rafmacalaba/datause-encoder-data huggingface dataset:
examining the robustness of trends in poverty to alternative weighting, identification and aggregation choices in the construction of multidimensional poverty measures.

In particular, the paper presents estimates for three alternative weighting schemes for different dimensions, and using three alternative measures of multidimensional poverty. The weighting schemes range from uniform weights broadly following the approach of global multidimensional poverty indices (MPI) produced by the UNDP, to weights based on inverse incidence of different deprivations and those derived from the estimated relationship of deprivations to a survey-based measure of subjective welfare. The multidimensional poverty measures similarly range from the “dual cut-off” indices analogous to UNDP’s global MPI, to “union-based” indices that count all deprivations, to indices that are also responsive to the distribution of deprivations.

The main finding of the paper is that there is evidence of a significant decline in multidimensional poverty that is robust to these alternatives, although the magnitude of decline differs across the alternatives. The reduction in multidimensional poverty tends to be largest for subjective welfare weights and for union-based distribution-sensitive measures. The results thus corroborate and supplement the findings in Balisacan (2015). Notwithstanding the absence of a significant trend in income poverty, the period 2004-13 has witnessed improvement in several non-income dimensions of poverty in the Philippines. An examination of the factors underlying different trends in income and multidimensional poverty however remains a topic for further research.

The paper also finds that the contributions of different dimensions to aggregate multidimensional poverty can vary enormously with the choice of the weighting scheme and the aggregate poverty measure. Dimensional decompositions of multidimensional poverty such as those presented in this paper alert us to particular aspects of the judgments implicit in these choices, and thus offer further input into making these choices.

16
# The Distributional Impact of Taxes and Social Spending in Bhutan: An Application with Limited Income Data

Juan Pablo Baquero, Jia Gao, and Yeon Soo Kim<sup>1</sup> Keywords: distributional impact, fiscal policy, fiscal incidence, poverty, inequality, taxes, Bhutan JEL classification codes: H22, I38, D31

> 1 All authors are with the World Bank. The authors are thankful for guidance provided by Gabriela Inchauste (Lead Economist, World Bank). Corresponding author: Yeon Soo Kim ykim8@worldbank.org.
years 5 to 8 of formal schooling, young and adult education, and education for indigenous populations.

**_Textbooks._** Includes a continuous assessment of existing textbooks ( _Avaliação do Livro Didático_ ) for the first four-year series of the primary education cycle and a program ( _Programa Nacional do Livro Didático_ , PNLD) of free distribution of primary education textbooks for Portuguese, mathematics, science, social studies, history, and geography.

**_Libraries._** The National School Library Program ( _Programa Nacional Biblioteca da Escola_ , PNBE), introduced in 1997, distributes reference and literature books to schools, with complementary teaching and library administration materials. A total of 8 million books have been distributed to 56,000 schools.

**_Evaluation of Results._** _Sistema Nacional de Avaliação da Educação Básica_ (SAEB) evaluates educational outcomes at the end of the schooling cycles (4<sup>th</sup> , 8<sup>th</sup> , and 11<sup>th</sup> years). Surveys for 1995, 1997, and 1999 have been completed.

**_Eradication of Child Labor._** The primarily rural _Programa de Erradicação do Trabalho Infantil_ (PETI), managed by the Social Security and Social Assistance Ministries, provides transfers to local schools per child in the program, and income to selected poor families ( _Bolsa Criança Cidadã_ ). The program is aimed at families with an income below one half the minimum wage with children of school age (7 to 14).

**_Student Health._** The Student Health National Program focuses on ensuring good sight and hearing. It includes a sight rehabilitation campaign ( _Olho no Olho_ and _Quem Ouve Bem, Aprende Melhor!_ ), distribution of training and evaluation materials, medical treatment to students with sight problems, and distribution of glasses and hearing aids. The programs are funded with FNDE and Ministry of Health resources.

**_Student Nutrition._** Introduced in 1955, the well-known school lunch program _merenda escolar_ (or _Programa Nacional de Alimentação Escolar)_ is aimed at providing 15 percent of students’ daily caloric and protein needs. It is funded by FNDE.

**_Student Transportation._** FNDE finances municipal acquisition of vehicles for transporting students that live far away from schools.

**_Education Statistics._** A national school census is conducted every year. It comprises three surveys—of school education, university education, and education financing. In addition, a census of teachers was conducted in 1997.

