rafmacalaba/datause-encoder-data huggingface dataset:
Table 4.24. Health utilization by household head's education

|||Pre-crisis||Post-cnsis||
|---|---|---|---|---|---|
|||Visited<br>Clmic|Hospital|**Visited**<br>Clinic|Hospital|
|_Illiterate_||||||
||Mean|0.170<br>0.170|0.000|0.063<br>0.063|0.040|
||Stderr.|0.055<br>0.055|0.000|0.036<br>0.036|0.029|
||Obs.|47||||
|_Primary_<br>_education:_||||||
||Mean|0.280<br>0.254|0.058|0.178*<br>0.165*|0.032|
||Std.err.|0.025<br>0.024|0.013|0.021<br>0.020|0.010|
||Obs.|331||||
|_Secondary_||||||
|_education:_||||||
||Mean|0.349<br>0.334|0055|0.295*<br>0.281*|0.051|
||Std. err|0.014<br>0.014|0.007|0.013<br>0.013|0.006|
||Obs.|1173||||
|_Higher_||||||
|_education._||||||
||Mean|0.386<br>0.372|0.036|0.312<br>0.304|0.019|
||Std.err.|0.033<br>0.033|0.013|0.032<br>0.031|0.009|
||Obs.|215||||

(*) Statistically different (lower) than the corresponding pre-cnsis value.

In table 4.24, we at health care utilization by the household head's education. The table shows that household utilization increases with the level of education of the head. For instance, 33.4% of households with a head with secondary education had visited a clinic before the crisis, compared to 25.4% for households with head with primary education. This relationship would remain after the crisis.

It also seems to be the case that household with more educated head had lower health utilization drops after the crisis. Although utilization decrease on average for all household, regardless of the head's education, household with head with secondary education had an utilization drop of 5.3 percentage points (16%), while households with head with primary education had a drop of 8.9 percentage points (35%). This seems to hold true for the other educational categories, with households with illiterate heads experiencing the largest drops in utilization and households with heads with higher education having small decreases. However, the imprecise means, due to the limited sample sizes for these other groups, impede to establish this significantly.

All this may reflect that at higher education level there is more awareness on health. But it may well reflect that household with more educated heads are relatively richer or are located in relatively more urbanized area, which facilitate access to health care. In the next section we take a more careful look at this controlling for all these effects.

38
40 The percentage of households purchasing LPG was higher in urban than in the corresponding rural quintile and increased with quintile level (Table A2.5). Most households reporting positive consumption of LPG used it as the primary cooking fuel. The quantities purchased were also higher for urban quintiles and again increased with quintile level.

Table A2.5: Number of households purchasing LPG and average quantity purchased per month by purchasing households in India

|**Quintile**|**Number of**<br>**Rural**|**purchasing househo**<br>**Urban**|**lds (percent)**<br>**Total**|**Kg purchase**<br>**Rural**|**d per househol**<br>**Urban**|**d (per capita)**<br>**Total**|
|---|---|---|---|---|---|---|
|**1**|112,425 (0.4)|21,550 (2.1)|133,975 (0.4)|7.3 (1.0)|9.6 (1.3)|7.6 (1.1)|
|**2**|674,055 (2.0)|243,925 (9.4)|917,980 (2.6)|7.7 (1.3)|9.6 (1.5)|8.2 (1.3)|
|**3**|2,120,861 (6.4)|1,160,992 (21)|3,281,853 (8.5)|8.6 (1.6)|10.6 (1.8)|9.3 (1.7)|
|**4**|5,366,497 (18)|5,350,249 (44)|10,716,746 (25)|8.8 (1.9)|11.5 (2.2)|10.2 (2.0)|
|**5**|8,482,313 (49)|25,548,510 (76)|34,030,823 (67)|9.5 (2.6)|12.3 (3.2)|11.6 (3.1)|
|**All**|16,756,151 (12)|32,325,226 (59)|49,081,377 (24)|9.1 (2.2)|12.1 (3.0)|11.0 (2.7)|

_Source:_ Authors’ calculations. _Note_ : See notes for Table A2.3.

