rafmacalaba/datause-encoder-data huggingface dataset:
60 In the Philippines, both of the major intergovernmental grant schemes in 1977 were observed to be allocated in a way such that larger per capita grants were provided to smaller jurisdictions. 43 A coefficient of -0.34 was obtained when per capita Bureau of Internal Revenue grants were correlated with the populations of 96 municipalities in four provinces. A slightly smaller correlation of -0.26 was found for the capital spendingoriented Specific Tax Allotment grant program.

If one assumes that these allocations were made with some redistributional objective in mind, the findings from Bangladesh, Pakistan and the Philippines would suggest that granting governments do feel that smaller jurisdictions need to be treated preferentially, probably because of the perception that less populated areas are less able to mobilize resources of their own. Unfortunately, there is little evidence regarding the empirical relationship between actual local tax bases and grant allocations. One exception is the previously cited analysis of grants in the Philippines.44 Interestingly, the data there suggest that, at least for the BIR Allotment program, allocations of grants per capita were positively related, i.e., counter-equalizing, to property tax assessments per capita (a correlation of +0.27). The Specific Tax Allotment grant scheme did yield a negative (equalizing) correlation coefficient, but the coefficient was not significantly different from zero.

%3Bahl and Schroeder, "Intergovern6ental Fiscal Relations." 44Ibid.
in the analysis.

# **5 Experimental Design**

## **5.1 Willingness-to-Pay Elicitation**

Enumerators carried out home visits to elicit participants’ willingness-to-pay for a threehour training session about Fall Armyworm. During the home visit, each participant formulates her willingness-to-pay to receive training herself, and also her willingness-topay for each other sampled farmer in her village to receive the training. These valuations are elicited in a way that makes it clear they are mutually exclusive events: only one farmer can actually receive training.

The elicitation technique I employ is a multiple-price list variant of that proposed by Becker et al. (1964), in which a participant formulates a bid _b_ for an item and the bid is compared with a random price draw _p_ . If the bid is equal to or larger than the random price, the participant has to buy the item; if the bid is lower than the random price, the participant cannot buy the item.

Before the exercise, enumerators ask participants if they know what the Fall Armyworm is and inform them it spreads by proximity, so that the closer a plot is to pest-infected plants, the likelier it is that the pest spreads to the plot.<sup>12</sup> We also inform participants that one decision maker would be randomly selected in their village. The decision maker is the one who actually gets the opportunity to purchase the training, and participants do not know the identity of the decision maker before the elicitation. We inform participants they will make _N_ bids, _N_ = [6 _,_ 9], choosing their willingness-to-pay for themselves and for up to eight other farmers to receive the training in their village, but only one of these _N_ choices will be actually implemented if they are the decision maker. If they are not the decision maker, none of their choices will be implemented.

In the multiple-price list variant I employ, the participant is presented with 21 amounts, ranging from 0 to 40,000 UGX ($ 34.6) and increasing in steps of 2,000 UGX ($ 1.7): (0, 2,000, 4,000, ..., 40,000).<sup>13</sup> At every step, the participant is asked if she would be willing to pay amount _x_ to purchase the training. If the respondent accepts to pay _x_ , she is asked if she would be willing to pay _x_ + 2 _,_ 000. If she does not accept to pay _x_ , the enumerator stops and records her bid as _b_ = _x −_ 2 _,_ 000. Note that negative prices and prices above 40,000 are not allowed in the experiment. There are no participants who refuse to pay 0, and if a participant is willing to pay 40,000 or higher the enumerator records _b_ = 40 _,_ 000.

The enumerator follows this procedure to obtain a participant’s willingness-to-pay for

> 12Figure A.2 shows that, one year after this exercise, farmers believe that the closer a plot is to an infected plot, the likelier it is to be infected. Farmers’ beliefs are dispersed but decrease linearly with distance.

> 13All US Dollar amounts are expressed in PPP terms. I calculate amounts in $PPP using the 2018 conversion rate of $1=1157.27 LCU (Local Currency Units), retrieved by the World Bank Databank. Numbers in $PPP are rounded to the first decimal digit.

