rafmacalaba/datause-encoder-data huggingface dataset:
indicators such as weight and height of child and whether the child has been immunized. Computing HOI for access to basic infrastructure, such as safe drinking water, electricity and sanitation, would require household-level information for these indicators. With regard to circumstances, the minimum information needed to make the analysis meaningful would be gender, age, and location (urban/rural and/or regional) of the child; demographic characteristics of the household (size and composition); characteristics of parents (gender, age, and education); and some measure of household income, consumption, or wealth.

In practical terms, computing HOI for a particular opportunity when the number of circumstances is relatively large (more than three) requires an econometric exercise, which involves obtaining a _prediction_ of the D-index from observed access to opportunities and circumstances among children. In simple terms, the exercise consists of running a logistic regression model on the full sample of children for whom the HOI measure will be constructed to estimate the relationship between access to a particular opportunity and circumstances of the child. The estimated coefficients of the regression are used to obtain, for each child, his or her predicted _probability_ of access to the opportunity, which is then in turn used to estimate the D-index, the coverage rate, and eventually the HOI (see box 3 for more technical details on the econometric exercise).

# **Box 3. Computing the Human Opportunity Index from Household Survey Data**

To construct the HOI, the conditional probabilities of access to opportunities for each child based on his or her circumstances must be obtained. To do so, one can estimate a logistic model, linear in the parameters β , where event I corresponds to accessing the opportunity (for example, access to clean water), and _x_ the set of circumstances (for example, gender of the child, education, gender of the head of the household, and the like). One can fit the logistic regression using survey data: where _xk_ denotes the row vector of variables representing the _k_ dimension of circumstances, hence, _x_ ( _x_ 1,..., _xm_ ) and  (1,..., _m_ ) a corresponding column vector of parameters. From the estimation of this logistic regression, ˆ one obtains estimates of the parameters  _k_ to be denoted by  _k_ , _n_  where _n_ denotes the sample size.

Given the estimated coefficients, one can obtain for each individual in the sample his or her predicted probability of access to the opportunity in consideration: ˆ _p_ ˆ _i_ , _n_  1  _ExExpp_ <u></u> _x_ <u></u> _<u>ix</u>_ <u></u> _i_  _<u>n</u>_ ˆ _n_ <u></u> .

Finally, compute the overall coverage rate, _C_ ; the D-Index; the penalty, _P_ ; and the HOI using the predicted ˆ probability _p_ and sampling weights, _w_ : 8
(which would imply more variables and/or a highly non-linear terms). We use log of deposit accounts/1000 adults as the LHS variable in the regressions because the relationship with observable factors is more straightforward, and because doing so avoids predicting negative values for the total number of accounts. Using the log, however, presents a challenge because the predicted values must be retransformed and multiplied by adult population to calculate the total number of accounts in the country, and this process can induce a bias due to the effects of Jensen’s Inequality. There are various methods to correct for this “retransform bias”, but we choose the non-parametric smearing method (see Duan (1983)) retransformation correction which is robust to non-normal residuals (the residuals from some of our specifications do not appear to be normal based on a joint test of skewness and kurtosis). After generating a model, we then predict out of sample the number of deposit accounts per 1000 adults for the remaining countries and retransform the series to get the total number of accounts in each country. Where a given piece of data is missing for a given country (e.g. if we do not know the number of commercial bank branches in a country), a model with only the known elements is estimated and used to predict the number of deposits for the countries with that data element missing. In all the models were able to explain more than 70% of the variation in deposit accounts (within sample) and greater than 80% when all predictors were available. Table 7 contains the results of the different regression specifications; Table 8 describes the variables used in the different models.

# **_4.1.2 Cooperatives, credit unions, and other mutually owned banks_**

[ _Preferred estimate_ ] For countries which did not report data on the number of deposit account in these institutions, we use the reported numbers of _clients_ from the World Council of Credit Unions (WOCCU). In general, the WOCCU numbers should understate the true number of accounts in all mutually owned institutions, since they enumerate clients rather than accounts, and are voluntarily reported data for credit unions only, not all mutually owned institutions. Thus, we believe the WOCCU numbers are a very conservative estimate of the number of accounts.<sup>14</sup> For 13 countries in Europe for which there were no WOCCU data, we have data –

> 14 Nevertheless, there is a chance that some of the WOCCU members are unregulated institutions, and thus that the numbers reported by WOCCU are higher than what would have been reported by the financial regulator, implying 17
meaningfully code the quality of participation because public participation was very low to nonexistent because of the contextual factors described before which curtailed free public discussion in _gram sabhas_ .

