain indicators from the World Development Indicators: Literacy Rate is the adult female literacy rate; Gender Equality is a CPIA index on gender quality (1=low, to 6=high). Second, we measure a country’s individualistic culture by Individualism, which is based on World Value Survey, and it captures the extent to which the people in a society are mentally and habitually empowered to make their own choices and to pursue them in their actions. In more individualistic societies, we presume that competition is more encouraged and fiercer. Finally, we use the rule of law index from the World Bank’s Governance Indicators (Kaufman, Kraay and Mastruzzi 2004). To aid interpretation and to normalize in light of the different scales of these indicators, we transform all these indicators into dummy variables, which equal one when the value is above the median values among the countries, and zero else.

# **Incidence of female-headed firms and their basic characteristics**

Female-headed firms ideally would have women being the primary owner and running the firm, that is, acting as both owners and chief executives. Female-owned firms may not be a good indicator for entrepreneurs because family ownership is prevalent in both developed and developing countries (Anderson and Reeb, 2003), and when women inherit family firms without effective control, the firm is not in reality a female-headed firm. Furthermore, the previous literature using the firm sample of several African countries in the WBES data has documented that using the definition of women managers tends to better capture firms truly led by women than that of women’s participation in firm ownership (Aterido and Hallward-Driemeier, 2011). We thus conservatively define female-headed firms. We rely on two variables in the WBES data set to capture aspects of female leadership: women’s participation in ownership (“whether women have some ownership of the firm”), and

> 20 More information on the SIGI can be found here: https://www.genderindex.org/.

16
**Figure 5: Coffee Sales through Specialty Channels (60‐kg bags)**

<!-- Start of picture text --> 18,000<br>15,000<br>12,000<br>9,000<br>6,000<br>3,000<br>0<br>1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text -->

**Source: Uganda Coffee Development Authority**

**_— 24 —_**
# **Table A.10. Income Inequality, Reclassification: Top Performers Are Firms in the Top 50 Percent in Capital Productivity among the Top 50 Percent of the Capital Distribution**

||(1)<br><br>Top-pe|(2)<br> <br>rformers|(3)<br><br>Constrain|(4)<br> <br>ed gazelles|(5)<br><br>Surv|(6)<br> <br>ivalists|
|---|---|---|---|---|---|---|
||Coefficients|Odds ratio|Coefficients|Odds ratio|Coefficients|Odds ratio|
|Gross operating profit|0.000691**<br>(0.000326)|1.001|0.00122***<br>(0.000348)|1.001|0.00179***<br>(0.000557)|1.002|
|Age owner|0.0132<br>(0.0469)|1.013|-0.0419<br>(0.0328)|0.959|-0.0228<br>(0.0169)|0.977|
|Age squared owner|-0.000244<br>(0.000562)|1.000|0.000468<br>(0.000406)|1.000|0.000307<br>(0.000192)|1.000|
|Male(=1)|-0.244<br>(0.226)|0.784|-0.0225<br>(0.122)|0.978|-0.509***<br>(0.185)|0.601|
|No diploma|Ref.||Ref.||Ref.||
|Primary completed|1.087**|2.966|-0.0922|0.912|-0.0784|0.925|
||(0.483)||(0.318)||(0.178)||
|Some secondary|1.212***|3.362|0.348|1.416|0.419**|1.520|
||(0.415)||(0.275)||(0.176)||
|Post secondary|1.751***<br>(0.478)|5.760|1.123***<br>(0.306)|3.075|0.819***<br>(0.250)|2.268|
|Other post primary|1.024|2.784|0.732**|2.079|0.818|2.266|
||(0.626)||(0.373)||(0.938)||
|Can read|0.0655<br>|1.068|1.016***<br>|2.763|0.341**<br>|1.407|
||(0.465)||(0.225)||(0.140)||
|No opportunity in small firms|0.115<br>(0.301)|1.122|-0.0837<br>(0.142)|0.920|-0.0515<br>(0.159)|0.950|
|Firm size|0.205|1.228|-0.191**|0.826|-0.0611|0.941|
||(0.141)||(0.0864)||(0.118)||
|Trade register|0.623|1.864|1.307***|3.694|0.705|2.024|
||(0.806)||(0.347)||(0.907)||
|Financial knowledge|0.172|1.187|0.372***|1.450|0.370**|1.448|
||(0.236)||(0.133)||(0.170)||
|Business association|0.0330|1.034|-0.249|0.779|0.113|1.120|
||(0.295)||(0.181)||(0.231)||
|Lack of customers|0.190|1.210|0.155|1.168|-0.0528|0.949|
||(0.202)||(0.102)||(0.107)||
|Lack of credit access|-0.395**|0.674|-0.199*|0.820|-0.263**|0.769|
||(0.200)||(0.109)||(0.116)||
|Management problems|0.361|1.435|0.163|1.177|0.0854|1.089|
||(0.279)||(0.133)||(0.136)||
|Sector fixed effects|yes||yes||yes||
|Region fixed effects|yes||yes||yes||
|Pseudo-R2|0.1277||0.1161||0.0903||
|Observations|436|436|1,380|1,380|1,324|1,324|
|control variables which are includ<br>Standard errors in parentheses ***<br>Note: Discipline problems, no opp|ed in the regress<br>p<0.01, ** p<0<br>ortunity in large|ions but not pr<br>.05, * p<0.1<br>firms, firms a|esented in this tab<br>ge and lack of qu|le.<br>alified workfor|ce are non signifi|cant additional|

