rafmacalaba/datause-encoder-data huggingface dataset:
As noted earlier, some papers consider changes in longer term Treasury yields as a measure of the surprise element in policy announcements. Next, we estimate Equation 1 with changes in 10year Treasury yields as the measure of surprise policy announcement. We obtain larger spillover coefficients for changes in 10-year Treasury yields, Table 2. This is consistent with Gilchrist et al (2014) who similarly find a larger spillover for changes in longer term yields. Again, the spillover persists for all three days, and increases for equity prices and bond yields over longer horizons.

**Table 2: Effect of surprise US Monetary policy (change in 10-year yields) on Emerging Economies’ Financial Variables**

||E<br>k=1|xchange rate<br>k=2|s<br>k=3|k=1|Equity prices<br>k=2|k=3|k=1|Bond yields<br>k=2|k=3|
|---|---|---|---|---|---|---|---|---|---|
|10yr Treasury yield|0.068***|0.085***|0.074***|-0.060***|-0.104***|-0.134***|0.157***|0.223***|0.241***|
||[5.76]|[6.15]|[6.44]|[-3.50]|[-4.84]|[-7.06]|[3.52]|[5.54]|[5.35]|
|Constant|-0.077***|-0.042***|-0.046***|0.370***|0.414***|0.538***|-0.148***|-0.175***|-0.144***|
||[-19.79]|[-9.19]|[-12.22]|[66.68]|[54.54]|[81.73]|[-11.06]|[-12.66]|[-9.76]|
|Observations|1,300|1,300|1,300|1,194|1,200|1,197|1,049|1,052|1,053|
|R-squared|0.035|0.040|0.027|0.008|0.014|0.020|0.039|0.057|0.059|
|# of countries|20|20|20|19|19|19|17|17|17|

_Notes:_ Dependent variables are percent change in exchange rate, equity prices, or bond yields, between the day of FOMC announcements and k days after that, as per Equation 1. The independent variable is percent change in 10-year Treasury yield between t and t-1, t being the day of the FOMC announcements. Regressions include country fixed effects. Robust t statistics are in parentheses. *, **, *** indicate that the coefficients are significant at 10, 5, and 1 percent level.

We ask a number of ancillary questions: is the spillover impact symmetric for an unanticipated tightening and an unanticipated easing of policy? Is the effect proportionate to percent changes in Treasury yield, or is it particularly large for large changes in yields? In other words, is the impact disproportionately larger for tail events? Do emerging economies react in a similar fashion to monetary policy announcements by other advanced economies, such as the euro area, Japan, or the UK, as they react to announcements by FOMC?

In order to ask whether the spillover is symmetric for unanticipated policy tightening and easing, we define a dummy variable, ���h��, which takes the value 1 if 2-year Treasury yield increases after an FOMC meeting, and zero if it declines. We estimate Equation 2 below by including the interaction between this dummy and the change in Treasury yield in the egression. The regression specification and other variables remain the same as in Equation 1.<sup>16</sup>

> 16 Out of the 65 FOMC announcements in our sample, 22 events are classified as Tightening and 35 as Easing. The remaining 8 announcements cannot be classified as either since the 2-year Treasury yields did not change following the announcement. We drop these 8 announcements in estimating Equation 2.

11
<!-- Start of picture text --> Mumbai Housing Market - Floor consumption and Housing prices<br>ee|<br>2,200vot lowedSHEESH St A TT TTI TTT TTT<br>a 2,000 Olic- Apartments te |<br>Tioa onaelomer | TPE TTT tT TTT<br>®+ too1,800 | _laroetlettebeey‘New Apartments (in | | LITT [TTT tT TUT<br>= |<br>~—to} 1,600 | TTT oT ETT Et= TTT<br>Poot | LITT TP ETT Pa fe TT TT<br>=got laoo LU A | |<br>S 1.200 oT TT |Tle| i i4 a = elLTTea<br>oee a|<br>@ 1,000 1 el 1 } jj<br>* ee<br>800 Fi :<br>600eeee |<br>a TM anne<br>400 7<br>LL PTTUTTEtT eR TT ah TT<br>a Ie etree? TT<br>10,000 100,000 1,000,000 10,000,000 100,000,000<br>file source:VKP Mumbai Income v1-xlsx Price of dwelling unit (INR)<br><!-- End of picture text -->
such as health and education.<sup>7</sup> This scheme, which requires Venezuelans to apply for it, covers migrants already living in Colombia with either regular or irregular status.