While the finding regarding the quality of SHG women’s participation matched our expectation, the finding that nearly half of verbal interjections by non-SHG women also displayed a high quality of participation was unanticipated. In explaining this pattern we must consider a number of factors that might have a positive influence on women’s quality of participation independent of SHG membership. The literacy level is an important factor among them because we can expect literacy to make a positive difference to the quality of participation because of its role in making villagers more aware and articulate. From the village-level literacy data<sup>14</sup> , we see that villages from which _gram sabhas_ were sampled in one district in Karnataka had uniformly high literacy levels and in the other district had largely medium literacy. This fact combined with the long history of the panchayat system and _gram sabhas_ in this state and the vocal participation of de facto female leaders (female ward members; school headmistresses; female staffs of government departments, like the women the children dept.), who are a significant presence in _gram sabhas_ in medium to high literacy villages in Karnataka, might explain why women not affiliated with SHGs would be able to participate in _gram sabha_ discussions in a qualitatively superior way at least half of the times. In Tamil Nadu, the literacy level in villages from which _gram sabhas_ were sampled in the two districts ranged from medium high to medium low. So here, too, literacy could be having a marginally positively influence on the quality of participation of non-SHG women. In the case of Tamil Nadu, with its long-running state sponsored SHG program (which would have completed roughly 16 years by 2006), there is also the strong possibility of a spillover effect of SHGs on the presence and participation of non24
- 16 -

5.02 The enumerator then asked the respondent whether he or she would like to be connected to the New Onitsha Water Scheme and have a meter if the price of water were N 1 per drum.9' Figure 4 presents in schematic formlVe way the the "bidding game" proceeded in response to this question., For example, if the respondent answered "Yes" to a price of N 1 per drum, then the enumerator raised the price to N 2 per drum, and again asked whether the respondent would like to have a metered connection. If the respondent answered "No" to a price of N 2 per drum, the enumerator lowered the price to N 1.50 per drum, and again asked the respondent whether he would like to have a metered connection. After this question was answered the enumerator stopped the bidding game. Similarly, in the first instance if a respondent said "No" to water at N 1 per drun., the price was lowered in increments to N 0.12.

5.03 Using this bidding game procedure, it was possible to classify each household into one of the following seven groups based on how much the respondent indicated the household was willing to pay per drum: _N_ 0.00 - 0.11 per drum _N_ 0.12 - 0.24 " " _N_ 0.25 - 0.49 " " N 0.50 - 0.99 " " _N_ 1.00 - 1.50 " " N 1.50 - 1.99 " " > N 2.00

- 9/ In most situations it would be impossible to carry out a willingness-to-pay survey in which the enumerator raised or lowered the commodity price in this manner because individuals would simply not know how much water they consumed in terms of a standardized unit, such as a drum. However, because water vending is so widespread in Onitsha, not only is almost everyone used to thinking in terms of using standardized volumes of water, but they are also used to paying for water by the drum, bucket, or 1000-gallon tank. In this sense, Onitsha was an ideal setting for conducting a willingness-to-pay survey in which the price of water was varied.

- 10/ This bidding game approach for estimating the willingness of households to pay for water is one of several possible ways of eliciting households' preferences. The general methodology is termed the "contingent valuation method" because the respondent is asked how he would behave in a hypothetical or "contingent" market. For excellent reviews of the current state of the art, see (a) Ronald C. Cummings, David S. Brookshire, and William D. Schulze (editors), 1986, and (b) Robert Cameroon Mitchell and Richard T. Carson, 1989. For a discussion of the application of contingent valuation techniques in developing countries, see Whittington, Briscoe, and Mu, 1987.
This is not uncommon in social programs.12 Naturally it constrains the scope for pro-poor geographic targeting. We will see how much so later.

In the second stage, FEE households are identified within the selected UP's. Widows, day-laborers, low income professionals,'<sup>3</sup> landless or near landless farmers, and households with school-aged children not covered by other targeted programs are officially eligible to receive the program. 14 The program relies heavily on community involvement in the selection of the households. The selection is typically done by the School Management Committee (SMC); this is composed of teachers, local representatives, parents, education specialists and donors to the school. The food is distributed by the SMC (or sometimes by the UP or a local NGO). Each participating household is entitled to receive 15kg per month for each child enrolled in school, up to 20kg for more than one child.

