rafmacalaba/datause-encoder-data huggingface dataset:
concern when, as is common, other taxes like VAT are imposed on the full value, an excise that does not tax features such as packaging or convenience of availability may lead consumers to demand and producers to supply more such untaxed amenities, which seems unlikely to be a sensible use of scarce resources in developing countries.

_Ad valorem_ taxes, although less adversely affected by inflation and often imposed for distributional reasons (as discussed below), have their own problems. Since such taxes are more complicated to administer, sometimes administratively‐determined prices are used as the tax base thus converting the _ad valorem_ tax to a specific tax. In addition, _ad valorem_ taxes favor cheaper products, which may be considered undesirable if, for example, lower prices mean lower quality and hence perhaps more health or other risks. Moreover, _ad valorem_ taxes in effect introduce a multiplier effect in the price of the good. For a producer to increase the net‐of‐tax price by a given amount, the price charged to the consumer must be increased by 1/(1‐t) where t is the _ad valorem_ tax rate charged on the good. This multiplier effect may perhaps discourage improvements in the quality of the taxed good since the producer must increase prices by more than the value of an improvement in order to break even on the investment. On the other hand, if producers lower prices more when taxes are imposed in _ad valorem_ form, presumably consumption would be increased with harmful health effects particularly if lower‐ income consumers have a higher price‐elasticity of demand. In this case, however, as World Bank (2014) suggests may be the case in the Philippines and Bosch and Koch (2014) show for South Africa, it is not impossible that increasing taxes and hence prices will discourage lower‐income consumers most and hence end up reducing the regressivity of the tax as a whole. However, the distributional issue is not that simple. Not only is the incidence of excise taxes of little direct interest to those who do not consume the taxed products, but even for smokers and drinkers excise taxes on tobacco and alcohol are most regressive when assessed in terms of current incomes, much less so in terms of spending, and even less so when viewed from a lifetime perspective. Indeed, in the case of the UK, spending on alcohol as a percent of total spending is actually progressive for the first four expenditure quintiles (Crawford, Smith and Keen 2010).

As with setting the appropriate level of excise taxes, the main lesson one learns from examining the empirical evidence on the differential effects on prices, consumption, and health of specific and _ad valorem_ taxes is that the evidence supporting most proposed or actual differentiations is neither strong nor easy to interpret from a policy perspective. In practice, so little is certain about tax effects on prices and quality even in developed countries that in practice the choice of specific or _ad valorem_ rates 11
|Other Members<br>ChronicallyIll in HH|-0.031*|-0.086***|-0.027|-0.085**|-0.031|-0.081|
|---|---|---|---|---|---|---|
||(0.018)|(0.034)|(0.022)|(0.040)|(0.031)|(0.062)|
|Substitute for collected<br>fuelwood|-0.536***||-0.472***||-0.732***||
||(0.041)||(0.047)||(0.088)||
|Constant|-0.596***|1.674***|-0.562***|1.848***|-1.031***|2.145***|
||(0.145)|(0.383)|(0.185)|(0.485)|(0.247)|(0.736)|
|Observations|13281|13281|8690|8690|4591|4591|
|||-0.244||-0.135||-0.688*|
|Inverse Mills Ratio||(0.214)||(0.270)||(0.359)|

Standard errors in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% 43
65 Rosenzveig, M.R. and Volpin, K.I. "Governmental Interventions and Household Behavior in a Developing Country". Journal of Development Economics. 10:209225. 1982.

Scobie, G.M. Food Subsidies in Egypt: Their Impact on Foreign Exchange and Trade, International Food Policy Research Institute. (Washington D.C. 1983) Selowsky, M. "The Economic Dimensions of Malnutrition in young Children". World Bank Staff Working Paper, No. 294.

Selovsky, M. "A Note of Preshcool-age Investment In Human Capital in Developing Countries". Economic Development and Cultural Change. 24:707-720; 1976.

Srinivasan T.N.,. "Malnutrition: Some Measurement and PolicyIssues". Vorld Bank Staff Working Paper No. 373 (Vashington D.C.The World Bank, 1980).

Stigler, G.J. "The Cost of Subsistence". Journal of Farm Economics. 27:303314. May, 1945.

Strauss, J. "Households, Communities and Preschool Children's Nutrition Outcomes: Evidence from Rural Cote D'Ivoire". Mimeo.December 1986.

