rafmacalaba/data-use-mentions-extended huggingface dataset:
.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4<br>Bandwidth (Km) Bandwidth (Km) Bandwidth (Km)<br>Mean of Outcome: 0 Mean of Outcome: 4.06 Mean of Outcome: 1.67<br>(g) Cotton Agro-climatic Yield (h) Maize Agro-climatic Yield (i) Wet Rice Agro-climatic Yield<br>.1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2
single dimensional data. This result highlights the importance of data structures for estimating the pass-through. From the macro-financial factors, we also find a statistically significant effect of trade openness—similar to our baseline model, in which the estimated negative coefficient did not reach the common significance levels. The removal of about 200 observations with the median year before 1998 leaves us with a sample that includes the pass-through estimates with median year ranging from 1998 to 2011. During those years, global trade, trade openness, and development of global value chains were on the rise (World Bank, 2017). The latter 21
data set product 3B42RT 3 hour product gives the best results in a basket of 8 near real‐time rainfall products. The 3B42RT daily derived product is what is used in this paper.

To construct a flood proxy from the TRMM data, one has to define when a flood event is happening. In Wu et al. (2012) they propose four runoff based methods to define a flood threshold, and in addition Wu, Adler and Tian, et al. (2014) propose a slightly modified flood threshold definition with a point being flooded when: R�P�� �σ and Q�10m<sup>�</sup> /s where R is the routed runoff in millimeters, P�� is the 95th percentile value and σ is the standard deviation of the routed runoff. Q is the discharge in cubic meters.

As the TRMM data do not provide runoff estimates, only rainfall amount, that will be used as a proxy for flooding in a cell. The modified equation is: Ra�P�� �σ where Ra is the rainfall in millimeters, P�� is the 95th percentile value and σ is the standard deviation of the rainfall.

Following the definition, one has a data set with daily flood data. To use this with the nightlight data it has to be aggregated up to monthly values. In this case, it was chosen to construct two variables as flood proxies, one with values for number of days and the other with values for total rainfall above the flood threshold for the month.

Once the flood data set is finished, a data set with the flood and nightlights data are made, ref Figure 4. With this combined data set, we do a case study of the 2017 floods in Southern Thailand and run the fixed effect regression: L�,� � β� � Σ����� β��� FD�,�,��� � θ� � e�, � , which is the
In order to evaluate the effects of the program, potential beneficiary households (henceforth referred as the “ultra-poor”) were identified by the program in 40 BRAC branch offices covering 1,309 villages. Within every village, BRAC conducted participatory wealth appraisals whereby community members assigned a wealth class for each household: poor, middle or upper class. Following this community appraisal, BRAC program officers identified eligible “ultra-poor” households among the poor.<sup>1</sup> A baseline survey was conducted on all of the ultra-poor households, as well as a representative sample of the rest of the community. Importantly, the survey included questions on informal transfer networks of respondents’ households. Using the baseline village census, the identity of any household with whom the respondent’s household interacted within the was recorded. After the baseline the was village survey, program randomly introduced in 20 branch offices in 2007, and the other 20 remained as controls until 2011. The same sample of households were re-surveyed in 2009 and 2011, after which the control communities were also treated. An additional followup survey was conducted in 2014, that is seven years after the baseline and three years after the control branches had also been treated, and I will use this data to examine long-run trends of outcomes for early versus late treated ultra-poor households.

I find that the program crowded-out informal transfers received by the ultra-poor households in treatment villages, but this was driven mainly by outside-village transfers. The ultra-poor in treatment villages were 13 percentage points (ppt) less likely to have received informal transfers (during the 12 months preceding the survey) relative to the control group, while the effect on the intensive margin is imprecisely estimated. When I disaggregate the transfers by location, I find that they were 4ppt less likely to have received transfers from within their village but the value of transfers received were 29% higher relative to the control group. Moreover, they were more likely to report receiving food transfers from other households within the village. On the other
<!-- Start of picture text --> Direct Transfers Indirect Transfers<br>30.0 30.0<br>°<br>25.0 25.0<br>20.0 20.0<br>8 16.5<br>15.0 15.0 12.8<br>10.2 12 113 1s 191 12<br>10.0 ° e 10.0<br>+- 8.2 8.6<br>6.1 5 55 6.2<br>4.5 47 5.5<br>5.0 A 5.0<br>0.0 e 0.0 :<br>“5.0 -5.0<br>TIK GEO KYG ARM RUS MDA BLR UKR ARM GEO KYG TJIK RUS UKR MDA BLR<br>% of GDP @ Gini index (pp) @ Poverty headcount (pp) =%ofGDP @Giniindex(pp) —_@ Poverty headcount (pp)<br><!-- End of picture text -->
Our approach allows us to estimate the extent to which an observed change in a dimension—or category of dimensions—can explain the observed change in the multidimensional headcount ratio. In the presence of panel data, it allows doing this without separately tracking the transitions in and out of poverty of each individual. In repeated cross-section data—when such tracking is impossible—the approach constitutes a good approximation of how much each dimension contributes to the headcount’s change. In both cases, it summarizes separate information on the headcount by dimension, weights and incidence.

