rafmacalaba/datause-encoder-data huggingface dataset:
several censuses. In addition, censuses do not identify the legal status of immigrants and do not track return migration. Finally, throughout decades, there have been changes in methodology and coverage. Thus, the fact that the question asking for the year of immigration was reformulated between the last two censuses complicates the task of accurately isolating immigrants by cohort. The 1980 and 1990 questionnaires require that individuals report the year they came to stay, while the 2000 one asks for the year they came to live in the US, implying that for those having entered the country a multiple number of times, prior experience may not be observed. In addition, some individuals may declare the year of immigration as the year when they received their permanent residence, although they may have already spent time in the US as students, on work visas or as undocumented migrants.<sup>17</sup> Since censuses are filled out in the spring, we assume the information gathered by Census 2000 about immigrants arriving in 2000 cannot fully mirror the degree of comprehensiveness available for that year. Therefore, we choose to exclude the 2000 arrivals from the 2000 dataset. The same issue also arises with respect to Censuses 1980 and 1990. For the reasons detailed above, we consider interval 1975-1980 from Census 1980 to be equivalent to interval 1975-1979 as reported in Census 1990.

# **_3.2. EMPIRICAL RESULTS_**

The coefficients based on the estimation of equation (1) with Education Index 1 as a dependent variable are provided in Table 4. Annex table A1 provides the results for the other two specifications. Assuming migrants arriving in the first and the second half of a decade from a given country are homogeneous with respect to ability, the coefficient for years since migration ( _β3_ ) provides the part of the assimilation effect that is common to all migrants regardless of country of origin.<sup>18</sup> That effect is negative and significant in each specification, indicating that, other things being constant, the occupational placement of an individual that has migrated during the second part of a decade is about 3-4% lower than that of one who has arrived earlier, i.e, in the first part of the decade.<sup>19</sup> Thus, in accordance with the previous literature focusing on earnings, experience acquired in the US labor market is an important determinant of performance.

> 17 Jasso et al. (2000) claim that recent immigrants sampled in the decennial censuses “may be neither recent nor immigrant.”

> 18 Whatever is country-specific in the assimilation effect is captured by the coefficients of the country dummies.

> 19 The effect is 6% for the 1975 arrival, evaluated in the first decade after migration.

11
A second measure capturing the synchronization in policy rates is the share of economies in the same rate phase (i.e., easing or tightening), instead of the share that is increasing or decreasing rates (or doing QE). Although increases and decreases in interest rates will be correlated with whether an economy is in a tightening or easing phase, this second measure of synchronization will better capture more sustained shifts in the overall policy rate—rather than isolated changes in any quarter.<sup>32</sup> Chart 11 shows this comparison by splitting the same sample into the share in a tightening phase (in blue) and easing phase (in red) each month.

Chart 11 Share of Economies in an Easing or Tightening Phase (Percent of sample per month) <!-- Start of picture text --> Tightening<br>Phase<br>Easing Phase<br><!-- End of picture text --> Sources: Authors’ calculations based on the rate cycles identified in Section II, with data ending in May 2024. Notes: Shows share of sample in a tightening phase or easing phase in each month. Includes phases for individual euro area countries through 1998, and then phases for the ECB from 1999 through the end of the sample.

Some of the patterns noted in Chart 10 are even more apparent in Chart 11 when focusing on phases instead of individual rate changes. For example, the red (easing phases) continues to be more dominant than the blue (tightening phases), with an average of 62 percent of the economies in an easing phase over the full sample. The share of red (relative to blue) is even larger for easing phases than for rate decreases and QE, as rates are held constant for longer at the end of easing phases (as documented in Section VI.1), and these “holding periods” are included in the calculation of rate phases (Chart 11) but not as active policy adjustment (Chart 10).

What stands out even more distinctly in Chart 11, however, is the blue waves—which now look more like steep mountains—indicating periods of highly synchronized tightening phases. These rapid and highly synchronized shifts for a large share of the sample from an easing to a tightening phase are likely caused by global factors, and the blue “mountains” before 2020 could provide useful comparisons to the current, post-pandemic tightening phase.