The shares of total expenditure allocated to LPG for all households increased with quintile level for both rural and urban groups, while the shares for user households decreased. The shares for urban user households were greater than for rural except for the highest-income group (Table A2.6). The coefficient of variation of the calculated prices of LPG was 0.09, indicating that 95 percent of prices were within 80–120 percent of the national average price.

Table A2.6: Shares of total household expenditure on LPG for all households and for purchasing households in India (%)

|**Quintile**|**Rural**|**All households**<br>**Urban**|**Total**|**U**<br>**Rural**|**ser households**<br>**Urban**|**Total**|
|---|---|---|---|---|---|---|
|**1**|0.0|0.2|0.0|6.9|8.0|7.0|
|**2**|0.1|0.7|0.2|6.3|7.0|6.5|
|**3**|0.4|1.4|0.5|6.0|6.9|6.4|
|**4**|0.9|2.6|1.4|5.2|5.9|5.5|
|**5**|2.0|2.9|2.6|4.0|3.8|3.9|
|**All**|0.5|2.5|1.1|4.8|4.3|4.5|

_Source:_ Authors’ calculations.

# **Indonesia (SUSENAS, January Panel Module 2005)**

The data are taken from the consumption module of the National Socio-Economic Survey (SUSENAS) administered by Badan Pusat Statistik-BPS Statistics Indonesia. The fieldwork for the survey was carried out between January and March 2005.

The value of all non-purchased items (food only) was about 12 percent of total purchased expenditure. The ratio tended to decline at the upper quintile levels. The survey did not distinguish between purchased and non-purchased values for non-food items except for housing. The data were not deflated because adjustment factors were not available.
Table 2: Other farmers' characteristics by treatment status

|Variable|Treated||Control||Dierence|
|---|---|---|---|---|---|
||Mean|SD|Mean|SD|in mean|
|Midline survey: 2012||||||
|Is the head of household|0.585|0.493|0.588|0.493|-0.003|
|Male|0.420|0.493|0.414|0.493|0.005|
|Age|37.764|19.980|37.843|20.093|-0.079|
|Years of schooling completed|2.057|4.866|1.844|4.905|0.213|
|Single|0.063|0.504|0.058|0.509|0.005|
|Married|0.844|0.546|0.855|0.550|-0.011|
|Divorced, separated, or widowed|0.091|0.366|0.085|0.368|0.006|
|Number of children (ages < 15 years)|2.756|3.406|2.843|3.432|-0.087|
|Total hectares of owned land|2.004|3.995|1.880|4.033|0.124|
|Number of rooms in the house|1.427|2.116|1.444|2.138|-0.017|
|Housing walls made of brick|0.100|0.777|0.096|0.785|0.004|
|Housing roof made of tinplate|0.079|0.718|0.079|0.725|0.000|
|Midline survey: Recalled||||||
|Number of SLM techniques learned before 2010|1.236|4.514|1.303|4.563|-0.066|
|Number of SLM techniques adopted before 2010|0.509|2.024|0.554|2.045|-0.045|
|Number of observations|4,385||1,499||5,884|

Source: Household survey, 2012.

Notes: T-test inferences are based on standard errors clustered at the community level. ***, **, and * indicate signicance at the 1, 5, and 10 percent critical level for t statistics. SLM=sustainable land management.

