rafmacalaba/datause-encoder-data huggingface dataset:
_PROGRESA_ was first implemented during the first half of 1998 in 320 randomly assigned rural villages out of 506 villages pre-qualified for immediate participation.<sup>20</sup> The remaining 186 villages were assigned as control villages and received their first monetary transfers in December 2000. Implementation of the program in a phased manner and random selection of program villages for the first phase of implementation were extremely important factors aiding rigorous evaluations of the program.<sup>21</sup> The baseline survey ( _Encuesta Évaluation de los Hogares_ or ENCEL) for _PROGRESA_ was collected in 1997 in all 506 pre-qualified villages, covering 24,077 households in seven states.<sup>22</sup> _PROGRESA_ administrators ran a follow-up survey over the same set of households once every six months, generating a rich household panel dataset. Many evaluations of _PROGRESA_ were conducted on the basis of these surveys, including the previously mentioned studies by Skoufias and Parker (2001), Parker and Skoufias (2000), Schultz (2004), Todd and Wolpin (2006), Attanasio et al. (2005), and Coady (2004). Overall results show an increase in the enrollment rates of both boys and girls at the primary and secondary levels, a reduction in the incidence of illness among children of age 0-5 years, an increase in the annual growth rate of children of age 12-36 months and a reduction in all poverty indices. _PROGRESA_ (Opportunidades) is now covering around 5 million households (18 percent of the country’s total population) with a budget equivalent to 0.4 percent of GDP, contributing around 20 percent of the income of participating families.

In 2003, Ecuador updated _Bono Solidario_ , its existing social program focusing on poverty reduction that had been created in 1999. The new program, _Bono de Desarrollo Humano_ (BDH), is part of the Social Protection Plan of the _Ministerio de Bienestar Social_ and re-targeted the transfers under the erstwhile _Bono Solidario_ program. While the main purpose of BDH is to transfer cash to poor households, the transfers are also intended to be conditional on child enrollment and health care center visits. In its early days, compliance with the conditions was not monitored and consequently, non-complying households were not penalized. In order to

> 20 These villages were randomly selected out of 6,396 villages (4,546 considered to be in the treatment group and 1,850 in the control group) using probability proportional to size. Villages were identified on the basis of a community score based on information available from national census data regarding characteristics such as educational levels, occupational composition, and housing conditions.

> 21 Further details are available in Skoufias and Parker (2001).

> 22 The 7 states were: Guerrero, Hidalgo, Michoacan, Puebla, Queretaro, San Luis Potosi, and Veracruz.

16
Consider the change in the employment share of construction in rural areas of each of the 14 state‐groups. The top left panel of Figure 9 shows that higher construction shares are strongly positively associated with the predicted growth in wages for the _uneducated_ relative to _educated_ . Indeed, differences in construction industry growth explain about two‐thirds of the variation in the relative wage growth predicted by the SDI framework. The same exercise using the rural services share, an industry which also employs significant numbers of unskilled workers and which also expanded in relative terms over the last decade, shows a similar pattern but a weaker association with wages. In sum, the rural construction boom appears to have been an important, if not the main, driver of unskilled relative wage‐growth within rural India.