Vijga, A. "The Nutritional Impact of Food-for-Vork-Programmes: Report of a State-of-the-Art Study". (Vageningen,April 1983).

Williamson-Gray, C. "Food Consumption Parameters for Brazil and Their Application to Food Policy". Research Report 32. International Food Policy Research Institute. (Washington, D. C.,1982).

Villis, R.J. "A nev approach to economic theory of fertility behavior" Journal of Biblical Economy. 81:14-64, 1973.

Yarbrough, C., Lasky, R., Habicht, J-P., and Klein, R.E. "Testing for Mental Development". Symposia of the Swedish Nutrition Foundation XII pp 7-12
- 18 -

to distinguish between economic policy variables and "fundamental" or environmental variables. As used here, environment means the entirety of the factors that are independent of discretionary economic policy choices (or "exogenous" factors in relation to economic policy). This distinction is not the same as that between international and domestic factors: the environmental variables group together not only international factors, such as evolution of terms of trade, but also certain domestic factors, such as climate, dimension, natural resources, etc.. The distinction between international factors and domestic factors, often used in global studies of the impact of North-South relations, is hardly used at all in cross-sectional analysis of factors affecting growth. The main reason is that the purely international factu,rs affect all countries and it is often impossible to differentiate their impact according to country (growth of world demand, world inflation, and so on).

Although the distinction between country-specific environmental variables and economic policy variables is an essential one, it is rarely made explicitly (it is found in Wheeler 1984, Plane 1986, Guillaumont P. and S. 1986 and, to some extent, Genberg and Swoboda 1987).

Thus, the distribution of the variables between these two categories is not obvious. This presents hardly any problem in the case of the environmental variables representing initial conditions, such as initial product per capita, initial population size, initial school enrollment rate, initial structure of production or exports, etc.. While these variables are influenced by economic policy, they are not affected by the policy followed during the period considered.

A problem arises in regard to the environmental variables measured over the ssne period as economic growth. Examples are population growth and instability of exports. These variables are not totally independent of economic policy. Population growth can be affected not only by birth control policy but also, in the shorter term, by migration flows, which depend on economic growth and therefore on economic policy. In that connection, natural population growth is a better environmental variable than total growth; however, it is rarely used (it occurs in Plane 1986). Similarly, while the instability of exports undoubtedly results mainly from international factors (price and demand fluctuations) or climatic hazards, it is also affected in the long term by diversification policy and in the short term by producer pcice policy -- itself linked to monetary and budgetary policy.

Conversely, the economic policy variables are usually influenced by the environment. Thus, in cross-sectional analysis the economic policy instruments used, which vary from country to country, cannot be introduced directly; indicators are then used that result from the use of various instruments and from the environment. Examples are the customary monetary and budgetary policy indicators (e.g. rate of growth of money supply or inflation rate, which depends in part on the
# **5.1 Structural Gravity with Missing Data**

Following on from Section 2, equation (4) admits a structural interpretation of the exporter and importer fixed effects, respectively, for each sector: As defined before, _Yi_<sup>_k_</sup> denotes sectoral output (total sales at end user prices) and _Ej_<sup>_k_de-</sup> notes total sectoral expenditure, while Π<sup>_k_</sup> _i_<sup>and</sup><sup>_P k_</sup> _j_<sup>denoteoutwardandinwardmultilateral</sup> resistances for class _k_ . In practice, the fixed effects are estimated relative to a base country so, for example, _m_<sup>_k_</sup> 0<sup>and</sup><sup>_xk_</sup> 0<sup>arenotestimated,allowingforastandardconstantterm</sup><sup>_χk_.20</sup> For the base country, we assume that _Y_ 0<sup>_k_is observed, from which</sup><sup>_E_</sup> 0<sup>_k_is inferred as ‘apparent</sup> consumption’ deducting exports and adding imports to _Y_ 0<sup>_k_.Anormalizationofthesetof</sup> _P_<sup>_k_</sup> ’s and Π<sup>_k_</sup> ’s is required in any case, so it is natural to choose _P_ 0<sup>_k_=1(seeAndersonand</sup> Yotov, 2010a).<sup>21</sup> Limited data on sectoral output constitutes an important problem, for unfettered use of the structural gravity model requires the full set of output and trade data for all countries. An important contribution of this study is therefore to show how the gravity model can be used to project output information. Our methodology imposes the theoretical identity between the estimated importer and exporter country fixed effects with their structural gravity expressions in order to recover the required information. Fally (2015) shows that the fixed effects estimated with PPML are exactly consistent with the theoretical values from