33
Focusing on mutual funds offers important advantages. First, mutual funds, unlike other types of institutional investors, such as hedge funds, pension funds, and individual international investors, face regular reporting requirements. Thus, asset-level portfolios for mutual funds can be constructed and traced over time since their inception. Second, the structure of mutual fund families allows us to make within-family comparisons of the behavior of “specialized funds,” which can invest only in certain countries or regions, and “global funds,” which can invest anywhere in the world and thus have access to a larger set of instruments (more firms from more countries). Knowing that a fund within a mutual fund family holds certain stocks is an indication that the transaction costs to hold them are not exceedingly large and that those stocks are indeed desirable (at least to other fund managers within the same family). Moreover, information about those stocks has already been collected at the family level and, in principle, might be available to all managers in that family. Therefore, the relevance of information sharing, transaction costs, and industrial organization aspects discussed in the literature can be analyzed by comparing portfolios across funds within the same mutual fund family and across families. In addition, the returns of specialized funds can be compared to those of global funds, shedding light on the payoffs of different investment strategies.

We collect portfolio holding and return data for the universe of actively managed open-ended U.S. mutual funds established to purchase assets around the world. The data on holdings contain asset-level annual portfolios between 1991 and 2005. We work with a total of 499 fund families and 1,904 funds. The total number of fund-year observations is 8,420 and the total number of asset-level holdings for all funds in all years is 1,359,750. The portfolio holdings have been matched to identify the country to which each stock belongs, tracking holdings over time. We use daily returns at the fund level between September 1989 and June 2006 for 36 fund families that have a variety of mutual funds for which useful comparisons can be made.<sup>3</sup> We work with a total of 722,885 daily observations, comprising returns for all funds from these families.

Three stylized facts about mutual fund international investments emerge from our analysis. First, global funds have grown much more than specialized funds. For example,

> 3 For the return data, we focus on the largest mutual fund families in terms of assets under management and/or number of individual mutual funds. We are constrained by the need to concentrate on families with both global and specialized funds to make useful comparisons.

3
rigorously measure the effect of differences in the specific characteristics of the survey on employment statistics generated from the survey. Moreover, these effects may vary across groups in the population – for example, employment statistics for women and children may be particularly sensitive to the survey method.<sup>2</sup> When the wording of the employment questions emphasizes the main activity in order to define employment status, this may lead to underestimating the number of economically active women because of the large female presence among unpaid agricultural and family workers (Dixon-Mueller and Anker, 1988). Child and teenage work may be similarly underreported.<sup>3</sup> Different survey methods are expected to yield different measurement errors, and such errors may bias both survey statistics as well as estimates of relationships between measures of employment and other variables. This is true when labor variables are right-hand-side variables and when they are dependent variables. In continuous dependent variables, classic measurement error in the dependent variable does not bias OLS point estimates (although it may reduce precision). However, if the measurement error is introduced by the respondent “actively” trying to guess the true value of a variable of which they do not have exact information (this may be the case of a proxy respondent) it is possible that, in the linear regression framework, biases in the coefficients of interest are introduced – especially if both regressor and outcome variables are measured with error (Hyslop and Imbens, 2001). Moreover, in the case of discrete dependent variables the assumption of classical measurement error is not appropriate; that is, measurement error in discrete dependent variables, such as labor

> 2 Guarcello et al. (2009) review the discrepancies in child labor statistics across surveys of different design within a number of low-income countries.

> 3 Another important area where questionnaire design can impact the measurement is in regard to labor income from household-owned enterprises. This is not the focus of our paper. De Mel, McKenzie, and Woodruff (2007) implemented a survey experiment on this topic in Sri Lanka.

3
# Table 9 **Job** Turnover by Industry, 1999-2000

A. **Top 10 industries with highest rates of job B. Top 10 industries with highest rates of job creation destruction**

**creation**

|<br>Industry|Jobcreation<br>Share**in**<br>rate<br>employment|
|---|---|
|Financialintermediation|60.7<br>1.1|
|Wholesale trade|22.5<br>6.3|
|Otherbusinessactivities|19.9<br>2.7|
|Wood|18.5<br>2.6|
|Recreation&culture|14.6<br>0.9|
|Hotels&restaurants|14.5<br>2.3|
|Carsale&repair|14.4<br>2.9|
|Furniture|14.3<br>1.9|
|Retail trade|13.9<br>9.0|
|Rubber&plastic|13.9<br>0.9|