38
As illustrated in figure 5, and consistent with the finding of a positive treatment effect over the course of the program, the treatment effect is increasing in the exposure to treatment. For example, the treatment effect for children that have lived three years in a treatment area is 0.12 _z_ -score in height-for-age, corresponding to a 10 percent increase compared to the similar aged cohort in the control group.

# **Utilization**

Table 6 depicts the findings on utilization. Cross-section estimates based on equation (1) are given in Panel A. Estimates from a value-added specification, where we use average utilization for outpatient services and deliveries pre-intervention as a lagged variable, are shown in Panel B, and estimates from a difference-in-differences specification, equation (2), are reported in Panel C.<sup>16</sup> Four years after the intervention started, we find large positive effects on all four utilization measures. Although the point estimates are imprecisely estimated in the cross-section specification (see Panel A), the average standardized effect, reported in specification (v), is significantly different from zero. The precision improves and the point estimates are larger in the value-added specification, in which utilization for general outpatient services and deliveries are significantly different from zero at the 10 percent and 5 percent level, respectively. For both the value-added and the difference-in-differences specifications, the average standardized effects are highly significant.

The impact on utilization is substantial. For outpatient services, the increase goes from 16 percent in panel A to 27% in panel B. For deliveries, the point estimate in panel B suggests a 50 increase in utilization. For antenatal care, the increase varies between 21 percent (in panel A) to 25 percent in panel B.

The last three columns in table 7, Panel A and C, report changes in utilization patterns based on household data. We collected data on where each household member sought care in case of illness that required treatment. As is evident, households in the treatment communities switched from traditional healers and self-treatment, specification (vii), to the project facility, specification (vi), in response to the intervention. The average standardized treatment effects, reported in specifications (viii) and (xix), are significant in both the cross-section and the panel model.

> 16We have pre-intervention data for outpatients and deliveries but not antenatal care and family planning. Thus, we use the average utilization for outpatient services and deliveries pre-intervention as a lagged variable in the value-added model and estimate

_yjst_ = _αVA_ + _βVATj_ + _λ_ ̄ _yjt_ −1 + _εj_ .

where _ysjt_ is utilization for service _s_ in facility _j_ at time _t_ , and _y_ ̄ _jt_ −1 is the average utilization for outpatient services and deliveries in the pre-intervention period _t_ − 1. The difference-in-differences models can only be estimated on outpatient services and deliveries for which we have pre-intervention data.

12
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# **Can China’s Rural Elderly Count on Support from Adult Children?**

**Implications of Rural-to-Urban Migration**<sup></sup>

John Giles<sup>+</sup> Dewen Wang<sup>++</sup> Changbao Zhao<sup>*</sup>

**Key Words:** Ageing, Elder Support, Intergenerational Transfers, Migration, China. **JEL Codes** :  J14, O12, D64, H55, Q12

>  This paper has benefitted from the helpful suggestions of Emmanuel Jimenez, Phillip O’Keefe, Robert Palacios, Maria Porter, Adam Wagstaff and Xiaoqing Yu and from comments on an early presentation of this work from participants at the Association for Public Policy Analysis and Management (APPAM) meetings held in Los Angeles, November 6-8, 2008. We are also grateful for financial support for supplemental data collection which from the National Science Foundation (SES-0214702), the Michigan State University Intramural Research Grants Program, the Ford Foundation (Beijing), the Weatherhead Center for International Affairs (Academy Scholars Program) at Harvard University and the Center for Retirement Research at Boston College.

+Development Research Group, The World Bank, Washington (jgiles@worldbank.org).

++East Asia and Pacific Social Protection Unit, The World Bank, Beijing (dwang2@worldbank.org).

- *Research Center for the Rural Economy, Ministry of Agriculture, Beijing (changbao@rcre.org.cn).
**Table 1: Parameter values for simulations**