Our paper contributes to the literature on the decomposition of poverty measures, providing a guideline to split up the changes of the multidimensional poverty 3
Policy Research Working Paper 9499

# **Abstract**

This study looks at the redistributive effects of fiscal policy —in particular of direct taxation and expenditures—in Uruguay. This fiscal incidence analysis applies a widely recognized methodology to household survey data and government data for fiscal year 2017 and compares the results with the policies seen in 2009 to study the evolution of the distributional impacts of fiscal policy in the country. The study finds evidence that Uruguayan fiscal policy continues to reduce inequality, with government expenditures in the form of in-kind transfers leading to the largest decreases. While expenditures in basic education are benefitting the poorest, expenditures in tertiary education remain largely regressive. The personal income tax is found to be largely progressive, with the top quintile paying more than fourfifths of this tax. Uruguay’s fiscal policies also lead to a reduction in poverty, mainly due to well-targeted direct transfers.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at glaraibarra@worldbank.org, marisa.bucheli@cienciassociales.edu.uy, and dtuzmanfernandez@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
de la Seguridad Social_ ) and FRL ( _Fondo de Reconversión Laboral_ ).

IRPF and IASS constitute personal income taxes. IRPF is applied to labor and income, while IASS applies to contributory pensions. Both were designed to achieve an equalizing effect. Altogether they represented 3.4 percent of GDP in 2017. IRPF taxes are also applied to capital income. Labor is taxed at marginal progressive rates (the highest one was 36% in 2017) and allows deductions based on contributions to the social security system, children, rent and mortgages. Capital income is taxed at a flat rate, but different rates are considered for different capital concepts, ranging from 3 percent to 12 percent. The IASS taxes contributory pensions at progressive rates and does not allow deductions. Finally, the purpose of FRL is to support a fund for programs aimed at enhancing labor productivity. In 2017, FRL was applied through a tax on labor income at a rate of 0.125 percent, paid by employees (including self-employed), plus a rate of 0.125 percent paid by 5 GDP was estimated at 56.157 billion USD, equivalent to 16,245 USD per capita in 2017 (WDI accessed on June 27, 2019).

6 Indirect taxes such as the value added tax can be typically observed in household consumption expenditure surveys. In Uruguay, the latest survey of this kind was conducted in 2016, but the microdata and reports have not been made public. Similarly, property taxes are not available. Corporate taxes cannot be directly imputed into households and thus are not part of the fiscal incidence analysis. Finally, contributions to the social security system are not analyzed separately because behavioral responses to changes would be strong, and these cannot be accurately captured within the current accounting approach. Additionally, contributory pensions represent a large portion of government revenues, and any adjustment would also lead to changes in macroeconomic and public accounts, which are not considered here.

4
Table 4 Per Capita Expenditure on Subsidized Products, in US$ at PPP/year

|||**Ene**<br>|**rgy**<br>||||**Food**<br>||
|---|---|---|---|---|---|---|---|---|
||LPG|Electricity|Gasoline|Diesel|Flour|Bread|Sugar|Vegetable oil|
|Djibouti|1.8|95.1|36.9|n.a.|35.8|n.a.|51|29.2|
|Iran, Islamic Rep|10.6|83|102.8|0.6|12.6|163.7|n.a.|n.a.|
|Libya|4.4|26.4|26.8|0.5|9|30.1|17.9|46.6|
|Morocco|42.6|114.9|19.9|26.6|56.7|n.a.|26.8|n.a.|
|Tunisia|38.9|108.1|84.7|10.3|n.a.|n.a.|n.a.|n.a.|
|Average across countries|19.66|85.5|54.22|9.5|28.525|96.9|31.9|37.9|

_Source:_ World Bank estimations from Household Budget Surveys.

The results on the distribution of subsidies across quintiles are very different depending on the product and the country (Figure 1). Consider LPG first. In one country, the Islamic Republic of Iran, subsidies on LPG are progressive, meaning that poorer households
benefits vary according to household composition and monthly per capita income. The basic benefit was raised from R$50 to R$54 per household in July 2008. The BPC is a means-tested old-age non-contributory pension and disability grant program. It transfers a benefit equivalent to the minimum wage to elderly and disabled individuals whose household income is less than one quarter of the minimum wage. Its value was raised by 10% in March 2008, in line with the increase in the national minimum wage. We used these increase values to compute for each household that declared receiving either one of these benefits in the 2006 PNAD.<sup>7</sup> The transfer income effect was set to zero otherwise.