> 32 More specifically, an economy can be in an easing phase if it: (a) lowers the policy interest rate or has a QE program in the quarter; (b) keeps the policy rate on hold; or (c) raises the policy rate but this does not qualify as shifting to a tightening phase (as defined in Section II). Only the first set of economies are included as decreasing rates in Chart 10, while all three sets are in an easing phase in Chart 11.

33
invest in human capital and also to enhance tax collection mechanisms by increasing direct taxes if necessary and reassessing the redistributive effects of the system (government driven). OECD (2010) is more inclined towards improving tax administration, transparency and institutional image prior to addressing tax reforms (state driven). In this paper we have proposed a third way which encompasses enhancing communication and accountability mechanisms between the state and the citizenry (civil society or middle class driven).

# **V Conclusion**

In conclusion, in the Dominican Republic, public sector services in the areas of electricity supply, education and health present deficiencies persisting over time. If the quality of education, health or public transportation is low, the middle class is likely to opt for private services. At the same time, it would prefer to contribute less to the financing of public expenditure.

<<*...+ _if education, health care and other public services lack quality, the medium class population will be more likely to consider themselves losers from a fiscal point of view, which will make them less inclined to finance the public sector_ >>.

The paper has described how the middle class in the Dominican Republic is adopting private solutions instead of using collective goods. This trend, combined with low levels of institutional trust, results in low engagement in collective action for demand, leading to malfunctioning accountability and enforcement mechanisms between service delivery suppliers and beneficiaries. The main outcome is a vicious circle where it is too costly to create both the political and the civil society coalitions that would lead to achieve greater government accountability and improvement in service provision.

At the same time, collective demands for the 4% of GDP in education show that this perverse dynamic can be changed in certain cases, as the citizenry can exert some pressure on the executive, thus enhancing public accountability and ultimately achieving better quality services. This has been possible in a context less differences in the levels of opting out for private schools between the poorer and the middle class, compared to the wider gaps observed in the adoption of private solutions in the electricity sector and the health sector.

One of the main caveats of this paper is that we have not been able to test whether there are differences in civil participation and political activism across socioeconomic classes or income quintiles, due to lack of reliable income variables in opinion surveys. Still, we believe that the discussion embedded in this country case may be an interesting starting point for a

[30]
(1978), US immigrants are positively self-selected with respect to unobservable abilities and motivation. Furthermore, there is evidence of certain degree of heterogeneity in the assimilation rates of immigrants coming from different origin countries<sup>6</sup> .

Borjas (1985, 1995) and others<sup>7</sup> , on the other hand, highlight the importance of “cohort effects” – the unobserved quality of successive immigrant cohorts measured by ability and motivation – which has declined steadily over time. Once these are controlled for, the convergence between immigrants’ and natives’ earnings becomes less likely. In support of this argument, Borjas (1987) presents a more nuanced description of the selection mechanisms, which relate to the characteristics of origin (versus destination) countries.

More recent studies rely on combinations of cross-section and longitudinal sources rather than the widely used repeated cross-section data to produce assimilation estimates that control for biases caused by individual heterogeneity and out-migration. More recently, Lubotsky (2007) finds previous studies to have overestimated the wage progress of immigrants who arrived between 1960 and 1980. Focusing on period 1994-2004, Kim (2009a) obtains little evidence of convergence between earnings of immigrants and natives, both at aggregate level, and by migrant’s country of origin.

Most of these papers discussed above use wages and monetary compensation as measures of immigrants' performance which provide a useful metric for easy comparison across professions and locations. Yet, the focus on wages creates other complications. First, monetary compensation might reflect local living costs and other factors. Second, wages might exhibit changes over time such as due to inflation and labor market conditions. When dealing with assimilation effects over several decades, more stable metrics of performance might be preferred.