However, we know very little about the public’s preferences for specific policies towards Venezuelan migrants. To be sure, Proyecto Migración Venezuela has collected data on public opinion towards Venezuelans in Colombia. Using data collected in September 2020, Figure 3 shows the percentage of individuals agreeing to different statements. At the time of that survey, a large percentage of Colombians perceived Venezuelans as a threat to employment (i.e., taking jobs away) (57%) while the majority also perceived them as a fiscal burden (58%). Yet a majority of respondents also agreed that Venezuelan migrants should be given access to healthcare (72%) and employment (54%), while a strong plurality thought they should be granted legal documents to access health and labour markers under the same conditions as Colombians (48%).<sup>8</sup> However, these observational results have several potential limitations, including difficulty in interpreting whether Colombians prioritise some policy aspects over others. A survey experiment is therefore well-suited to elicit Colombians’ views towards realistic policy options and understand their likely drivers.

Figure 3 - Attitudes towards Venezuelan migrants, September 2020 <!-- Start of picture text --> Threat to employment<br>Fiscal Burden<br>Should be given access to employment<br>Should be granted access to health<br>Should be given legal documents<br>0 20 40 60 80<br>Percent<br><!-- End of picture text --> Note: Authors's calculation using data from Proyecto Migracion Venezuela, Revista Semana 2020. Percentage of respondents agreeing with the statements.N(2020-9)= 2,289

> 7 https://www.cancilleria.gov.co/newsroom/news/presidente-duque-anuncia-decision-historica-crear-estatutoproteccion-temporal

> 8 Proyecto Migración also collected data in April 2019 and March 2020. The proportions of respondents agreeing to these statements have remained stable between these waves. We thank Adriana Sabogal and Proyecto Migracíon Venezuela for granting us access to the data. https://migravenezuela.com/

13
# **GAS**

4.19 The only social policy practiced in the gas sector is the Patagonian subsidy, which is designed to compensate households in the country’s coldest region for the relatively high heating costs that they face. This is achieved via a discount of around 75% on an initial ‘subsistence’ block of natural gas or LPG consumption, that is regionally differentiated to reflect climatic conditions, and varies from 62.5 to 1,250 cubic meters monthly. This subsidy is financed by a national surcharge of US$0.004 per cubic meter of gas sold from the wellhead, which generated US$100 million of revenues in the year 2002.

4.20 In common with the national Tariff Compensation Fund for electricity, the Patagonian subsidy is based on a principle of horizontal equity across regions, and takes no account of ability to pay. In fact, the poverty rate in Patagonia is among the lowest in the country. In November 2002, it stood at 37% as against 46% at the national level. Moreover, due to this relatively low poverty rate and to its small population, Patagonia contains barely 3% of Argentina’s poor households.

4.21 The distributional incidence of the Patagonian subsidy can be estimated on the basis of survey data on natural gas and LPG expenditure in that region (Figure 10)<sup>7</sup> . The figure illustrates that a relatively small proportion of the beneficiaries and of the benefits accrue to the bottom income quintile, but are instead concentrated in the middle quintiles of the income distribution. The Patagonian subsidy has an associated error of inclusion of around 70%. The error of exclusion is 4% with respect to the total poor resident in the Patagonian region. The concentration coefficient is estimated at +0.08, indicating a broadly egalitarian but mildly regressive distribution.

> 7 This exercise is based on expenditure reported by 70 Patagonian households in the OPSM survey, which account for 45% of the households surveyed in that region. An important limitation is the fact that the survey was undertaken during the summer period. Given that higher income households probably spend more on winter heating than lower income households, the distributional incidence for the winter period could be significantly more regressive.