|_Symbol_|_Description_|_Value_|
|---|---|---|
|_a_|Biting rate (number of bites per female mosquito)|100 per year|
|1<sup>_b_</sup>|Proportion of infectious bites on humans that<br>produce an infection|0.8|
|2<sup>_b_</sup>|Proportion of bites by susceptible mosquitoes on<br>infected humans that produce an infection|0.8|
|_m_|Number of female mosquitoes per human host|0.1|
|μ|Mosquito mortality rate|36 per year|
|τ|Incubation period of parasites in mosquito|10 days|
|_w_<br>_q_|Spontaneous rate of recovery from sensitive strain|0.7/year|
|,<br>_X_<br>_Y_<br>_q_<br>_q_|Spontaneous rate of recovery from single drug-<br>resistant strain|0.7/year|
|,<br>_XY_<br>_XZ_<br>_q_<br>_q_|Spontaneous rate of recovery from strain resistant<br>to two drug|0.84/year|
|_XYZ_<br>_q_|Spontaneous rate of recovery from multi-drug<br>resistant strain|1/year|
|α|Malaria attributable mortality|0.2/year|
|ρ|Excess rate of recovery of treated individuals|12/year|
|γ|Rate of loss of immunity|2/year|
|_X_<br>_s_|Rate of emergence of_de novo_resistance to<br>artemisinin monotherapy|12<br>10<sup>−</sup>|
|_Z_<br>_Y _<sup>_s_</sup><br>_s_ ,|Rate of emergence of de novo resistance to partner<br>drug monotherapies|3<br>10<sup>−</sup>|
|_XZ_<br>_XY _<sup>_s_</sup><br>_s_<br>,|Rate of emergence of de novo resistance to<br>combinations|15<br>10<sup>−</sup>|
|δ|Underlying population death rate|5<br>10<br>3.3<br>−<br>×|

_Source_ : See Laxminarayan (2004).

24
previous five years, increased from 27 percent in 2000 to 43 percent in 2011 (Carranza and Gallegos, 2013).

Physical violence against women became less socially acceptable during the decade, but the rates of women and men that believe physical violence is justified remains high. Between 2000 and 2011, the share of women who found wife beating acceptable under specific circumstances decreased from 85 to 68 percent. The reduction was larger among younger women (it fell to 64 percent) and among men. In 2000, 75 percent of men justified wife beating and in 2011 this was 45 percent (Carranza and Gallegos, 2013). The high proportion of women and men who still agree with wife beating is concerning. Carranza and Gallegos note that the belief that domestic violence is justified is frequently correlated with poorer wellbeing outcomes among women and their children. Women who believe that a husband is justified in hitting or beating his wife tend to have a lower sense of entitlement, self-esteem and status. Such a perception acts as a barrier to accessing health care for themselves and their children, affects their attitude towards contraceptive use, and impacts their general wellbeing.

The harmful practice of female circumcision is still widespread despite its illegality. A 2003 UNICEF report ranks Ethiopia among the top countries where female genital mutilation or cutting (FGM/C) practices are common (UNICEF 2003). The report shows that there were 23.8 million girls/women who have undergone FGM/C. In 2011, 30 percent of Ethiopians in rural areas and 19 percent of Ethiopians in urban areas lived in households in which a girl younger than 14 had been or would be circumcised. The WIDE-3 studies documented that the practice was still widespread and that in some sites there was vocal female opposition to the ban (Bevan, Dom and Pankhurst 2013).

19
|**Issue**|**Recommendation**|
|---|---|
||individual labor module (after the screening questions).<br>For activities which are either (i) entrepreneurial and/or (ii) communal to the<br>household (e.g. farming), separate modules are preferable. Usually this<br>means including:<br>(i) a specifically designed module to collect data on household agricultural<br>activities. Data would be collected on land and other inputs, including<br>household labor inputs, and on the value of production sold or retained at<br>the household; and<br>(ii) a specific non-farm household enterprise model.|
||Both modules could be used to measure the inputs (hours) of all household<br>members rather than ask these questions individually, but asking both<br>individual questions on hours and enterprise/plot level questionsof labor<br>inputs is preferable to make sure that all activities get covered for all<br>individuals. See Grosh and Glewwe (2000) for examples of each<br>approach.|
|_Source: Authors_||

These techniques are often not used. One excuse sometimes offered for not using these good practice techniques is that they make the questionnaire “too long”. It is certainly true that trying to collect too much data within a multi-purpose household survey can result in a long and tedious questionnaire, and a very expensive survey – which then will become imprecise exactly because of interviewer and respondent fatigue (Grosh and Glewwe, 2000). Certainly in the case of a census, for example, to keep costs down a short questionnaire is necessary for the majority of the respondents. Some compromises are inevitably made. The problem is that the tradeoffs being made with respect to the quality and consistency of labor force and employment data may not be considered at the time because the good practice approaches are often not even tested against the “shortcut”. Only after the survey is complete does it become clear that the shortcut actually cost more because the data have too much non-sampling error to be reliable, or even usable. The result of shortcuts can be poor quality data on employment outcomes, leading to ill-informed policy choices.<sup>11</sup> In the remainder of the paper, we demonstrate how serious these non-sampling error biases can be, using the case of Uganda.

> 11 Although failure to properly screen and measure economic activity is most common in censuses, it is also found in SSA in CWIQ and DHS surveys, and in some cases in multipurpose integrated household surveys.  Both the CWIQ and the DHS surveys were not designed to measure individual or household economic activity, but in both surveys employment status is often used by analysts as an the key independent variables to explain the demographic, health, and service delivery outcomes for which the surveys were designed.

10