|Industry|Jobdestruction<br>Sharein<br>rate<br>employment|
|---|---|
|Construction|19.9<br>12.0|
|Insurance&pensions|18.2<br>0.6|
|Mineral products|17.8<br>1.9|
|Realestate|17.5<br>2.4|
|Hotels&restaurants|17.5<br>2.3|
|Carsale&repair|16.9<br>2.9|
|Wood|16.0<br>2.6|
|Leather|15.8<br>0.5|
|Retail trade|15.5<br>9.0|
|Recreation&culture|15.0<br>0.9|

**D. Top 10 industries with highest employment growth**

**C. Top 10 industries with highest excess job**

|**reallo**|**cation**||**gro**|**wth**||
|---|---|---|---|---|---|
|||||Net|Shi|
||xcesso|aren|||aren|
|Industry|<br>reallocation e|<br>mployment<br>reallocatio|Industry<br>n<br>employrnent|employment<br>~<br>growth|<br>~~employment|
|Wood|32.0|2.6|Financialintermed.|_55.5_|1.1|
|Wholesaletrade|29.4|6.3|0th.businessactivities|9.8|2.7|
|Recreation&culture|29.3|0.9|Wholesaletrade|7.8|6.3|
|Hotels&restaurants|29.0|2.3|RTV&communic.|5.2|1.3|
|Carsale&repair|28.8|2.9|Furniture|3.6|1.9|
|Retailtrade|27.7|9.0|Apparel|2.7|5.0|
|Rubber&plastic|27.7|0.9|Wood|2.5|2.6|
|Insurance&pensions|25.1|0.6|Petroleumproducts|-0.1|0.6|
|Metalproducts|23.5|1.3|Recreation&culture|-0.4|0.9|
|Travel|22.9|1.9|Rubber&plastic|_-0.5_|0.9|

Note: The ranking is based on mdustries whose share in total employment is at least 0.5 percent This means that 18 industries with smallest employment are excluded.

_Source. Annual survey of wages and salaries, 1999 and 2000, Bank staff calculations._ 21
- working with other Bank units, particularly the regions, to promote a multisectoral approach to population issues. This will entail systematic attention to population and fertility issues in country economic work and Country Policy Papers, as well as in education, agriculture and rural development;

- expanding the Bank's operational linkages with external organizations active in the sector, such as WHO, UNFPA and IPPF, as well as bilateral agencies and NGOs.

The Bank's commitment to population, heal hi and nutrition was reaffirmed by President Conable in his spe..ch to the Safe Motherhood Conference in Nairobi, February 1987. President Conable stated that lending to these sectors would double by 1990, totaling as much as $500 million, with projects in about 50 countries.

# **C. A Framework For** Assessing Program Effectiveness

Population sector activities are unusually complex in comparison with other development projects. They involve organized efforts to change behavior in areas that are particularly private. Population projects do not generate physical and marketable output as is usually the case with agricultural or industrial projects, and they are often undertaken in complex bureaucratic environments involving more than one agency. The role of donor agencies in this sector, being removed from direct action, is doubly complex. For these reasons it is particularly important to have a framework within which an assessment of Bank activities can be undertaken.

In this review the general approach of the Working Group on Family Planning Effectiveness of the National Academy of Sciences is adopted. Several helpful observations are made in the NAS report. First, while it is recognized that family planning programs have many possible impacts, the key measure of success which is adopted is the level of contraceptive prevalence which is achieved given the underlying level of demand. Second, family planning programs are recognized to be complex systems and it is argued that their effectiveness is determined by a number of elements that derive from the system. Third, the report suggests some ways that the system can be decomposed to assess the working of the system.

Figure 1, taken from the NAS report, is a flowgraph of the factors influencing program effectiveness.