> 20Structural gravity in theory has a scaling term equal to the inverse of worldwide sales times the mean measurement error in the bilateral trade data, data that are notoriously rife with measurement error. The practice in (5) combines the importer 0 and exporter 0 fixed effects with the worldwide scaling effect. Regression cannot identify both terms because the full set of fixed effects regressors are perfectly collinear when the constant vector is also included. (Perfect collinearity also arises if _x_<sup>_k_</sup> 0<sup>or</sup><sup>_mk_</sup> 0<sup>isattemptedtobe</sup> estimated.)

> 21This normalisation implies _mk_ 0<sup>=</sup><sup>_E_</sup> 0<sup>_k_whilst</sup><sup>_xk_</sup> 0<sup>isidentifiedfrom</sup><sup>_xk_</sup> 0 = _Y_ 0<sup>_k/χk_�</sup> _j_<sup>_τ k_</sup> 0 _j_<sup>_mk_</sup> _j_<sup>.</sup> Then (Π<sup>_k_</sup> 0<sup>)</sup><sup>_σk−_1=1</sup><sup>_/χk_�</sup> _j_<sup>_τ k_</sup> 0 _j_<sup>_mk_</sup> _j_<sup>completestheidentificationofmultilateralresistancesfromobservedandin-</sup> ferred variables.

22
# **Table 3: Panel A – Multinomial Regressions**

Log Odds ratio between Renters and Mortgage holders (Marginal Effects)

||||||||||**Hausman F**|**-**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|**parttime**|**unemp**|**retired**|**urban**|**lage*secedu**|**lage*teredu**|**inc*secedu**|**inc*teredu**|**Statistic**|**Pseudo r**<sup>**2**</sup>|
|_New EU Countries_|||||||||||
|Czech Republic|-0.0277|0.1536 ***|-0.0170|0.0520 ***||0.0167||0.1138 ***|0.95|0.31|
|Estonia|0.0511|-0.0451|0.0896 *|0.0119||-0.0980 *||0.0152|0.82|0.20|
|Hungary|-0.0160|-0.0076|0.0117|0.0617 ***|-0.1125 **|-0.0844|-0.0136|0.0084|1.55 *|0.11|
|Latvia|-0.0035|-0.0086|-0.0399|-0.0460 ***|0.3202 **|0.3600 **|0.0386|0.0716|1.06|0.10|
|Lithuania||0.0018||-0.0013||-0.0692 **||0.0175|0.89|0.23|
|Poland|0.0161|0.1087 **|0.0008|0.2000 ***|0.1446 ***|0.1660 ***|0.0092|0.0200|3.44 ***|0.32|
|Slovak Republic|0.0118|0.0542|0.0267|0.0096|0.0855|0.0136|-0.0718|-0.0736|0.75|0.30|
|Slovenia|0.2500 **|0.2302 ***|0.1853 ***||-0.0661|-0.1653|0.0086|0.0263|1.47 *|0.11|
|_EU-15 and Cyprus_|||||||||||
|Austria|-0.0271|-0.0509|0.0302|0.1659 ***|0.4344|0.2960|0.0106|0.1328|2.45 ***|0.25|
|Belgium|-0.1460 ***|0.0202|-0.0374|0.0456 **|-0.2000 *|-0.3604 ***|0.0588|0.0948|1.88 **|0.34|
|Cyprus|0.0428|0.1871 *|0.1250 *|0.0902 ***|-0.3229 ***|-0.2526 **|-0.0657|-0.0418|1.61 **|0.19|
|Denmark|0.0947 *|-0.0182|0.0969 **|-0.0542 **|0.4869|0.3635|0.6578 **|0.6601 **||0.34|
|Finland|-0.0755 *|0.1002 **|-0.0726 *|0.0717 ***|-0.2186|-0.4100 **|0.0532|0.0700|2.87 ***|0.32|
|France|0.0411|0.2284 ***|0.0172|-0.0053|-0.3743 ***|-0.3626 ***|-0.0956 *|-0.1190 **|2.05 ***|0.32|
|Greece|0.0180|-0.0434|-0.0603 **|0.0371 **|0.1672 ***|0.2712 ***|-0.1006 ***|-0.0456|1.91 ***|0.17|
|Ireland|-0.0661 **|0.0274|-0.0186|0.0850 ***|0.0452|-0.1613|-0.0087|-0.0469|3.05 ***|0.31|
|Italy|0.0388|0.0811 **|-0.0240|0.0679 ***|0.0294|0.0800|-0.0316 **|-0.0535 **|3.38 ***|0.13|
|Luxembourg|-0.1484 **|0.1576|0.0106|-0.0470|-0.0632|-0.0214|0.1089|0.2617 ***|1.09|0.35|
|Netherlands|0.0724 **|0.0651|0.0858 ***||-0.1427|-0.0483|0.0103|0.0648||0.29|
|Portugal|-0.0726|0.0101|-0.0109|0.1293 ***|0.0123|0.1429|-0.0493|-0.0358|2.33 ***|0.16|
|Spain|0.0779 *|0.0187|0.0221|-0.0135|0.0107|0.0540|0.0202|-0.0243|1.88 **|0.14|
|Sweden|-0.1277 ***|0.0284|-0.1287 ***|-0.0394 **|0.1942|0.1972|0.0734|0.0722|1.08|0.31|
|United Kingdom|-0.1915 ***|0.1615 **|0.0158|-0.0096|0.2652 ***|||-0.0285|4.85 ***|0.32|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