26
# **2.2.1 The Rent- to-Value Approach**

Another method to estimate the capitalization rate is used by Yates (1994) and Sounders and Siminski (2005) on income distribution in Australia. In these examples, the rent-to-value rate is calculated as the value of gross imputed owner-occupied rent derived from the National Accounts<sup>16</sup> divided by an estimate of the gross value of the owner-occupied housing stock derived from the Household Expenditure Survey (HES). Imputed rents are then obtained by applying the capitalization rate to the value of the property reported in the HES.

Two problems related to this approach have been underlined in the literature (see Phillips, 1988, ILO, 2004, Garner and Kogan, 2007). The first consists in using the same capitalization ratio for tenants and owners within the same area. This might be misleading when dwellings’ characteristics differ significantly between these two population groups. The second problem arises from the omittedvariable bias embedded in the variable, which summarizes underlined differences in the quality of the dwellings for owners and tenant. ooeelle Regardless of previous caveats, the rent-to-value approach has been used, for instance, in the United States National Accounts imputation for the services of owner-occupied housing (see Lebow and Rudd,

> 15 In Malpezzi and Mayo  (1987) the rent-to-value ratio is also defined _amortization ratio_ .

> 16 This is based on the market rents received for dwellings that have similar characteristics to those that are owner-occupied.

11
the publicly available geo-coordinates of survey locations are generally subjected to a random offset (i.e. are scrambled) in order to protect the anonymity of survey respondents. A second objective of this study therefore is to investigate whether these random offsets undermine the ability of this approach to produce accurate poverty maps.

Assessing the success of this alternative approach to producing a poverty map will depend on how success is measured. When the ability to capture the geography of poverty is used as the metric of success, our analysis suggests that estimating small area poverty using remote sensing data offers a promising alternative to building a poverty map using population census data. In our application to Malawi the two approaches are found to produce maps that show nearly identical geographies of poverty. When the objective is to identify poverty in a specific district or set of districts of the country, however, we find that poverty estimates obtained using remote sensing data can deviate substantially from the benchmark estimates obtained using population census data. In summary, the value of poverty maps built using remote sensing data alone will depend on the needs of the decision makers.

On the empirical question of whether randomly off-setting survey coordinates (to protect anonymity of survey respondents) may undermine the accuracy of a poverty map derived from remote-sensing data, our findings echo the comparison with census-based estimates. The random scrambling of the geographic coordinates does not meaningfully alter the estimated geography of poverty, yet may have meaningful impact on estimates for individual districts and may furthermore lead to an over-estimation of statistical precision. The latter observation arguably stems from the fact that the random offsets partially destroys the spatial correlation structure in the data and error term.

Our validation study confirms that for many practical purposes accurate poverty maps can be obtained in countries where population census data are not available. Given that remote sensing data are globally available and are typically updated on an ongoing basis, this also opens the door to updating poverty maps on a higher frequency – whenever new household survey data become available. On a practical note, since the remote-sensing-based models are estimated at the survey PSU level (rather than household level), with the area-level poverty rate as dependent variable (rather than household income or consumption), experimenting with alternative poverty measures or alternative poverty lines would require returning repeatedly to the basic modeling stages of the analysis. This is not the case in the census-based approach, where the model is estimated at the household level. In this sense the remote-sensing-based approach can become rather burdensome.

It remains to be verified whether the remote-sensing-based approach is equally successful in heavily urbanized settings. Small, though not necessarily low-population, 33
# Tables