<!-- Start of picture text --> Kerala Kerala<br>R-sq=.66<br>Gujarat Gujarat<br>Northern States Northern States<br>Maharashtra Maharashtra<br>RajasthanWest Bengal West Bengal Rajasthan<br>Tamil Nadu OrissaUttar Pradesh Uttar Pradesh Orissa Tamil Nadu<br>Andhra Pradesh Andhra Pradesh<br>Madhya Pradesh Bihar Bihar Madhya Pradesh<br>KarnatakaSeven Sisters Karnataka Seven Sisters R-sq=.34<br>.4 .6 .8 1 .2 .3 .4 .5 .6<br>diff. log rural construction share 68-61 diff. log rural services share 68-61<br>Kerala Kerala<br>Gujarat Gujarat<br>Northern States Northern States<br>Maharashtra Maharashtra<br>RajasthanTamil NaduWest Bengal Orissa Uttar Pradesh R-sq=.02 Tamil NaduRajasthanWest Bengal OrissaUttar Pradesh R-sq=.04<br>Andhra Pradesh Andhra Pradesh<br>Madhya Pradesh Bihar Madhya PradeshBihar<br>Seven Sisters Karnataka Seven Sisters Karnataka<br>-.3 -.2 -.1 0 -.3 -.2 -.1 0<br>diff. share NREG job-card ed/uned 68 diff. share NREG worker ed/uned 68<br>diff. log rel. wage ed/uned 68-61 Fitted values<br>Figure 9: Drivers of changes in educated vs. uneducated wages within rural India<br>-.15 -.15<br>-.2 -.2<br>-.25 -.25<br>-.3 -.3<br>-.35 -.35<br>-.15 -.15<br>-.2 -.2<br>-.25 -.25<br>-.3 -.3<br>-.35 -.35<br><!-- End of picture text --> It is interesting to contrast the labor market impacts of the above compositional shifts to those of NREG (National Rural Employment Guarantee). Phase‐in of NREG began at around the mid‐point of our 2004‐2011 window. Analyses of NSS data preceding the 68<sup>th</sup> (2011‐12) round provide mixed evidence as to the rural wage impacts of NREG expansion (see Azam, 2012; Zimmerman, 2013; Imbert and Papp, 2015). However, NSS68, for the first time, provides _individual_ level data on NREG registration (job‐card holding) and take‐up (i.e., NREG employment in the last 12 months). This allows us to construct, for each state, the 16
social assistance jumps up in the second bimonth of 2018 in response to the Venezuelan influx and again in the fourth bimonth of 2020 as the response to the COVID-19 pandemic in addition to the influx of Venezuelan refugees and migrants. Civil defense (police) spending shows no change in 2018 and only increases in 2020. Figure 6 seems to suggest that the immediate consequence of the population shock is a temporary increase in social assistance expenditure as government of Roraima along with the Federal government spend more on welfare programs to accommodate the displaced Venezuelans but there seems to be no substantial impact on provision of public services.

Figure 6: Different categories of expenditure (Millions of Brazilian reais) by functions in Roraima.

<!-- Start of picture text --> Investment Personnel Spending<br>−20 −10 0 10 20 −20 −10 0 10 20<br>Education Health<br>−20 −10 0 10 20 −20 −10 0 10 20<br>Social Assistance Police<br>−20 −10 0 10 20 −20 −10 0 10 20<br>60 200<br>40 150<br>20 100<br>0 50<br>0<br>−20<br>40 80<br>60<br>30<br>40<br>20 20<br>0<br>10 −20<br>4 15<br>2 10<br>0 5<br>−2 0<br><!-- End of picture text --> Note: The dots show the bimonthly data on government expenditure by functions or categories in Roraima from year 2015 to 2020 and the x-axis shows distance from the second bimonth of 2018 in units of 2 months with 0 representing April 2018 and y-axis is in millions of Brazilian reais and is adjusted for inflation using national IPCA. The solid line represents the predicted values of a local linear smoother estimated using raw data on each side of the threshold at zero. Outer dashed lines indicate 95 percent confidence intervals.

Providing social security to all irrespective of legal status is a constitutional obligation in Brazil. Social security in Brazil is organized in three main blocks, social welfare and pensions, which is contributory and social assistance and health, which is non-contributory. The social assistance programs are administered and implemented jointly by the federal, state and municipal governments. The two largest social assistance cash transfer programs are Bolsa Familia and the Continuous Benefit Provision (BPC). Social assistance programs are designed to target the vulnerable at each part of their life cycles. For example, Bolsa Familia (PBF), which is a conditional cash transfer for households living in poverty and extreme poverty, targets not only families but also pregnant mothers, 10
I POLICY RESEARCH WORKING PAPER 1421

# Summary findings

Using the 1992 Bulgarian household budget survey, Bogetid and Hassan analyze the distribution of income and of the income tax burden by income and expenditure class and by rural-urban sector. They find:

* Low income inequality (although that is changing rapidly).

* A progressive income tax system. The poor (the lowest two-income decile) pay only 1.4 percent of their per capita income in income tax; the rich (the top decile) pay nearly 6 percent. In-kind income and expenditures are excluded from taxation.

* The urban sector paying proportionately more in taxes than the rural sector. For example, urban households pay 5.3 percent of their per capita income in income tax, whereas the rural sector pays 2.4 percent.

* Income tax contributes significantly to reducing income inequality at both the national and sectoral (rural-urban) le ecl, as the poor pay a smaller share of taxes than their share of national income.

These results hold whether income or expenditure is used as an indicator of economic well-being. Bogetic and Hassan caution that as in-kind income becomes monetized and the economy becomes more market-oriented, the system will become less progressive and urban-rural differences will diminish.