**_Secondary Education._** Benefits from federal programs to improve access and quality, including the School for Youth Project ( _Projeto Escola Jovem_ ), which seeks improvements in curricula, teacher training, libraries, and laboratories, distance education, expansion of capacity, and administration of school systems. The project is financed by the IDB with cofinancing from the federal and state

28
|Dependent variable<br>Log number of exported products<br>Log export sales<br>(1)<br>(2)<br>(3)<br>(4)<br>Labor productivity_f,t−_1<br>0.061*<br>0.051<br>0.144***<br>0.128***<br>(0.034)<br>(0.034)<br>(0.034)<br>(0.034)<br>Size_f,t−_1<br>0.115**<br>0.111**<br>0.249***<br>0.242***<br>(0.054)<br>(0.053)<br>(0.065)<br>(0.063)<br>Tariff_fj,t−_1<br>-0.077<br>-0.664**<br>(0.222)<br>(0.284)<br>Tariff_fj,t−_1_×_China_j_<br>-2.482***<br>-3.980***<br>(0.548)<br>(0.900)<br>Extensive margin competition_fjt_(N_◦_countries)<br>1.006***<br>1.441***<br>(0.131)<br>(0.159)<br>Extensive margin competition_fjt_(N_◦_frms)<br>1.069***<br>1.799***<br>(0.068)<br>(0.095)<br>Intensive margin competition_fjt_<br>-0.094***<br>-0.060***<br>(0.008)<br>(0.011)<br>(0.220)<br>(0.293)<br>Country level controls<br>Yes<br>Yes<br>Yes<br>Yes<br>Firm fxed effects<br>Yes<br>Yes<br>Yes<br>Yes<br>Country fxed effects<br>Yes<br>Yes<br>Yes<br>Yes<br>Year fxed effects<br>Yes<br>Yes<br>Yes<br>Yes<br>Observations<br>17,241<br>17,241<br>17,241<br>17,241<br>_R_2<br>0.511<br>0.536<br>0.529<br>0.565<br>_Notes_: The regressions are OLS estimations of equation (3) for the period 1999-2005. In columns (1) and (2) the dependent variable is the logarithm of the number of exported products to country_j_ by frm_f_ in year_t_, and<br>in columns (3) and (4) the dependent variable is the logarithm of the export sales in country_j_ by frm_f_ in year_t_. Country level control variables defnitions are reported in table 4. Firm size _ft_ represents the logarithm<br>of employment at the frm level and frm labor productivity_ft_ is measured by value added per worker. Heteroskedasticity-robust standard errors clustered by frm-country pairs are reported in parentheses. _∗∗∗, ∗∗,_and_∗_<br>indicate signifcance at the 1, 5 and 10 percent levels respectively.|
|---|

28
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the foreign-owned sector is especially high with three quarters of migrant workers being women. In 2004, the proportion of individuals who were unemployed (defined as “those who are not working but sought employment”) is relatively low for both migrants and non-migrants registering 0.9 percent and 1.1 percent respectively. For migrants, the proportion of those who were unemployed in origin areas<sup>20</sup> is 2.6 percent, which is a much higher rate than the average proportion of the unemployed after migration.

The average monthly earnings reported in the VMS,<sup>21</sup> are 1.25 million dong, 1.11 million dong and .91 million dong for “State”, “Private” and “Foreign” workers respectively,<sup>22</sup> whereas the migrants who went to the Central Highlands earned .50 million dong on average. Although the income levels of migrants before migration are not reported in the VMS _,_ the survey questionnaire includes a question about migrants’ self-assessments of the change in income before vs. after migration. Table 1 demonstrates that approximately 84.7 percent of migrants feel that their incomes are better whereas few migrants (3.5 percent) reported that their incomes are worse. For those who work for foreign firms, the proportion of migrants who responded that their incomes are better is slightly higher than the average of migrants at 87.9 percent. Migrants who went to the Central Highlands did not improve their incomes as much: the proportion of those who feel that their incomes are better is lower than average at around 74.1 percent whereas that of those who think that their incomes are worse is higher than average (9.7 percent).

> 20 In the survey, each migrant is asked about the main activity in the last six months before migrating. The proportion of those who responded as “unemployed and looking for work” is defined as the proportion of unemployed in the origin area.