11
“measured financial literacy” papers. They were excluded because they did not test an intervention but they do provide valuable insights on the importance of financial literacy and knowledge on outcomes. Studies were included when they discussed any program, educational outreach, media intervention or other type of communication or training for consumers which could either strengthen knowledge (financial literacy) or modify attitudes and behaviors (financial capability).

One of the insights from the Econlit search on these key terms is that a relatively small percentage of the literature involves an evaluation of some kind of financial literacy or capability intervention or program. Even for the terms “financial education” and “financial literacy” only about 10% of the studies in these categories evaluated the effectiveness of an intervention. Searches of “financial awareness” and “financial competence” yielded no impact evaluations and even the widely used term “financial capability” produced only two papers for the database out of a total number of 77 citations. With the exception of the search for “financial literacy,” a vast majority of the papers listed under the searches conducted on Econlit are not about personal financial literacy or capability.

The review of nine previously published narrative reviews identified more than 500 unique references which included more than half of the 188 papers that are listed in Appendix 1. The references cited by these nine narrative literature surveys, however, have relatively little overlap. This can be seen with greater precision in Table 2 below which presents the Pearson correlation coefficients and associated R<sup>2</sup> terms for reference lists across the literature survey papers.<sup>6</sup> In only two instances do the survey papers in Table 2 have a correlation coefficient above 0.2 (Martin 2007 with Hathaway and Khatiwada 2008 and Collins and O’Rourke 2010 with Agarwal et.al. 2010), which is already a relatively low level of correlation. The R<sup>2</sup> terms in parentheses (which is simply the squared term of the correlation coefficient) are sometimes called the coefficient of determination and provide another indication of the strength of the relationship among the variables. Approximately 55% of the R<sup>2</sup> figures are below .01 indicating a very weak relationship and relatively little overlap across studies.

> 6 A matrix was created based upon the references in each of these papers, for a total of 536 unique citations. For each of the nine literature reviews, a “1” or “0” was entered to indicate whether the reference was cited in the review. This matrix was then used to calculate the Pearson correlation coefficients and r<sup>2</sup> terms.

8
_pL_ varies depends on how _uS − uL_ changes. If<sup>_d_</sup><sup><u>(</u></sup><sup>_uS_</sup> _db_<sup>_−uL_</sup><sup><u>)</u></sup> decreases, both prices and quantities of _l_ would increase, consistent with our empirical results, while tempering the increase in _pA_ .

Chen and Rey (2012) show that their results on loss leading can be extended to heterogeneous valuations for _B_ as well as imperfect competition among large retailers and heterogeneous valuations for _A_ , both of which relax the degree of asymmetric market power between food and non-food markets. The result that an increase in valuation for the more competitive product will lead to a rise in price of the less competitive one can be extended similarly.

# **M.4 Multi-product Pass-through Formula Example**

In addition, we can use this model as an example to illustrate the intuition behind the multi-product pass-through formula for two products in equation (42). We can write out the demand and inverse demand as follows: _∂qL ∂qA ∂qA ∂qL_ With these equations, we can observe that<sup>_−f_(</sup><sup>_vAL_)and</sup> _∂pA_<sup>=</sup> _∂pL_<sup>=</sup> _∂pA_<sup>=</sup> _∂pL_<sup>=</sup> _−f_ ( _vAL_ ) _− f_ ( _τ_ ). Therefore, _A_ and _L_ are complements. In addition, we have equations for the inverse demand and hence the response of marginal revenues to quantities and benefits, as follows: 69
# **_Table 4a. Descriptive statistics for rural schools, by class size quartiles_**