They contend that the bias toward higher urban taxes is justified to some extent by the fact that urban households benefit more from government services than rural households do.

- This paper a product of the Country Operations Division, Europe and Central Asia, Country Department I -is part of a larger effort in the region to analyze the consequences of government public finance policies. This paper is a part of a volume under preparation, _Financing Govemment in the Transition: Bulgaria,_ edited by Zeliko Bagetic and Arye Hillman. The preparation of the volume is supported by the Bank's Research Support Budget under dissemination grant "Taxation and Revenue Adequacy in Transition: Observations and Implications from Bulgaria" (RPO 679-60). Copies of the paper are available free from the World Bank, 18 1 8 H Street NW, Waashington, DC 20433. Please contact Faithlyn Smith, room H5-245, extension 36072 (26 pages). February 1995.

_The Policy Research Working Paper Serics dssmcrinates the findings of work on progress to encosrage the exchange of ideas about deelopment issu. An objectinv of tbe seris is toget the fmndings oYt ickly, even if tbepresentaiomare kss than fully poshe The papers carry the names of the authors and sould be used and cited accordingly. The findings, interpretations, and condusions are the authors'own and should not be attributed to the WorldBank, its Executive BoarofDirectos, or any of its mmber cotnis_ Produced by the Policy Research Dissemination Center
In our econometric analysis, we examine the frequency of dams in an area as a function of the sharing of the water resource and other characteristics that may affect the benefits and costs of dams in a location. We use Geographic Information System (GIS) software to create dependent variables, measures of resource sharing, and other explanatory variables. We define observations at the level of a geographic subbasin-country area: the intersection between a “subbasin” of a major river system and a country. Using the subbasin to define the unit of observation for dam counts is necessary because dam demand and supply characteristics (e.g., population and hydrological suitability for dam construction) vary by subbasin. Intersecting these units with country borders allows us to model countries’ decision making about dam placement.

Subbasins are defined by the HYDRO1k dataset from the US Geologic Survey (USGS), which uses global elevation data to divide land area into river basins and subbasins (USGS 2012). The subbasins are coded using the Pfafstetter system (Verdin and Verdin 1999), which provides a hierarchical coding of river basins and their subdivisions into several possible levels of subbasins. The finest subbasin classification has 6 digits. We rely on the 3-digit subbasin level as our basic unit of observation for tractability.<sup>8</sup> The Pfafstetter system codes basins in a way that makes it possible to identify whether each subbasin lies upstream of the others and thus is the basis of our count of downstream countries. An additional restriction was necessary because not all the areas coded with the same first digit by the Pfafstetter identifier share a river mouth.<sup>9</sup> To fix this problem, we used additional river basin data from 8 This choice facilitates the analysis but does mean that our coding misses upstream-downstream relationships in about 60 small coastal basins where the entire major river basin is only one 3-digit subbasin in HYDRO1K.

9 Given the system’s need to identify only 10 first-digit basins within a continent, a coastal area with many smaller rivers all draining to the sea can have many subbasins with the same first digit. In our analysis, the shared first digit initially gave a false impression of upstream-downstream relationships between some subbasins that are actually in different river basins.

6
<!-- Start of picture text --> Peak out of sample MCC 7000 Number of plots to reach peak MCC<br>0.30<br>6000<br>0.28<br>4<br>[e)<br>OS 0.26 gn 5000<br>va<br>aE 0.24 we2<br>a 8 4000<br>Pha 2<br>8 g<br>3000<br>0.20<br>0.18 2000<br>ow? _ss® yw’ ot «oTM gr? «oTM ge? a? yw’ ot «oe gr? oo<br>otyo er geSS ow” i ~~) ox? 3 orey) gr ge0 on? ies ~~) go? o*<br>Geolocation strategy Geolocation strategy<br><!-- End of picture text -->
devaluation rapidly, even though the consequences of the May 2005 had not fully materialized at the time of the survey.

Figure 2 illustrates a second important point, namely that the Pula devaluation increased the price of imported food products and consumables in general, making consumers the major losers of the devaluation. A likely causal mechanism linking the currency devaluation to subjective wellbeing is therefore (expectations about) the development in prices, particularly the price level of food and consumables. During the time of the _Afrobarometer_ survey in Botswana in late May and early June 2005, this was a very salient feature of the devaluation to the Batswana. In a report in the _Mmegi_ newspaper, several people being interviewed who were employed in various low-wage jobs expressed concern at the consequences of the devaluation. A taxi driver reportedly stated that the expected price increases “...will have a devastating impact on our business and the economy at large.” In the same report, another employee is quoted for saying that “putting food on the table will empty wallets” and that “I am concerned and feel impoverished.” These examples suggest that people in Botswana had clear expectations about what consequences the devaluation would have for the price level of consumables and, therefore, for their own well-being. They also suggest that the expectations of increasing prices could be an important factor driving individuals’ feelings of being impoverished and are therefore the most likely causal mechanism linking the currency devaluation to the drop in subjective well-being we observe in Figure 1.