The ultimate goal of most population programs is to influeuice fertility. (In most countries there are also goals for mortality reduction, but these will be given little emphasis in this review since they have already been covered by the Health

**7**
Table F.2. Treatment Effects on Misinformation about Venezuelan Forced Migrants

||% of<br>Venezuelans<br>in<br>Colombia|Venezuelans’<br>Years of<br>Education|Venezuelans’<br>Access to<br>Subsidized<br>Health<br>System|Venezuelans’<br>Children<br>Attend Public<br>Schools|Ven. Children<br>Born in<br>Colombia are<br>Colombians|Venezuelans<br>Pay Higher<br>Taxes Than<br>Colombians|Venezuelans<br>Receive More<br>Subsidies Than<br>Colombians|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Game [=1]|-0.483|0.708**|0.156***|0.040|-0.021|0.025|0.027|
||(1.117)|(0.281)|(0.041)|(0.027)|(0.033)|(0.038)|(0.041)|
||[0.694]|[0.051]|[0.003]|[0.333]|[0.538]|[0.538]|[0.538]|
|Video [=1]|-3.944***|-0.524*|0.072|0.034|0.005|0.040|0.038|
||(1.147)|(0.289)|(0.044)|(0.029)|(0.034)|(0.041)|(0.044)|
||[0.005]|[0.239]|[0.308]|[0.446]|[0.921]|[0.538]|[0.538]|
|Observations|812|826|825|825|825|828|828|
|Av. Dep. Var. (Control group)|16.803|7.431|0.474|0.858|0.813|0.289|0.563|
|Diff. p-value|0.002|0.000|0.045|0.846|0.444|0.695|0.803|
|YWYjoint test|0.000|0.000|0.000|0.322|0.710|0.664|0.557|
|Correct Answer|5%|5.98|Yes|Yes|Yes|No|No|

_Notes:_ This table presents OLS estimates derived from Equation 1. The dependent variables are as follows: (i) % of Venezuelans in Colombia: This variable represents the respondent’s answer to the question “What percentage of the Colombian population do Venezuelans represent today?” (ii) Venezuelans’ years of education represents the respondent’s answer to the question “How many years of average education do you think Venezuelan migrants in Colombia have?” (iii) Venezuelans’ access to subsidized health system is an indicator variable equal to 1 if the respondent agrees with the statement “Venezuelan immigrants have access to Colombia’s subsidized health system.” (iv) Venezuelan children attend public schools is an indicator variable equal to 1 if the respondent agrees with the statement “Venezuelan children can attend public schools in Colombia.” (v) Venezuelan children born in Colombia are Colombians is an indicator variable equal to 1 if the respondent agrees with the statement “Venezuelan children born in Colombia are Colombians (they have the right to obtain Colombian nationality).” (vi) Venezuelans pay higher taxes than Colombians is an indicator variable equal to one if the respondent believes that Venezuelans pay A lot more or More taxes than Colombians. (vii) Venezuelans receive more subsidies than Colombians is an indicator variable equal to one if the respondent believes that Venezuelans receive A lot more or More government transfers than Colombians. All columns control for gender and student status. The Diff. p-value reports the p-value of a Wald test assessing the equality of coefficients between the game treatment and video treatment. The YWY joint test reports sharpened False Discovery Rate (FDR) q-values. Significance levels are denoted as:<sup>_∗∗∗_</sup> at 1%,<sup>_∗∗_</sup> at 5%, and<sup>_∗_</sup> at 10%.
the fact that our weakly relative measures allow α > 0 , thus shifting up the schedule (Figure 2). There are 18 countries with _M_ in the interval ($1.95, $3.75), i.e., there are an extra 18 countries in the segment where the absolute line is no longer binding.

So our new data on national poverty lines suggest that relative poverty is a more prominent concern than past work indicated. This echoes our finding that the overall elasticity of the poverty line to the mean in our sample is quite high—less than unity but similar to some past estimates for developed countries.