|Rural Schools||First Quar<br>|tile<br>|S<br>|econd Qua<br>|rtile<br>|<br>|Third Quar<br>|tile<br>|F<br>|ourth Qua<br>|rtile<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||#|Mean|Median|#|Mean|Median|#|Mean|Median|#|Mean|Median|
|Mean Score|1839|148.69|148.80|1487|148.45|148.50|1248|148.24|148.30|1462|147.96|148.10|
|Median Score|1839|146.62|147.00|1487|147.48|148.00|1248|147.77|148.00|1462|147.60|148.00|
|Percentage >173|1839|9.11|0.00|1487|9.34|0.00|1248|9.62|7.69|1462|10.25|7.69|
|Percentage >150|1839|47.81|50.00|1487|47.75|50.00|1248|47.15|46.67|1462|46.21|46.29|
|Percentage<135.5|1839|24.26|20.00|1487|24.56|22.22|1248|25.81|25.00|1462|27.37|25.00|
|Participation ratio|1839|74.38|75.00|1487|73.85|75.00|1248|73.25|75.00|1462|69.74|71.75|
|Exams Per Student|1839|3.20|3.17|1487|3.24|3.20|1248|3.28|3.25|1462|3.29|3.26|
|Nr of Subjects|1839|5.04|5.00|1487|5.87|6.00|1248|6.47|7.00|1462|6.94|7.00|
|# Students|1839|106.92|96.00|1487|136.87|125.00|1248|196.17|170.50|1462|274.06|234.00|
|# Teachers|1839|18.98|18.00|1487|21.26|20.00|1248|25.67|23.00|1462|31.99|29.00|
|# Non-Teaching Staff|1839|12.01|11.00|1487|13.35|13.00|1248|15.67|14.50|1462|17.51|16.00|
|Students/Teacher|1839|5.53|5.21|1487|6.39|6.16|1248|7.51|7.37|1462|8.43|8.26|
|Students/Staff|1839|3.39|3.21|1487|3.91|3.78|1248|4.64|4.50|1462|5.39|5.24|
|Class Size|1839|7.04|7.00|1487|11.43|11.00|1248|15.49|15.50|1462|21.88|21.00|
|Share Ukrainian|1839|0.91|1.00|1487|0.90|1.00|1248|0.87|1.00|1462|0.89|1.00|
|Share of Males|1839|43.72|42.86|1487|43.46|44.44|1248|43.21|42.86|1462|42.08|42.31|
|Unemployment Rate in Rayon|1839|3.24|2.90|1487|3.03|2.70|1248|2.80|2.50|1462|2.66|2.30|
|Average Wage in Rayon|1839|1405.11|1336.00|1487|1404.92|1328.00|1248|1417.27|1338.00|1462|1430.95|1338.63|

21
Salehyan, 2021; Hirvonen, 2016). It is precisely for this reason that the capacity for migration in response to climatic shocks is much more limited than commonly assumed: poor people, who are disproportionately affected by climate change, have more incentives to migrate but often cannot leave because they lack the necessary resources (Cattaneo et al., 2019). In this perspective, migration is a costly investment in risk diversification that only richer households can undertake, while poorer households are “forced to stay” rather than “forced to move” (Kaczan and Orgill-Meyer, 2020). Depending on the interaction between the severity of the climatic event and household-specific characteristics such as wealth, number of income sources, assets and resources, we should expect a wide heterogeneity of outcomes depending on whether liquidity constraints or migratory responses ultimately prevail. Indeed, the empirical evidence is mixed, with some studies showing that the poorest households are more prone to migrate in response to weather shocks (Gray and Mueller, 2012b; Mueller, Gray, and Kosec, 2014; Mastrorillo et al., 2016), while others find that liquidity constraints trap poor people in immobility (Cattaneo and Peri, 2016; Hirvonen, 2016; Bazzi, 2017). Cattaneo et al. (2019) argue that this contradictory evidence can be explained by the different types of migration involved: poor families respond to negative shocks through low-return or even “survival” migration, taking the form of temporary movements across short distances, whereas wealthier families engage in risk management migration, which is typically costlier, semi-permanent and longer-distance migration. Koubi et al. (2022) suggest that (im)mobility depends on both the type of the climate shock that individuals experience and their adaptive capacity in terms of endowments and resources. The simulations by Choquette-Levy et al. (2021), who parameterize an agent-based model on household survey data from Nepal, support this perspective, but also highlight that cash transfers and risk transfer mechanisms may prevent climateinduced immobility of farmers.

In short, the final outcome is ultimately household- and context-specific. In such a complex picture, migration and immobility are only two of the possible outcomes, and the decision to leave can be interpreted as one of many potential adaptation strategies that an individual or household can adopt. As such, there is a need to improve our understanding of the causes and consequences of the immobility of populations ‘trapped’ by environmental disasters, because too often the policy focus is on ‘those who leave’ rather than to ‘those who cannot’ (Findlay, 2012).