Source: EU Statistics on Income and Living Conditions (SILC) database, 2007 4
# **Measuring Poverty in Latin America and the Caribbean: Methodological Considerations When Estimating an Empirical Regional Poverty Line**<sup></sup>

R. Andrés Castañeda<sup></sup> Leonardo Gasparini<sup>§</sup> Santiago Garriga<sup>†</sup> Leonardo R. Lucchetti<sup>‡</sup> Daniel Valderrama<sup>††</sup> _JEL classification:_ I3, I32, D6, E31, F01 _Key words:_ Poverty Measurement, Regional Poverty Lines, Purchasing Power Parity, Latin America and the Caribbean.

> We are grateful to Oscar Calvo-Gonzalez, Liliana Sousa, Maria Ana Lugo, Thiago Scot, Ankush Khullar, participants at the World Bank “Summer Initiative on Research on Poverty Inequality and Gender”, and the Global Poverty Working Group within the World Bank whose suggestions greatly improved earlier drafts of the paper. All remaining errors are ours.

>  World Bank. E-mail: <u>acastanedaa@worldbank.org</u>

> § _Centro de Estudios Distributivos, Laborales, y Sociales_ (CEDLAS) at the _Universidad Nacional de La Plata_ (UNLP). E-mail: <u>gasparinilc@gmail.com</u>

> † Paris School of Economics - _École des hautes études en sciences sociales_ . E-mail: santiago.garriga@psestudent.eu

> ‡ World Bank. E-mail: <u>llucchetti@worldbank.org</u>

> †† World Bank. E-mail: <u>dvalderramagonza@worldbank.org</u>
**Table 7. Percentage of children under 3 who are reported to have had a fever (and fever withoiut** a **cough) in the past two weeks by quintile ranking of household.**

||_Poorest_|_Quintile2 _|_Quintile3 _|_Quintile4 _|_Richest_|_Poorest-_<br>_Richest_|
|---|---|---|---|---|---|---|
|_WesternandCentralAfrica_<br>Anyfever|37.6|39.5|37.7|32.1|27.9|9.7|
|Fever withoutacough|19.1|19.8|18.2|17.2|13.9|5.2|
|_EasternandSouthernAfrica_|||||||
|Anyfever|41.7|42.6|43.5|43.9|36.4|5.3|
|<br>Fever withoutacough|13.4|14.0|14.3|15.5|12.7|0.7|

_Source:_ Author's calculation from pooled and weighted DHS data.

The change does not appear to be uniform across the distribution. In Western and Central Africa the percentage with a fever goes from 38 to 32 percent between the third and fourth quintiles, and in Eastern and Southern Africa it goes from 44 to 36 percent between the fourth and fifth (richest) quintiles. Across the remainder of the distribution the level is almost flat-with a slight increase between the first and second quintile. Because differences in absolute levels of wealth can be small between one quintile and the next, quintiles can distort one's impression of the relationship. Figure 2 shows a nonparametric estimate of the incidence of fever (and fever without a cough) for each value of the wealth index. 1<sup>6</sup> The visual impression of these graphs is that of a weak downward slope suggesting that the quintiles may indeed lead one to understate the differential across the distribution.