What might we expect on _a priori_ grounds about the trends over time in weakly relative poverty, as compared to absolute poverty? That will depend in part on how the distribution of relative incomes evolves. As a stylized fact, there is no correlation across countries between rates of growth and rates of change in a standard measure of relative inequality.<sup>32</sup> In other words, amongst developing countries, economic growth tends to be distribution-neutral on average.<sup>33</sup> This motivates a consideration of distribution-neutral growth as a benchmark case. To see how the trend rates of reduction in the poverty rate will differ using our relative poverty measure under distribution-neutral growth, let _Fi_ (<sup>_Z_</sup> _i_ ) denote the proportion of the population of country _i_ living below our weakly relative poverty line, while _Fi_ ( _Z_ * ) is the corresponding poverty rate using the absolute line. Under a distribution-neutral growth process it is readily verified that the proportionate rates of poverty reduction are:<sup>34</sup> Here the partial elasticities, ∂ ln _Fi_ ( _Z i_ /) ∂ ln _M i_ < 0 and ∂ ln _Fi_ ( _Z_ * /) ∂ ln _M i_ < 0 , hold both<sup>_Z_</sup> _i_ and the Lorenz curve constant. Since our relative poverty measures satisfy the WRA, the relative poverty rate will fall as long as the growth rate ( _d_ ln _M i_<sup>/</sup> _dt_ ) is positive. The absolute poverty rate will also fall with positive growth. Whether or not the relative poverty measure falls more

> 32 Ferreira and Ravallion (2009) provide an overview of the evidence on this stylized fact.

> 33 Growth can be distribution neutral within all countries, but not distribution neutral in the world as a whole, depending on how the rates of growth vary with initial mean incomes. Ravallion (2009) shows that the overall growth process in the developing world has not been distribution-neutral.

> 34 ′

> We exploit the fact that _Li_ ( _Fi_ ( _Zi_ )) = _Zi_ / _M i_ where _L_ is the Lorenz curve. Thus _Fi_ (<sup>_Z_</sup> _i_ ) is homogeneous of

> degree zero in _Z i_ and _M i_ , holding constant the Lorenz curve (and hence the function<sup>_L_</sup> _i_<sup>′</sup> (.) ).

15
Promoting Democracy in Fragile States the incumbent UP. Our theory suggests that such a coordinated shift away from parochial candidates would have been facilitated by the deliberation and reflection on political alternatives that the town halls allowed. Voters in a community may have sought to coordinate their votes to maximize strategic impact (Cox, 1997; Dryzek, 2009).

The second unanticipated effect was in how the civic education and town halls program increased reports of exposure to some forms of intimidation. We consider a few possible explanations. First, we wanted to be sure that we were not being misled by differential non-response. The non-response rates for the items that appear in Table 7 are considerably higher than for the other outcomes (see the row labeled _N_ ). If it were the case that non-response rates were higher in the control areas, and that non-response tends to be associated with experience of manipulation or surrendering of rights, then this would result in a biased ATE estimate for the civic education program. However an analysis of non-response patterns does not show that they can explain the counterintuitive findings (see section I in the supporting information).

Second possibility, the fact that the outcome used in Table 7 are based on survey selfreports mean that they combine two types of effects: (i) effects on actual levels of manipulation and surrendering of rights and (ii) willingness to _report_ such manipulation and surrendering of rights. With respect to actual levels of threats, at a theoretical level, it is when citizens’ actions disturb an equilibrium favorable to incumbent elites that you would expect to see an attempts at intimidation. Such intimidation may be wielded in attempts to prevent the change. No meaningful political change comes without some degree of conflict. But it is also possible that the civic education and town halls program increased people’s _willingness_ to report threats, even if the level of threats did not change appreciably. We investigated these possibilities as best we could. As part of our project implementation, we had project monitors (who, for the program communities were the same as the field facilitators, and for the control communities were different) collect community-level indicators 33