# _E. Migration as adaptation_

One of the most interesting advances in the scientific literature is the progressive integration of the issues 10
expected from a drought, while a storm only has an impact over one year and although not shown in the figure the elasticity is about 40 percent that of the reported ones for earthquakes and floods.

<!-- Start of picture text --> Figure 2: Elasticity of Natural Capital w.r.t Different<br>Disasters<br>0.056<br>0.054<br>0.052<br>0.05<br>0.048<br>0.046<br>Earthquakes Floods<br>No Affected Per Capita<br><!-- End of picture text -->

# **_Impacts of natural disasters on income_**

First, we estimated a Cobb-Douglas production function relating income with four capital inputs (equations 10 and 11). Table 12 provides a comparison of the results from the fixed effects and random effects. The F-test rejects the null hypothesis that all dummy parameters except one are zero, thus implying that a fixed effect model is better than a pooled OLS model. On the other hand, the Hausman specification test rejects the null hypothesis that individual effects are uncorrelated with other regressors in the model (i.e., that random effects is the appropriate method), thereby supporting the use of fixed effects.

_<u>Table 12: Fixed and random effects, regression results</u>_

|Variables|Fixed e|ffects||Random|effects|
|---|---|---|---|---|---|
||Coeff.Estimate|Std error||Coeff.Estimate|Std error|
|TOPEN|0.17|0.01|***|3.94E-04|1.13E-03|
|ln(HR)|0.40|0.23|***|0.23|0.10 **|
|ln(PK)|1.17|0.50|**|0.74|0.13 *|
|ln(L)|0.07|0.04|***|0.13|0.02|
|ln(E)|0.10|0.04|**|0.07|0.04 ***|
|Intercept|-9.76|4.17|**|-2.11|0.46*|

Number of observations: 257; F test (99, 152) = 1.94, Prob>F = 0.0001 Hausman test, Ho: individual effects are uncorrelated with other regressors in the model, i.e., random effect model is appropriate. Chi2(5) = 55.97, Prob>chi2 = 0.0000 Notes: *, ** and *** mean statistically significant at 1%, 5% and 10% levels, respectively. TOPEN – trade openness, HR – human capital, PK – produced capital, L – land resources, E – energy resources.

17
16 these groups, but People's Republic of China, India, Mexico and Malaysia often are not. A question of interest is how sensitive these country classifications are to the definition used for manufactured goods.

Table 4 provides relevant information by tabulating 1987 manufactures exports under the IBRD/UNCTAD/GATr and UNIDO definitions for a selection of countries often included (or excluded) from these groups.'<sup>9</sup> The table shows the rank of each country based on total manufactures exports, as defined by IBRD/UNCTAD/GATT or LTNIDO, as well as the compound annual growth rate in these shipments over 1982 to 1987 and 1977 to 1987.

Two points are evident from Table 4. First, several of the countries appear to have been iDcluded in these classifications largely on the basis of their historical importance which has declined considerably in recent years. Argentina, for example, is currently the 16th largest developing coun!ry exporter of manufactures with shipments of less than one-quarter of India's, and also well below other countries like Malaysia, Indonesia, Pakistan, and Thailand. The People's Republic of China, a country often excluded, clearly should be added to the major exporter group. With over $27 billion in exports China was third behind Taiwan (China) and the Republic of Korea.20 '9 See UNCIrAD, Handbook of International Trade and Development Statistics 1987 Supplement, (New York: United Nations, 1988) particularly the country classifications outlined in the general notes on page iii to vii for a list of their "major developing country exporters of manufactures". The World Bank maintains a similar country classification "exporters of manufactures" that includes China, Hong Kong, Rep. of Korea, Singapore, India, Israel, Brazil, Hungary, Poland, Portugal, Romania and Yugoslavia and a few other countries. GATT annually tabulates a list of the 30 leading exporters of manufactures that includes both developed and developing countries. See Table IV.3 in GATT's IntemSnationnLalode .bok, vol. I, various years, for these lists.