Chiswick (1977) and Jasso and Rosenzweig (1986, 1988) are among the first papers to the study migrants’ occupational placement. In a widely cited paper, Jasso and Rosenzweig (1995) identify the differences in occupational placement of migrants entering the United States through family reunification versus skill based visa programs. Powers and Seltzer (1998) and Powers et al. (1998) provide gender-specific evidence of improvement in initially undocumented immigrants’ occupational status on the US market. Green (1999) employs 1981, 1986 and 1991 censuses to show that after a period of adjustment, Canadian immigrants are more likely to be found in professional and more skilled manufacturing occupations compared to natives, but this pattern declines across successive cohorts. Using 2000 US Census data, Mattoo et al. (2008)

> 6 Chiswick, 1978, Borjas, 1995, 2000, Duleep and Regets, 1997, Schoeni, 1997 7

> Funkhouser and Trejo (1995), Lalonde and Topel (1990), and Baker and Benjamin (1994) for Canada

5
Table 5: Quarterly Drivers of Portfolio Debt Inflows by Sector, 2002–2014

|Panel A|: All Count|ries||
|---|---|---|---|
|(1)<br>Total|(2)<br>Public|(3)<br>Banks|(4)<br>Corp.|
|log(VIX_t−_1)<br>-1.160<sup>_∗∗_</sup>|-0.0572|-0.717|-0.386<sup>_∗∗_</sup>|
|(0.531)|(0.201)|(0.381)|(0.183)|
|GDP Growth_it−_1<br>0.0297|-0.00441|0.0237|0.0104|
|(0.0323)|(0.0135)|(0.0156)|(0.0119)|
|Observations<br>2695|2695|2695|2695|
|_R_<sup>2</sup><br>0.006|0.000|0.008|0.005|
|CountryFE<br>Yes|Yes|Yes|Yes|
|Panel B: Ad|vanced Eco|nomies||
|(1)|(2)|(3)|(4)|
|Total|Public|Banks|Corp.|
|log(VIX_t−_1)<br>-1.736|0.526|-1.557|-0.705|
|(1.263)|(0.360)|(0.901)|(0.435)|
|GDP Growth_it−_1<br>0.147|0.0311|0.0689|0.0466|
|(0.0938)|(0.0363)|(0.0476)|(0.0364)|
|Observations<br>1127|1127|1127|1127|
|_R_<sup>2</sup><br>0.025|0.004|0.019|0.018|
|CountryFE<br>Yes|Yes|Yes|Yes|
|Pa|nel C: EM|||
|(1)|(2)|(3)|(4)|
|Total|Public|Banks|Corp.|
|log(VIX_t−_1)<br>-0.750<sup>_∗∗∗_</sup>|-0.423|-0.135|-0.192<sup>_∗∗∗_</sup>|
|(0.234)|(0.207)|(0.108)|(0.0567)|
|GDP Growth_it−_1<br>-0.0242|-0.0228<sup>_∗∗_</sup>|0.00457|-0.00596<sup>_∗∗∗_</sup>|
|(0.0121)|(0.00906)|(0.00625)|(0.00170)|
|Observations<br>1372|1372|1372|1372|
|_R_<sup>2</sup><br>0.010|0.010|0.003|0.010|
|CountryFE<br>Yes|Yes|Yes|Yes|

Sample is from 2002q4-2014q4, countries as listed in Appendix A.4. Portfolio Debt inflow data is constructed by AHKS, as described in Section 2. Dependent variables are expressed as a percentage of GDP. VIX is the implied volatility of S&P 500 index options. GDP growth is calculated as a year-onyear percentage growth. Errors are clustered at the country level. ** p _<_ 0.05, *** p _<_ 0.01 34
In the combined ROW and Cameroon scenario, household incomes fall, although less than under unilateral Cameroonian liberalization. The impacts offset each other in such a way that rural and base-year poor households have only slightly larger falls in their incomes than their urban and base-year non-poor counterparts. The impacts of ROW liberalization imply that incomes fall slightly more for urban and base-run non-poor households than in rural and base-run poor households in the case combined scenario.

# **4.4   Poverty effects**

In the analysis of variations in the Foster-Greer-Thorbecke (FGT) poverty indicators, impacts derive from two sources: (1) the change in household income; and (2) the change in consumer prices, which, in turn, affect the poverty line. Overall, it is clear that free world trade slightly reduces poverty, whereas domestic liberalization substantially increases poverty (Table 4).