> 16. The estimate is a moving average estimate of the percentage who report fever across the wealth distribution and includes about 5 percent of the sample for each estimate (2,501 observations in Western and Central Africa and 2,001 observations in Eastem and Southern Africa). Point estimates are then connected with a cubic spline.

_14_
particular stratum. There were relatively few refusals; two-thirds of the attrition was because a household was away temporarily when the survey team visited the village.

We also initiated qualitative research in purposively selected villages in six districts in north and south Bihar (Gaya, Khaimur, Kishanganj, Muzaffarpur, Purnea and Saharsa) during February and August 2009.<sup>11</sup> The results of this qualitative work will be used in interpreting some of our quantitative findings.

Our method of measuring forgone incomes relies on asking participants themselves what they think they would have done in the absence of the program.<sup>12</sup> If they said that they would have worked, we asked them how many days and at what wage. The specific questions (in the local dialect) were fine-tuned in the piloting stage. As noted in the introduction, our method has the advantage that we obtain individual-specific impacts, incorporating idiosyncratic information on the available opportunities—information that would be unlikely to be available as data in an observational study. We are thus able to implement quite fine distributional analysis, as required for assessing impacts on poverty.

Counterfactual questions are not always easy for respondents. However, this did not appear to be a problem in our case. With appropriate training for interviewers, quite high response rates were obtained. The overall response rates to our questions on forgone income were 92% in the first round of our survey, up to 98% in the second round.<sup>13</sup> In common with most other methods of impact evaluation using micro data, there may be general equilibrium effects that this method cannot hope to identify. In the present context, two different survey respondents may have in mind the same forgone work opportunity, such that aggregate forgone income will be lower than the sum of the individual reports. We will test the sensitivity of our results to the possibility that our methodology has led us to over-estimate forgone earnings.

Certain protocols were established for cleaning and analyzing these data:

> 11 The qualitative results are reported in Development Alternatives (2009), Indian Grameen Services (2009) and Sunai (2009).  Dutta et al. (2013) provide summaries of the findings and their implications.

> 12 Jha et al. (2012) also asked survey respondents working on MGNREGS whether they thought any other work was available. They did not, however, ask for forgone incomes, but instead used prevailing wage rates for the imputation. 13 Exploiting our survey design, we can also confirm the reliability of reported forgone incomes for the main relevant activity, namely casual work; see Dutta et al. (2013).

9
knowledge on these cost components, and overall are 7.9 percentage points more likely to correctly identify the costs of a remittance transaction. The effect size is about half (4.6 percentage points) for the family-only training, and insignificant for the migrant-only training. Despite these differences in magnitude, we cannot reject equality of impacts across the various treatments.

# **3.3 Impact on Real Outcomes: Remittances**

There are two main channels through which financial literacy training might be expected to impact on real outcomes for remaining household members: it may affect how much money they receive in remittances, and it may affect how households use the money that they do receive.

We start first with remittance behavior. Higher financial literacy may change the extent to which remittance transfers take place through the formal financial system. However, in the case of our Indonesian sample, almost all remittances occur through formal channels, and thus column 1 of Table 5 shows no impact on greater formality given this starting point. This is consistent with separate survey evidence from the Bank of Indonesia which has found most remittance transactions tend to occur through formal channels.

Financial literacy training may also change the frequency and amount remitted. Migrants may send more remittances if financial literacy training enables them to manage their money abroad better and have more savings, or if they feel that financial literacy training has improved their receiving household’s financial management. A better understanding of the costs of remitting may reduce the frequency of remitting if migrants bundle transactions into fewer, larger, transfers in order to reduce transaction fees, or may increase the frequency of remitting if financially informed migrants are able to seek out cheaper methods for sending small transactions (Gibson et al, 2006; Aycinena et al, 2011).

Table 5 shows that financial literacy training had no significant impacts on the likelihood or frequency of remitting, or on the amount of remittances received. Moreover, the point estimates on the amount received are negative, suggesting that, if anything, households which took part in training received fewer remittances. These results are consistent with those of Gibson et al. (2012), who survey migrants and also find no impact of financial training on the amount of remittances sent. However, it might still be the case that the existing levels of remittances are used differently by recipient households. We now turn to these compositional 14