20 With _$14.5_ billion in manufactures exports (as defined by IBRD/UNCTAD/GATT) Mexico's teade exceeds that of Argentina, Brazil and Singapore. This would seemingly justify classifying Mexico as a major exporter. However, approximately 70 percent of this trade originates in special plants or areas located near the U.S. border to take advantage of "off-shore assembly" (OAP) provisions in the United States tariff code. These OAP plants import semi-finished or unassembled parts from the U.S. and produce assembled products or manufactures. When these items are re-exported to the U.S., tariffs are not applied to the components produced in the United States. Since the official Mexican trade data exclude these OAP produced items, statistics on this trade must be collected from reported imports by the partner country.
spouse of the head, an older child, or older relative living in the household. In actual implementation of surveys, proxy respondents are not randomly chosen, but are normally selected by interviewers on the basis of availability. In this sense, the experiment did not exactly mimic the actual conditions that result in proxy responses in household surveys. Because we have information about the relationship between the proxy respondent and the individual on whom the proxy responds, we can assess whether there are systematic response patterns that depend on who the proxy is. However, because a typical survey does not generally identify the proxy in the relation to the person for whom the information is collected, we cannot determine what our results imply in terms of potential ‘bias’ of a typical survey due to the use of proxy respondents.<sup>12</sup> The four different survey assignments broadly reflect commonly used approaches in practice. The benchmark reference to which the other survey assignments are compared is the detailed self-report questionnaire. This is generally considered to be the “best practice” approach of household surveys. The use of multiple questions is recommended by ILO especially when some categories of workers (especially casual workers, unpaid family workers, apprentices, women engaged in non-market production, workers remunerated in-kind, etc.) may not be able to correctly interpret a question about “any type of work” as referring to their situation (Hussmanns, Mehran, and Verma 1990).

> 12 An alternative research design to assess the effect of proxy respondents would have been to interview two members of the household who report on their own labor activities and proxy report on the other. We did not implement such a design because it proved to be too difficult to ensure a proper implementation for a medium to large sample. After consultation with counterparts in Tanzania, we concluded that it would be difficult to assure that proxy and self responses would be independent and would remain unaffected by the knowledge that another household member reports on the same information, given the normally social nature of an interview. The specific concern was that the design (and open communication about this design within the village) would trigger either a coordinated response by household pairs and/or accommodation of response to other’s expectations, which would introduce potentially much larger (unobserved) respondent biases.

13
key socio-economic variables, wealth and education (Tables A7 and A8). These estimates give an approximation to the ‘gross effects’ of wealth and mother’s educational attainment. Finally, we relate socio-economic variables and proximate determinants in an attempt to quantify the influence of mother’s educational attainment and income/wealth on proximate determinants. More specifically, we examine the effects on reproductive behavior (short birth interval, Tables A9 and A10) as well as on the use of public health services, more specifically BCG vaccination (Tables A11 and A12). These models are also estimated with a community fixed-effect (Tables A 10 and A12) with the same intention as above. These estimations hence examine the transmission channels through which possible complementarities between MDGs may work.

The subsequent section discusses the estimation method and the results. It also assesses the changes in observables that might explain the observed success or failure in terms of child mortality reduction in the selected countries. After this backward-looking exercise, we illustrate possible complementarities in MDG achievement by simulating the effect of universal primary education of mothers on child mortality – a scenario that is within reach for some of the selected countries in a reasonable period of time.

# _Empirical model_

The core model of the subsequent analyses is a discrete time survival-model. Jenkins (1995) shows that discrete survival models can be estimated based on the application of standard binary dependent variable models to reorganized data. We estimate discrete logistic (proportional odds) model where child survival by month (up to the 60<sup>th</sup> month) is the explained variable. This survival variable is constructed from the birth history section of the DHS. To avoid recall bias we only include births from the last five years preceding the survey. The estimates that link proximate and socio-economic determinants rely on logitmodels since the right-hand side variables, short birth interval, access to safe drinking water and vaccination are coded as dichotomous variables. As noted above, both the survival and the logit models are also estimated with a community-level fixed effect. All the coefficients reported in Tables A4 to A12 are odds ratios. For the child mortality estimates, odds ratios larger than one imply that mortality is more likely than survival. For all estimations, we report robust standard errors that are corrected for clustering at the household level.

To facilitate the interpretation of the results, we quickly would like to illustrate the meaning of the reported odds ratios, as some readers might not be familiar with this type of model. Say a child’s probability to die is 5 percent when the mother does not have primary 26