**Table 1:** Female labor force participation and Gini index (fixed effects panel analysis, all countries).

|Dep. var.: Gini index (1=100)|||||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Female LFP (% of female population ages 15+)|0.302***<br>[0.116]|0.535***<br>[0.151]|0.731**<br>[0.343]|0.656**<br>[0.323]|
|Female LFP squared|-0.003**<br>[0.001]|-0.005***<br>[0.001]|-0.006*<br>[0.003]|-0.006**<br>[0.003]|
|Services value added (% of GDP)||0.176***<br>[0.049]|0.165***<br>[0.052]|0.128**<br>[0.050]|
|Exports of goods and services (% of GDP)||0.049*<br>[0.027]|0.013<br>[0.027]|0.025<br>[0.028]|
|Imports of goods and services (% of GDP)||-0.012<br>[0.021]|0.027<br>[0.032]|-0.024<br>[0.032]|
|Foreign direct investments (% of GDP)||0.008***<br>[0.003]|0.010***<br>[0.002]|0.008***<br>[0.002]|
|Age dependency ratio (% workforce over total<br>population)||0.216***<br>[0.046]|0.225***<br>[0.055]|0.255***<br>[0.048]|
|Labor force with secondary education (% of total)|||-0.090*<br>[0.050]|-0.128**<br>[0.049]|
|Government expenditure (% of GDP)|||-0.025<br>[0.017]|-0.028**<br>[0.010]|
|International migrant stock (% of population)||||0.350***<br>[0.109]|
|Country fixed effects|Yes|Yes|Yes|Yes|
|Year dummies|Yes (2002-<br>2018)|Yes (2002-<br>2018)|Yes (2002-<br>2018)|Yes (2002-<br>2018)|
|Observations|1025|1003|708|587|
|Within R-squared|0.206|0.360|0.302|0.346|
|Between R-squared|0.025|0.062|0.011|0.001|
|Overall R-squared|0.010|0.012|0.005|0.018|

_Notes:_ Robust standard errors in brackets;<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

_Source:_ Authors’ own elaboration using World Bank’s data.

26
measures the proportion of people below a certain poverty line but takes no account of how far they are below that line—the degree of poverty—or whether they are becoming even poorer. To address the situation of the poorest and to evaluate whether their economic situation has improved, the squared poverty gap measure (or P2) is used. This takes into account the degree of poverty, because it gives more weight to the poorest and most vulnerable. The P2 poverty measure reveals that the extreme poverty depth fell 4.7 percentage points to 14 percent during 1993-2001 (Figure 4.3). As a matter of fact, the squared poverty gap measure reveals that poverty was deeper in 2001 than in 1981 (5.6 percentage points), implying that the poorest became poorer. In fact, Bahia has performed slightly worse than the Northeast and Brazil as the squared poverty gap measure increased by 2.1 and 2.7 percentage points, respectively.

<!-- Start of picture text --> Figure 4.3: Squared Poverty Gap (P2), 1981-2001<br>2 5<br>B r a z il<br>N o r t h e a s t R e g io n<br>2 0 B a h ia<br>1 5<br>1 0<br>5<br>0<br>1 9 8 1 1 9 8 5 1 9 8 8 1 9 9 0 1 9 9 3 1 9 9 5 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 1<br>Source: Calculations based on selected years of PNADs in 1981-2001.<br><!-- End of picture text --> The income of the extreme poor is stagnant. One possible explanation for the recent increase in the depth of poverty could be the drop in average incomes of the extreme poor. Data for 1995-2001 reveal that after the launch of the inflationbeating Real Plan in 1994 average per-capita household incomes fell in all the richest northeastern states, including Bahia (Table 4.2). In 2001, the median income of the extreme poor household heads (R$40) is less than a fourth of the median income of the non-poor (R$180) in Bahia. Moreover, workers in Salvador need to work more hours to be able to pay for a basic food basket. Data published by Dieese reveal that a worker, remunerated a minimum salary per months, needed to work around 140 hours in December 2002 to afford the basket compared to 118 hours in December 2001.<sup>8</sup>

> 8 Source: www.dieese.org.br.

15
then equation (8) implies that the cereal share must change by _β_ 1 _gH_ + _β_ 2 _gH_ ( _gH_ + 2 log( _c_ )) by year _H._ With _β_ 2 = 0, the quadratic Engel curve collapses to the Working-Leser model, and the change in the cereal share is a constant, _β_ 1 _gH_ , across households. In this case, economic growth does not affect the distribution of welfare costs of higher cereal prices; every household experiences the same decline in its cereal share and hence the same fall in welfare costs.