Although the relationship between the Pula devaluation and subsequent drops in subjective well-being is clear in Figure 1, we can use pretreatment observations as approximate counterfactuals for post-treatment observations only on the assumption that the devaluation is a plausibly exogenous shock to the citizens of Botswana. Given the imbalanced sampling of the preand post-treatment groups, the plausibility of the exogeneity assumption of course requires that we successfully condition on relevant confounders, most importantly by adjusting for rural-urban 16
Table 9 presents the likelihood of bypassing for individuals who live in households that belong to the poorest and richest income quintiles in Chad. It shows that (conditional on seeking care) patients in rich households are significantly more likely to bypass providers than poorer patients. More specifically, patients in the 5<sup>th</sup> quintile are 3 times more likely to bypass modern providers as are patients in the poorest quintile. Nearly 37% of patients in the 5<sup>th</sup> income quintile bypassed a provider compared to 20% of patients in the 1<sup>st</sup> quintile.

Table 9: Bypassing of Health Centers by Quintile (Percentage)

||**1**<sup>**st**</sup>**Quintile**|**5**<sup>**th**</sup>**Quintile**|
|---|---|---|
|Modern sector|9.9|28.6|
|Informal sector|12.7|18.3|
|Total|19.8|36.5|

Source: Chad 2004 QSDS and authors’ calculations.

Focusing on the bypassing of private facilities, we observe that it is mostly people in the two richest quintiles who bypass private providers, with a significantly higher probability for the 5<sup>th</sup> quintile as shown in Table 10. It is also noteworthy that for all income groups, patients of private providers are more likely to bypass another private facility. For all levels of income, the patients of the formal public sector are the least likely to bypass a private provider.

Table 10: Bypassing Private Facilities

|**Quintile**|<br>n|**Public**<br>% Bypassed<br>private|<br>n|**Private**<br>% Bypassed<br>private|**Not**<br>n|**-for-Profit**<br>% Bypassed<br>private|
|---|---|---|---|---|---|---|
|Poorest|302|2.6|18|5.6|58|3.4|
|Q2|238|1.7|30|0.0|104|2.9|
|Q3|241|4.6|32|6.2|77|2.6|
|Q4|239|4.6|49|16.3|63|9.5|
|Richest|185|13.0|98|33.7|50|14.0|

Source: Chad 2004 QSDS and authors’ calculations.

# 6.3 Costs of bypassing

Because of the important impact on travel and direct medical costs involved, bypassing decisions are not casually taken by patients. By visiting a health center which is not the closest to their residence, the ‘bypassers choose to travel a distance on average twice longer than patients who go to the closest alternative (Table 11). The average travel time between the house and the clinic is also multiplied by two, 15
_36_ _Price Support at Any Price?_ macroeconomic gains appear bigger than policy costs. It means that (except for the first years of applying the policies) the policies may be financed from additional economic growth.

<!-- Start of picture text --> Agricultural Policy Effects for the Rest of Economy<br>(GDP gains diminished by policy costs)<br>2 -4<br>0<br>0-<br>0. -2<br>8-4<br>CL.<br>Fast Pami Fast PIU. L ast momr Jobs conMore pskw .Ful fwinacigV Budget ow<br>Io't4xA red..<br>Price support Altemraves Gradual Integraion<br>n 1995 l 2000 [ 2005 U 2010 l I<br><!-- End of picture text --> Figure 15 _Successful integration of the Polish agriculture with the EU require a fast restructuring of the sector. The resources for restructuring that the Polish economy may find are scarce; therefore, it is even more important that they are used in an effective way._ Efficiency of the restructuring efforts will be to a big degree reduced, if the accompanying policies have contradictory effects (slowing down restructuring and economic growth). Therefore, a preferable way of supporting the income relation in the period prior to Poland's full participation in the CAP is accelerating agricultural employment reduction rather than supporting prices.
_Table 8. Household Characteristics and the Risk of Poverty Alternative Consumption Aggregates and ihe Traditional' Poverty Line_ The Incidence of Poverty Under Different Household Characteristics and Consumption Aggregations