The empirical analysis will be based on the Household Expenditure Survey (HES) collected in 1995-96 by the Bangladesh Bureau of Statistics, following well-established and credible survey practices, with support from international agencies including the World Bank. The household questionnaire contains extensive information on household expenditures, and has specific questions on household participation in FFE. A comprehensive consumption aggregate can be formed from the data, including imputed values of consumption in kind, valued at local market prices. We deduct the imputed value of the FEE transfer from the consumption aggregate. A simple random sample of households was drawn from each primary sampling unit (PSU) and a detailed community survey was done. The PSU is the 12 See, for example, the discussion of the political economy of program placement for an Argentinean program in Ravallion (1999c).

**13** Defined as fishermen, weavers, cobblers, potters, blacksmiths, etc.

14 In particular, the Vulnerable Group Development and Rural Maintenance Program are government sponsored programs distributing food to the poor that are not compatible with the FFE program. The household questionnaire accounts only for household participation to the FFE program. The community questionnaire contains information on the presence of the Vulnerable Group Development (reported positive for 4% of the villages) and on other programs: there are very few villages in which the two sets of programs overlap, so that the extent of potential omitted bias when analyzing the intra-village targeting performance is relatively small.

17
well as relative skill endowments, for enterprises in large developing countries find it advantageous to locate their skill-intensive operations in Singapore. Our result suggests that as Singapore becomes even more skill-abundant, for given GDP levels, Chinese enterprises invest even more there. For specific countries, over the sample period an increase in parentcountry Singapore’s skilled-labor abundance relative to that of host countries increased outbound FEI stocks to all developing countries in the sample.

Table 4 offers a similar cut of the data by considering manufacturing inbound and outbound FEI stocks broken down into investment with industrialized and developing countries. The first and second columns list results for manufacturing data with developing countries and the third and fourth columns for industrialized countries. Unlike the aggregate sample, variations in joint market size and the squared GDP differences are insignificant for FEI stocks to and from industrialized and developing countries.

The primary difference between bilateral investment with industrialized and developing countries arises in the coefficients on skill differences. The direct impact on inbound and outbound FEI is negative in the case of industrialized partners, although in the case of inbound stocks insignificant. In contrast, this coefficient is significantly positive for both inbound and outbound manufacturing investment with developing countries, indicating that this investment has a vertical orientation. As Singapore’s skill endowments rose compared to those in neighboring developing economies, outbound manufacturing FEI stocks grew larger. The results are similar for inbound stocks, where the skill-difference variable is also positive and significant at the one percent level. The positive coefficient implies that inward FEI in Singapore from such countries would increase indicating that as they become more skill abundant relative to Singapore they invest more in the latter. Finally, 26
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3 consumption). Under the law of one price, and given relatively weak assumptions on preferences, relative prices are equal to the rate at which consumers themselves – regardless of their income levels and allowing for different utility functions – are willing to trade one such component (e.g. bread) for another (e.g. a bicycle).

There are, of course, a number of reasons why prices may not be ideal welfare weights in practice. These range from the existence of externalities to the fact that price data is often geographically coarse, so that much actual price variation in space is missing from the information available to the researcher. Nevertheless, as Ravallion (2011, p.247) argues: “It is widely agreed that prices can be missing for some goods and deceptive for others. There are continuing challenges facing applied economists in addressing these problems. However, it is one thing to recognize that markets or prices are missing or imperfect, and quite another to ignore them in welfare and poverty measurement. There is a peculiar inconsistency in the literature on multidimensional indices of poverty whereby prices are regarded as an unreliable guide to the tradeoffs, and are largely ignored, while the actual weights being assumed in lieu of prices are not made explicit in the same space as prices.”

# _1. What is the disagreement about?_

Some of the debate between proponents and skeptics of multidimensional poverty measurement was featured in the Forum Section of the June 2011 issue of the _Journal of Economic Inequality_ (e.g. Alkire and Foster, 2011b; Ravallion, 2011; Lustig, 2011). Those articles actually suggest strong agreement on at least one basic point: “poverty is multidimensional” (Ravallion, 2011, p.236). No one seems to dispute that deprivations exist in multiple domains, and are often correlated. And that, therefore, considering information on those various dimensions, rather than only on incomes or consumption expenditures, is likely to be useful for designing policies to effectively combat poverty.

The disagreement is about how best to _measure_ this multidimensional poverty – i.e. how best to convey information about the extent of these various deprivations in a way that is useful for analysts and policy makers. Some, like Alkire and Foster (2011a) and Maasoumi and Lugo (2008), have proposed scalar indices that seek to combine, in a single number, information from those various dimensions. A key advantage of such scalar indices is that they generate a complete ordering of countries, regions or individuals, even when the rankings disagree across individual dimensions. The Multidimensional Poverty Index - or the Human Development Index, in the space of attainments - attracts a good deal international attention in large part because it