As a consequence of ROW liberalization, the poverty headcount index decreases from 40.22 to 39.28 percent for the entire country, the poverty gap falls from 13.76 to 13.28 percent, poverty severity declines from 6.38 to 6.08, and the Gini index goes from 0.458 to 0.454. Free ROW trade reduces poverty in rural areas, where income gains are greatest, but leaves the situation of the urban poor practically unchanged, peculiarly for the poverty gap and poverty severity (Table 4). One of the reasons explaining this stagnation in urban area is the fact that the sharp poverty alleviation in households belonging to food industry (poverty headcount shifts here from 20.54 to 14.15 percent) is strongly mitigated by the poverty worsening among households operating in the textile industry (poverty headcount rise from 29.20 to 31.28 percent), which represent a greater proportion of the total population. Overall, ROW liberalization benefits both 20
We also note a similar trend for full immunizations, but not as strong as measles vaccination. What this suggests is that those outcomes that are directly dependent on a strong health system seem to be performing well, including the role of health extension workers who have been linked with improving sector performance in rural areas.

Second, in contrast with health services, we find mixed results on child health status (undernutrition and mortality). Although there has been a substantial reduction in child undernutrition, there has also been an increase in the inequality between the poor and the rich. Child nutritional outcomes are affected by many factors, including food security, feeding practice, availability of monitoring services and supplements, which was not captured by this study so as to explain the increasing inequality. It is also the case that multisector determinants play a key role in nutrition and in mortality. Having considered this limitation, pro-rich progress in health status coupled with propoor progress in health services would be due to differences in the quality of services accessed by different groups, as well as the role of other socio-economic factors beyond the health sector. This predicament might also point to the differential progress in other important determinants of these health outcomes. However, for child mortality, wealth-related health inequalities progressed from a much pro-poor inequality to more equality. Although the direction is towards the pro-rich inequality, the distribution has been around the equality line. It is important to note that various DHSs surveys have 36
<!-- Start of picture text --> a<br>ita =<br>an a ial<br>Population<br>HM 0-164<br>HN 165- 591<br>592- 1234<br>HN 1235- 2079<br>HE 2080 - 3490<br>0 100 200 400 Km<br>Leia tii td<br><!-- End of picture text -->
large increases in unit labor costs in EU11 over the last decade. For the case of EU15, such a correlation exists but is not as robust as in the case of EU11, suggesting that institutional factors play a more important role as determinants of unit labor costs differences in high productivity countries.

**Given the relatively generous social protection system, expected demographic developments are among the most formidable obstacles to the sustainability of the European growth model.** With employment protection high, social benefits generous, and reduced mobility, the most important methods for improving workforce productivity in the long term are to increase labor market participation and both develop skills and attract talent, meanwhile ensuring fiscal sustainability.

**Figure 17. Labor Productivity and Labor Costs per Hour, 2009**

<!-- Start of picture text --> 200.0<br>Luxembourg<br>180.0<br>160.0 Ireland<br>140.0<br>120.0<br>100.0<br>Finland Netherlands Sweden<br>Denmark<br>80.0 Austria<br>United Kingdom Belgium<br>France<br>60.0 Spain Germany<br>Italy<br>40.0 Greece<br>Slovenia<br>Slovakia<br>20.0 Lithuania Poland Hungary Estonia Czech Republic Portugal<br>Romania Latvia<br>Bulgaria<br>0.0<br>0 5 10 15 20 25 30 35 40<br>Labor cost per hour (euro)<br>Labor productivity<br><!-- End of picture text --> _Source:_ World Bank and Eurostat

**Together the expected shrinkage of the working-age population and the dramatic rise in the proportion of the population above age 65 pose a serious threat to the European growth model.**<sup>16</sup> The median age in EU11, which is currently comparable with the rest of Europe, is expected to increase 6.8 years on average by 2050 (Figure 18). In the rest of the EU the increase is expected to be 5.8 years.<sup>17</sup> The old-age dependency ratio in the EU11 will deteriorate with the share of the elderly in the population reaching 33 percent in 2060 from 16 percent in 2010. Currently, there are about 5 persons of working

> 16 The demographic prospects of economies in Central and Eastern Europe were discussed extensively in World Bank (2007). See Annex 2 for more detail.