Estimating equation (8) on the NSS61 sample of around 117 thousand households, we obtain _β_<sup>�</sup> 1 = _−_ 0 _._ 351 (0 _._ 012) and _β_<sup>�</sup> 2 = 0 _._ 020 (0 _._ 0009),<sup>33</sup> which allows us to reject WorkingLeser in these data. Figure 12 displays the quadratic Engel curve along with a semiparametric counterpart estimated by partially linear regression. The parametric model does a good job of capturing the flattening of the Engel curve at the upper tail of the per-capita expenditure distribution but overpredicts the extent to which cereal shares fall with income growth for the poorest households. Even so, adjusting the net consumption ratio to reflect the cereal shares that will prevail in 2040 has no appreciable distributional implications. Figure 13 shows the incidence of price effects by percentile in rural and urban areas assuming that _H_ = 35, _gurban_ = 1 _._ 49 _,_ and _grural_ = 1 _._ 17 (see next subsection). In both sectors, the revision of _px/c_ leads to a small upward shift in the incidence curve. The ”true” distribution of welfare costs of the 10 percent rise in cereal prices is likely to lie somewhere in between these pairs of incidence curves. Repeating this exercise using the better-fitting cubic Engel curve (see Figure 12) leads to practically identical conclusions.

# **5.6 Changes in poverty**

Since consumption changes do not map linearly into poverty changes, predicting the increase in the poverty rate attributable to climate change involves an additional step. On top of the assumption of distributionally neutral growth underlying the CCICs, we need to take an explicit stand on the growth-rate that will prevail between our initial consumption measurement (2005) and the endpoint of our horizon (2040). In particular, the predicted poverty rate in the population given climate change, as calculated from our sample, is where _g_ and _H_ are as in the previous subsection, _c_<sup>0</sup> _i_<sup>is household</sup><sup>_i_’s initial per-capita expen-</sup>

> 33The regression includes household demographics (size, proportion females, proportion old and young, head age and age squared), scheduled caste/tribe dummies, an urban dummy, and district fixed effects. Standard errors are adjusted for clustering on district. Separate regressions for urban and rural areas yield very similar estimates of _β_ 1 and _β_ 2.

26
13 automatic de-registration of the unemployed participants.<sup>12</sup> For example, the Restart programme in Britain does not mean that the unemployed move automatically off the register, so a positive correlation between the Restart measure and outflows from unemployment does imply that the programme has improved the search effectiveness of some of the unemployed. In the Polish case hirings of the unemployed into regular jobs will be regressed on the stock of vacancies and unemployment and the (lagged) stocks of participants of retraining and further training. In Poland persons entering a training scheme do not leave the register. With a lagged training variable one can, therefore, test the effect of training on hirings of the unemployed: after having participated in a training scheme unemployed individuals might find a regular job more readily than those who have not participated. Using such estimates of outflow functions, based on aggregate data, has the advantage that it allows the detection of additional net hirings after substitution and dead weight effects concerning the stock of unemployed have been accounted for.

The "working horse" giving economic content to models of outflows from unemployment is the matching function where in its simplest form unemployed job searchers are matched with vacant jobs. In this context ALMPs can be thought of as measures which facilitate this matching process. When ALMPs are added to the stock of unemployment and vacancies as factors potentially determining job matching we speak of an "augmented matching function". Below, such a function is used to evaluate Further Training and Retraining in Poland.

As a second application of transition methodology, "problem groups" in the labour force are identified by Markovian analysis of flows between the labour market states employment (E), unemployment (U) and not-in-the-labour-force (N).<sup>13</sup> On the basis of LFS data transition probability matrices (P) of the following type can be estimated for specific groups of the labour force: 12For a critique of applying augmented matching functions even when ALMP participation implies deregistration see Calmfors (1994).

13The assumption that movements between labour market states are governed by a Markov process implies that the transition probabilities depend only on the state currently occupied. Applications of Markovian flow analysis to Western labour markets are Marston (1976), Toikka (1976) and Clark and Summers (1979,1982a,1982b), among others, while applications to a labour market in transition are Abraham and Vodopivec (1993), Bellmann et al. (1995), Gora and Lehmann (1995), Foley (1995) and Steiner and Kwiatkowski (1995). The Markovian assumption can be considered appropriate for a transition economy subject to a sudden structural shock, where individual work histories will be of lesser importance.