> 6 “Minister Patrus Ananias (Social Development) said on Wednesday that the average Bolsa Família benefit will rise from R$78.70 to R$80.00. He added that the 8% value of the increase [in the basic benefit] was determined on the basis of the INPC (Índice Nacional de Preços ao Consumidor), _with the objective of improving the purchasing power of low-income families in the midst of the world food crisis_ ” (Folha de São Paulo newspaper, online, 25 June 2008, our translation and emphasis).

> 7 Subsequent to these increases in the transfer amounts, the programs were also expanded on the extensive margin, to cover additional households. These effects are not captured by the analysis.
**Table 3. Poverty and vulnerability rates for different group in 2016**

|||Vulnerability (%)|Poverty (%)|Ratio of<br>vulnerability to<br>poverty|
|---|---|---|---|---|
|Regions|Tigray|32|27|1.2|
||Afar|44|24|1.9|
||Amhara|28|26|1.1|
||Oromia|40|24|1.7|
||Somali|60|22|2.7|
||Benishangul-Gumuz|36|26|1.4|
||SNNP|46|21|2.2|
||Gambella|42|23|1.8|
||Harari|28|7|4.1|
||Addis Ababa|9|17|0.6|
||Dire Dawa|36|16|2.3|
|Ecological|Moisture-reliable highlands|36|23|1.5|
|zones|Drought-prone highlands|31|21|1.5|
||Moisture-reliable lowlands|38|25|1.5|
||Drought-prone lowlands|56|27|2.1|
|Urban/rural|Urban|17|15|1.1|
||Rural|43|25|1.7|
|National||38|23|1.6|

Source: Author calculations based on HCES/WMS, 2016.

# **Sources and types of vulnerability**

As discussed above, vulnerability to poverty can be either poverty-induced (structural) which is driven by low average consumption of households (lower than the poverty line) or risk-induced (transitory) which is due to high consumption variability/volatility. The first two columns of Table 4 report the percentage of households that are poverty vulnerable and risk vulnerable, respectively. The results show that in
and Kermani (2012), and Pradhan et al. (2016). Finally, one last set of papers argues that development of the insurance sector and economic growth are independent from each other (Pan and Wei, 2012).

From the point of view of an individual, several factors may drive demand for insurance. These include utility maximization and risk aversion (Yaari 1965; and Hakansson, 1969), income and wealth, subjective discount factors, insurance premium rates (Outreville, 1985), human capital endowment (Szpiro and Outreville, 1988), and religion (Browne and Kim, 1993). These theories usually assume perfect capital markets, intergenerational distribution of income in the presence of population growth, and universal access to savings products. Despite the perhaps limited applicability of these theories to developing countries, Outreville (1990, 1996) tries to identify the link between an economy's financial development and insurance market development. Using insurance premium income data from 48 developing countries compiled by UNCTAD, Outreville (1996) sets up an empirical model of life insurance demand that depends on GDP per capita, anticipated inflation, real interest rates, and life expectancy at birth as a proxy for the price of insurance. Demand for insurance also depends on whether the market is monopolistic, whether foreign insurance companies are present, and on the level of financial development.<sup>3</sup> He finds an income elasticity of insurance demand that is positive and provides regression coefficients for a number of other explanatory variables, which are not straightforward to generalize given the limited amount of insurance premium data available.

Outreville’s results are similar to those of Browne and Kim (1993) who conclude saying that “As more data become available, an analysis of insurance consumption in a wider sample of countries would likely lead to a greater understanding of insurance demand. Further, a time series study of demand could potentially lead to a greater knowledge of the growth and maturation of insurance markets.” In addition, empirical and
they from? What draws them to Africa? What impact do they bring to the countries where they invest?

Answers to these questions are difficult as long as data on _private_ Chinese investment overseas, both quantitative and qualitative, are lacking. As it stands, consistent OFDI statistics tracking the role of the private sector, from either China’s official sources or international organizations, have not been available, while information from African host countries are typically anecdotal and scattered.

As a first attempt to fill this information gap, a pilot research project sponsored by the World Bank’s research department was conducted in the spring of 2012, with a view toward exploring the extent and impact of Chinese private investment in Africa. The study took three steps to accomplish its goal: a) gathering and synthesizing the data available from China, the home country; b) gathering official data from selected African host countries; and c) conducting company interviews at selected sites in both home and host countries to gain insight into their respective perceptions and experience. The data unearthed through these efforts were crosschecked and analyzed, leading to a number of findings that shed light on this important but still largely unknown phenomenon.