|_Household_<br>_Characteristics_|_Food_<br>_Spending_|_Food plus_<br>_Basic Non-Food_|_Food plus_<br>_Non-Food (with_<br>_education and energy)_|_Erpenditure_<br>_including imputed_<br>_water_|_Expenditure_<br>_including imputed_<br>_water and housing_|_Expenditure_<br>_with imputed_<br>_water, housing,_<br>_and durables_|
|---|---|---|---|---|---|---|
|Average Risk of Poverty|0.50|0.53|0.52|0.52|0.53|0.52|
|Rural|0.55|0.60|0.60|0.61|0.63|0.63|
|Urban|0.46|0.41|0.38|0.38|0.37|0.36|
|Sierra|0.50|0.49|0.47|0.47|0.47|0.46|
|Costa|0.49|0.49|0.48|0.48|0.49|0.49|
|Oriente|0.67|0.69|0.69|0.69|0.70|0.70|
|Black and White TV|0.52|0.50|0.49|0.49|0.49|0.48|
|Color TV|0.33|0.27|0.22|0.22|0.21|0.19|
|Bicycle|0.49|0.45|0.42|0.42|0.41|0.40|
|Refrigerator|0.39|0.33|0.29|0.28|0.27|0.26|
|<br>Radio/Cassette|0.46|0.45|0.43|0.43|0.43|0.42|
|House with Mud Walls|0.54|0.53|0.55|0.55|0.56|0.56|
|House with Dirt Floor|0.65|0.67|0.69|0.70|0.73|0.74|
|House with Wood Walls|0.60|0.68|0.65|0.67|0.72|0.72|
|Telephone Connection|0.30|0.21|0.14|0.14|0.09|0.09|
|<br>Networked Electricity|0.48|0.47|0.45|0.45|0.45|0.44|
|<br>Networked Water|0.44|0.40|0.36|0.36|0.35|0.34|
|Waste Disposal|0.43|0.37|0.34|0.34|0.32|0.31|
|Sewage Removal|0.45|0.41|0.38|0.38|0.37|0.36|
|Head with No Education|0.61|0.68|0.67|0.68|0.69|0.70|
|Primary Educated Head|0.55|0.57|0.56|0.57|0.58|0.57|
|<br>Secondary Educated Head|0.40|0.35|0.30|0.30|0.30|0.29|
|<br>Tertiary Educated Head|0.28|0.18|0.14|0.13|0.11|0.10|
|Indigenous Head|0.76|0.76|0.76|0.76|0.77|0.78|
**Decoupling and recoupling**

portfolio rebalancing, whereas the second represents new flows, allocated according to today ́s weights. It follows that, in times of little or no new flows to equity funds, the second term becomes irrelevant and the correlation between country flows and past weights due to rebalancing is negative. By contrast, in times of massive inflows or outflows, the second term dominates, and therefore hysteresis in portfolio composition (as noted, influenced by the composition of the benchmark) induces a strong cross-country correlation of flows in line with the results in **Table 10** .

# **6. Final remarks**

The paper documented an intriguing result: on the one hand, business cycles in emerging markets have gradually decoupled from those in advanced economies, as trade diversification, commodity strength and, particularly, the emergence of China took over the G7 as the main global factor behind output fluctuations in the emerging world. On the other hand, cross-market co-movements (market betas, even at lower frequencies) have remained high or even grown higher in the past few years, even before the synchronized sell off of 2008 took place.

Are these contrasting findings due to the globalization of emerging markets, namely, the increasing share in the hands of global investors prone to cross market arbitrage and proxy hedging? Our preliminary tests, using alternative stock and flow measures of FG, provide mixed answer to this question. While a closer look at the impact of traditional FG proxies yields negative results, capital flows from global equity and bond funds indeed foster financial recoupling during downturns, reflecting the fact that they trade near their respective benchmarks and respond to withdrawals by liquidating holdings across the board. Perhaps predictably, financial integration, in the sense of exposure to a common pool of global investors, strengthens the impact of swings in the risk appetite and liquidity preferences of those investors, much in the same way as the internationalization of banks makes local banking sectors more sensitive to liquidity shocks in global markets. That said, the relatively muted role played by country fundamentals in asset returns even at longer frequencies deserves a more careful and detailed look.

<u>17</u>