> 21 The actual income figures are not used in the empirical analyses since the income differentials in the model are computed based on expectations. Comparing actual wages and predicted wages, it turns out that the Mincerian wage regressions in the empirical section predict migrants’ actual incomes reasonably well.

> 22 The lower wages amongst the workers in foreign firms relative to the other sectors may reflect partly their younger age, the higher proportion of females who work in this sector and the low-wage industries for which they work such as clothing and footwear manufacture (Fukase, 2013a).
The first column in Table 4 performs the decomposition on the full sample (11,321 women in the 2005 survey and 13,671 women in the 2010 survey) and includes female labor market participation. Overall, about one-third (0.084 of 0.26) of the change in cumulative fertility between 2005 and 2010 can be explained by the changes in covariates. The remainder is left unexplained (or “explained” by changes in coefficients between 2005 and 2010). This mirrors Canning’s (2011) argument that while changes in household-level characteristics do affect fertility, changes in fertility behavior are more like changes in social norms than changes in individual decision-making characteristics. The decomposition results show that the single biggest part of the drop in fertility is due to increased female education levels. The increase in the average number of years of schooling completed accounts for 70 percent of the explained portion of the fertility drop and 22.7 percent of the overall fertility drop. This is in line with Schultz’ (1994) argument that the largest part of the fertility decline in African countries since the 1980s can be explained by changes in women’s schooling. The increase in household living standards, measured by the score on the asset index, emerges as the second main contributor to the fertility decline, accounting for slightly over 15 percent of the fertility decline.

The increased participation of women in the labor market has also been significantly associated with the decrease in average cumulative fertility. The magnitude of the association is however rather modest: The increased proportion of women working explains only 5.4 percent of the overall decline in fertility. This can likely in part be explained by the types of work most women in Rwanda are engaged in (agricultural work on the family farm and employment in household businesses), which are more compatible with family responsibilities than modern non-farm wage employment (Mammen and Paxson, 2000). Contrary to intuition, the decrease in the percentage of women not exposed to mass media is associated with an increase in fertility. This is probably the result of endogeneity. As shown in Table A1, the increase in exposure to mass media was highest among the older women, who also have the highest cumulative fertility. In addition, it is likely that women with a 19
**Table 10: Average leverage by sector, balanced data (%)**

||**Leverage 1 – M**|**arket based**|**Leverage 2 – Acc**|**ounting based**|
|---|---|---|---|---|
|**Year**|**Infrastructure**|**Services**|**Infrastructure**|**Services**|
|1997|45.1|52.8|46.9|51.6|
|1998|41.1|60.6|46.4|62.3|
|1999|41.1|54.7|39.5|64.9|
|2000|49.9|47.4|30.6|64.3|
|2001|47.4|53.9|31.3|56.0|
|2002|29.7|56.1|17.2|59.4|

Source: Datastream and authors’ calculations.

The main messages arising from Table 10 can be summarized as follows. First, both the Asian crisis, and, to a lesser extent, the 2000 stock market bust did have an initial impact on the leverage of these companies. However, it appears that this impact was short–lived, and, at least in terms of the market–valued leverage rate, has returned to its initial level. Second, up to 2001 there seems to have been a negative correlation between leverage in infrastructure and service transport companies. Indeed, they seem to have reacted with opposite signs to the Asia crisis. The transport service companies have seen their leverage increase, while the leverage of transport infrastructure operators has declined significantly since 1997. While there is no simple explanation to this fact, it may be reasonable to assume that the infrastructure sector is likely to be slower to react to shocks since their investments tend to be heavier and start from stronger initial equity commitments. These commitments tend to be more effective at reflecting risks and hence require fewer adjustments. Also, large investment commitments and hence borrowing requirements in the sector are easier to spread out over time than they may be for services.

Overall, the transport services sector continues to be characterized by a relatively high level of leverage, measured by both leverage(1) and leverage(2). Both measures point toward an average leverage of 55–60%. Also, the trends of the two measures appear to be very similar over this period. As for transport infrastructure, the two measures of leverage appear to behave quite differently over time. Leverage(1), based on the market value of equity, has increased over the period and remained above 40% up until 2001. A significant drop to 30% is observed in 2002. While the book–valued average leverage(2) starts at a similar level as the market–valued leverage(1) in 1997, it has decreased more quickly, standing at 17% in 2002.