# I. <u>Findings based on data from China, the home country</u>

The research started with China’s Ministry of Commerce (MOC), the authentic official source of data on China’s foreign trade and investment. Other sources, including China’s Council for Promoting International Trade (CCPIT), some coastal provincial departments of foreign trade and investment and business associations, were also consulted with for additional information.

The most useful data come from a comprehensive database containing 1,586 Chinese investment projects active in Sub-Saharan Africa by the end of 2011, made available by MOC based upon its approvals for Chinese outbound investment projects.<sup>8</sup> The database contains the following information: name of the investing company, origin province of the company, destination countries, activities to be
Figure 2 provides preliminary information on the scope of the financial sector crisis in the seven countries by providing information on the peak level of non-performing loans in the financial system. Since the level of non-performing loans is a reflection of the performance of the real sector, this number can also be used as a rough proxy for the extent of corporate distress. Spain and the US were the only countries in the sample, where the extent of nonperforming loans in the system remained limited, i. e. below ten percent as a share of gross loans. In the Philippines and Finland, official NPLs reached substantial proportion accounting for over or close to 20 percent of financial system assets.'<sup>7</sup> In Ghana, more than half of banking system loans were non-performing. In the Mexican case, it should be noted that official numbers recorded the level of NPLs at around 11 percent of banking system loans. However, non-performing assets transferred to Fobaproa amounted to 23 percent of financial system loans or 17 percent of financial system assets at end 1996.

**Figure 2 Magnitude of Crisis and Resolution Costs**

<!-- Start of picture text --> (in percent of GDP) 1<br>Finland . ..--.<br>Ghana<br>Mexico<br>Philippines<br>Spain<br>Sw eden<br>USA I,<br>0 25 50 75 100 125 150<br>l Resoluton cost * Totalclairrs 0 Nonefonrningloams<br><!-- End of picture text --> _Source:_ Caprio and Klingebiel (1999) and IMF, International Financial Statistics.

# **1. Cross Country Experience with Rapid Asset Disposition Agencies'**<sup>**8**</sup>

Table 1 provides an overview of the main characteristics of the four country examples with rapid asset disposition vehicles. Two of the agencies-the Mexican and the Spanish AMCs-were housed in an existing public agency, the
# Appendix 3: Sampling and survey design

## Catchment areas

The project implementation unit of the Health Services Improvement Project in the Tajikistan Ministry of Health and Social Protection provided the research team with the lists of rural health centers and the villages in their catchment areas. Few RHCs were randomly excluded from the sample when the total number of RHCs was not divisible by three. The study was set up to also evaluate the impact of two additional intervention, collaborative quality improvement and citizen scorecard, that were randomly introduced at the RHC level. Therefore, having the number of RHCs in each district divisible by three was desirable. The effects of these interventions are not discussed in this paper. The exposure to these additional interventions is balance across PBF and control districts and all regressions in our analysis include controls for exposure to these interventions.

In total, this selection process resulted in a sample of 216 RHCs/ catchment areas. There were 108 chosen catchment areas in the seven PBF districts and 108 areas in the nine control districts. In each treatment group, 33 areas were in the Sughd region and 75 in Khatlon.

Six catchment areas were removed from the follow-up survey because they were either closed for renovation, closed or downgraded to health house level. The catchment areas were removed from the analysis sample.

## Health Facility Assessments

A detailed facility assessment was conducted in each selected rural health center. In catchment areas where there was at least one health house, a single health house was randomly selected for an abridged facility assessment. If the catchment area had more than one health house, all health houses had equal probability of being selected.

In each facility, interviews with health workers providing maternal and child health services or hypertension-related services were conducted. Four health workers were interviewed in each rural health center and 2 health workers in each health house.

Direct clinical observation of consultation of children under-5 and adults 40 and above were conducted in rural health centers. In the baseline survey, the target set for the survey teams was to observe 5
>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Imports: 'low tech' products Imports:'medium to high tech' products<br>Exports: 'low tech' products Exports:'medium to high tech' products<br><!-- End of picture text --> Source: Computations based on EU25 data from UN COMTRADE Statistics.

Hence, since around 1998 there has been a very strong shift towards products characterized by medium to high technology processes. Since exports of resource intensive products expanded broadly in line with total exports, the growth in the weight of medium to high tech products has been at the expense of low technology products. On the import side, the composition has been stable with medium to high technology products accounting for most of Turkey’s imports from the EU.

EU markets have played a critical role in transformation of Turkish export basket. Table 11 listing 20 top sectors, identified in terms of four-digit SITC products, ranked according to the value of exports to the EU (Table 11) provides a good illustration of it.<sup>9</sup> These sectors drove the 9 Sectors successful in global markets have also been successful in EU markets. Annex Table 1 provides the list of top twenty sectors in terms of the size of their worldwide exports. It is the same for total exports and EU-destined exports.