> 17 These figures are based on the median scenario of the UN World Population Projections.

19
financial institutions and another 30% stated it had been “somewhat important”. Only one PCR, notably that of Mexico, was deemed “of little importance” for supervision; Argentina and Brazil both indicated that the registry was “very important” for banking supervision.<sup>17</sup> Argentina, Brazil and Mexico all view bank supervisors as the primary users of PCR data.

Regulators overwhelmingly report that they look at the financial institutions’ use of credit reports in lending as part of the supervision process (53/57) and most use PCR data to support both their on-site (53/57) and off-site (49/57) supervision responsibilities. More than 80% (47/57) use PCR-data to analyze trends in banking and credit. However, only about 30% (17/57) report that they use statistical models to analyze PCR data; Argentina and Mexico are among this group of 30 but Brazil is not. The most frequent use of public registry data is to calculate the total indebtedness of borrowers with the financial system. A simple summation by borrower across institutions is the most important application for the rich datasets found in public registries for 30 of the 57 PCRs in the survey.<sup>18</sup> Supervisors also indicated the importance of the ratings in PCRs as a supervisory tool. Ratings are often used to support more forward-looking provisioning policies. Rather than relying solely on information on a loan’s past-due status, regulators may require banks to assign a more forward-looking “rating”. The rating is typically assigned by the lender to either the borrower (the most common) or to each line of credit according to a common scale determined by the regulator. Ratings may depend on arrears and cash-flow projections and estimated probabilities of repayment to obtain a more forward-looking measure.<sup>19</sup> Consistency rules between the ratings of different lenders to the same borrower are often established e.g.; two lenders cannot input ratings that differ by say more than one point on the scale otherwise the system rejects both. Depending on the sophistication of the system, including ratings will require a higher degree of supervisory oversight to ensure that ‘ratings’ are consistent and to resolve disputes. Nearly 60% (33/57) of the PCRs surveyed, collect rating information including all Latin American countries

> 17 These questions were not asked in the subsequent 2002 PCR survey.

> 18 Other answers to the question on the most important supervisory use of PCR data included: to track conglomerate borrowing (3); for use in calculating provisions (6); to identify discrepancies between institutions in risk ratings of borrowers (5); for information on the status of credits (7); and other (2).

17
percent. In contrast, we find small and insignificant coefficients on the interactions with the value of physical stock and household dwelling, and acres of land. We might expect that livestock value exhibits the greatest buffering behavior, since livestock is a liquid asset, as compared with land holdings, owned dwellings, and other forms of assets. These results suggest that older adults, where they have livestock, can use it to compensate for having to work more in the face of a death shock.

# **4.4. Results for health indicators**

The focus thus far has been on the burden of prime-age mortality on older adults with respect to having to work more hours. In this section, we extend the focus to look at various measures of well-being, mainly individual health indicators and household food consumption. Our health indicators are BMI, acute illness, chronic illness, days of restricted activity and days of no activity. We report the results in Table 8. Changes in BMI over the course of the panel are not significantly different for elderly individuals experiencing adult death in their households or among their non-resident biological children (column 1). This lack of response holds for other health indicators as well, namely chronic illness, and days of restricted and no activity (columns 4, 5 and 6). These results are consistent with prior work with the short run panel from the same data set. Ainsworth and Dayton (2003) find--using the first four years of the same panel--that elderly individuals’ BMI returns to its original level within one year of an adult death.

On the other hand, column 2 of Table 8 shows that per capita consumption of the household is actually significantly greater for individuals experiencing a co-resident death in the near past (0-4 years ago). One possible explanation for this finding is that care giving to co-resident adults may have been drawing away funds from the elderly individual’s own consumption, especially when the adult is very sick. When death in the household occurs, consumption of older adults may actually increase in the short run, due to the freeing up of these resources. An alternative explanation is that there is surplus labor.

Column 3 reports results for changes in acute illness status. We find that both coresident and non-co-resident adult deaths are associated with increases in the probability of acute illness for deaths occurring in the more distant past. This finding seems to concur 22