# **5. Summary and policy implications**

The evidence presented in this paper seems to confirm the anecdotal evidence: debt is replacing equity in the financing of the investment needs of utilities and transport services in developing countries. Even if this observation is not obvious from the leverage calculated from book values of equity, it is quite strong when considering the market valuation of equity over the past 10 years. Moreover, the results presented here point toward an acceleration of 14
<!-- Start of picture text --> 65<br>Chart 5a: Mathematics: Test score gap at percentiles between<br>students in large vs. small communities - 2003 vs. 2007<br>55<br>L 45<br>o<br>c 35<br>a<br>t 25 Math-2007<br>i<br>Math-2003<br>o 15<br>n<br>5<br>G<br>a<br>-5 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95<br>p<br>Percentile<br>40<br>Chart 5b: Mathematics: Characteristics component of the test<br>35 score gap between students in large vs. small communities -<br>2003 vs. 2007<br>30<br>L<br>o 25<br>c<br>a 20<br>t Math-2007<br>15<br>i Math-2003<br>o 10<br>n<br>5<br>G<br>0<br>a<br>5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95<br>p Percentile<br><!-- End of picture text --> 19
the differences in factor endowments between the 1992 and 1982, Gehlhar was able to provide reasonably accurate projections of trade shares in 1982. However, Gehlhar found that introducing a human capital factor was crucial to explain changes in trade shares, implying a need to add this factor to the standard model before using it for projections. This is in line with a number of growth regressions, where human capital is found to be a critical determinant of economic growth (e.g. Barro and Lee, 1993; Barro and Sala-i-Martin, 1995; Levine and Renelt, 1992).

For the analysis on long term changes in trade patterns, such as these projections, Gehlhar also found that the results were improved with increases in the Armington elasticities of substitution. Because of the results of this model validation exercise, _elasticities of substitution twice as high as the standard GTAP elasticities were therefore used in the projection experiment reported in this paper._ In the liberalization experiments the standard elasticities were used.

# **3. Base Case Projections**

**The** projected values of exogenous variables used in generating the baseline simulations are presented in table I (see Hertel _et al.,_ 1995 for more details). These were based on combinations of historical data and projections of the growth in population, in the labor force, in real GDP and in investment obtained from World Bank sources. Capital stock projections were generated by adding investment in each year and subtracting depreciation using the methodology of Nehru and Dhareshwar (1994). The human capital projections were based simply on the growth in the stock of tertiary education in each country during the 1980-87 period (Nehru, Swanson and Dubey 1994). The stock of agricultural land was held constant throughout the analysis. Finally, the rates of sector and factor neutral total factor productivity (TFP) growth rates for each of the 15 regions are obtained as the residual from a simulation with exogenous GDP growth and factor accumulation. The TFP growth rates are constant across all non-agricultural sectors within a region, while the TFP growth rates in the agricultural sector in all regions were set to 0.7 percent/year above the average rate for the economy as a whole (Gehlhar _et al.,_ 1994).

From the cumulative growth projections in table 1, it is clear that there are substantial differences between the developed and the developing countries in their rates of factor accumulation. While population growth rates are sharply higher in all of the developing country 5
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Policy Research Working Paper 6861

# **Abstract**

Up to now catastrophic and impoverishing payments have been seen as two alternative approaches to measuring financial protection in health. Building on the previous literature, the authors propose a unified methodology in which impoverishing and catastrophic payments are mutually exclusive outcomes. They achieve this by expressing out-of-pocket payments as a ratio of ‘discretionary’ consumption, defined as the amount by which total consumption (gross of outof-pocket payments) exceeds the poverty line. This allows the authors to identify both households who are impoverished by out-of-pocket payments (their ratio exceeds one) and households who are pushed even further into poverty by out-of-pocket payments (their ratio is negative); the authors call such payments ‘immiserizing’. Households experiencing ‘catastrophic’ payments are a subset of those who incur out-of-pocket payments but who are neither impoverished nor immiserized by them. Two alternative definitions of catastrophic payments are offered: those that absorb more than a pre-specified fraction of discretionary consumption; and those that leave a household’s nonmedical consumption (total consumption net of out-of-pocket spending) below a pre-specified multiple of the poverty line. The authors also offer a simple financial protection index that reflects the percentages of households incurring immiserizing, impoverishing, catastrophic, non-catastrophic, and zero out-of-pocket payments. They illustrate their unified approach with data from the World Health Survey, using international poverty lines and a catastrophic payment threshold of 40 percent.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at awagstaff@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team