rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 4:** **_Poverty and Banking Crises_**

This table shows changes in absolute poverty (increase in the fraction of the population on less than the international $1 a day benchmark) distinguishing between the parts attributable to mean income changes to distributional shifts in intervals for which data is <u>available and which span the start of a banking crisis</u>

|||||Change in<br>Di|stribution<br>||
|---|---|---|---|---|---|---|
|||Dater|ange|poverty|factor|ChangeinGini|
|DZA|Algeria|1988|1995|1.0|0.6|-0.7|
|ARG|Argentina|1992|1998|2.3|1.6|0.7|
|AZE|Azerbaijan|1995|2001|-5.5|-1.5|0.3|
|BGD|Bangladesh|1985|1988|13.1|3.1|0.6|
|BOL|<br>Bolivia|1990|1999|2.9|5.3|0.3|
|BRA|Brazil|1989|1993|-1.9|-2.2|-0.5|
|BRA|Brazil|1993|1995|-3.8|-1.3|-0.8|
|BGR|Bulgaria|1995|2001|3.1|2.0|0.6|
|BDI|Burundi|1992|1998|8.8|5.0|1.5|
|CHN|China|1994|1995|-7.0|-2.1|-1.9|
|CRI|Costa Rica|1993|1996|-0.8|0.5|0.3|
|CIV|Cote d'Ivoire|1988|1993|2.1|-0.9|0.0|
|CZE|Czech Republic|1988|1993|0.0|0.0|1.4|
|EGY|<br>Egypt|1991|1999|-0.9|-0.4|0.3|
|SLV|El Salvador|1989|1995|-2.0|-1.5|0.2|
|EST|Estonia|1988|1993|3.5|0.8|3.3|
|GHA|Ghana|1997|1999|-61.5|-2.7|-1.4|
|HUN|Hungary|1989|1993|0.0|0.0|1.2|
|IDN|Indonesia|1996|1999|5.5|-3.5|-1.6|
|JAM|Jamaica|1996|2000|-1.8|1.5|0.4|
|KEN|Kenya|1992|1994|-7.0|-14.7|-6.5|
|KOR|Korea|1993|1998|-19.9|-16.1|-0.6|
|KGZ|Kyrgyz Republic|1993|1997|-0.2|0.0|-3.3|
|LVA|<br>Latvia|1995|1998|0.4|0.4|1.3|
|LTU|Lithuania|1994|1996|-2.2|-0.8|4.8|
|MDG|Madagascar|1980|1993|11.2|-3.4|-0.3|
|MEX|Mexico|1989|1995|1.1|-1.6|-0.2|
|PHL|Philippines|1997|2000|-9.0|0.4|-0.2|
|POL|Poland|1992|1993|2.6|1.8|-4.0|
|ROM|Romania|1989|1992|0.8|0.2|0.7|
|RUS|Russia|1993|1996|0.9|0.3|1.5|
|SVK|Slovakia|1988|1992|0.0|0.0|0.0|
|LKA|Sri Lanka|1985|1990|-4.7|-2.6|-0.5|
|THA|Thailand|1981|1988|-7.9|-2.8|-0.2|
|THA|Thailand|1996|1998|0.3|-0.1|-1.0|
|UGA|Uganda|1992|1996|-14.4|-1.9|-0.4|
|UKR|Ukraine|1996|1999|2.0|0.0|-1.2|
|VEN|Venezuela|1993|1995|5.5|3.5|2.6|
|VNM|Vietnam|1993|1998|-9.7|-0.8|0.3|
|YEM|Yemen|1992|1998|2.9|-2.4|-2.9|
|ZMB|Zambia|1993|1998|-5.2|5.6|1.3|

Note: Shows all spells from Kraay (2004) that span the start date of a banking crisis shown in Caprio et al. (2004). Distribution factor the change in poverty (percentage points) attributable to actual changes in Lorenz curve, holding mean constant.

21
The cuts in bound tariffs are to be implemented in equal annual cuts over 5 years in the industrial countries and over 11 years in developing countries. As shown in Table 1, the cuts are smaller for developing countries than for the industrial countries. This table also shows the auxiliary conditions that apply to the tariff cuts. If the cuts, together with any exceptions permitted, result in average-cuts of less than 54 percent in the industrial countries, then all of the cuts must be scaled up proportionately. If the average-cut exceeds 36 percent in a developing country, the cuts may be proportionately reduced in size.

**Table 1 Formula coefficients used in the Agricultural Negotiations**

||**Developed c**|**ountries**|**Developing c**|**ountries**|
|---|---|---|---|---|
|_Band_|_Range, %_|_% Cut_|_Range, %_|_% Cut_|
|A|t0≤ 20|50|t0≤ 30|33.3|
|B|20 < t0≤ 50|57|30 < t0≤ 80|38|
|C|50 < t0≤ 75|64|80 < t0≤ 130|42.7|
|||70|t0>130||
|D|t0>75|||46.7|
|**Average cut**|**Min**|**54%**|**Max**|**36%**|

Source: Laborde and Martin (2011a) To avoid the problems of snowballing exclusions encountered in the Kennedy and Tokyo Rounds of negotiations, the Doha negotiations have attempted to negotiate a set of rules for managing exceptions. Members would be free to make smaller cuts—subject to agreed constraints—on a certain number of agricultural tariff lines. By focusing attention on the defensive concerns of individual countries, this approach appeared to encourage a strong focus 22
Berry, D., & O'Connor, E. (2010). Behavioral risk, teacher–child relationships, and social skill development across middle childhood: A child-by-environment analysis of change. _Journal of Applied Developmental Psychology_ , _31_ (1), 1-14.

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<mark>Boakes, N. (2009). The impact of origami-mathematics lessons on achievement and spatial ability of middle-school students. In</mark> _<mark>Origami4: Fourth International Meeting of Origami Science, Mathematics, and Education</mark>_ <mark>(pp. 471-481).</mark>

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Bosworth, K., & Judkins, M. (2014). Tapping into the power of school climate to prevent bullying: One application of schoolwide positive behavior interventions and supports. _Theory Into Practice_ , _53_ (4), 300-307.

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<mark>Bowman-Perrott, L., Burke, M. D., Zaini, S., Zhang, N., & Vannest, K. (2016). Promoting positive behavior using the Good Behavior Game: A meta-analysis of single-case research.</mark> _<mark>Journal of Positive Behavior Interventions</mark>_ <mark>,</mark> _<mark>18</mark>_ <mark>(3), 180-190.</mark>

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43
“ _Of course I want to help Venezuelan migrants, we are all migrants in some way, you know? I am just worried about the The backlash could response from my people. political be difficult for my party._ ” (Elected Official in Latin America, March 15, 2021)

# **I INTRODUCTION**

Robust evidence supports a correlation between migration inflows and voter support for anti-migrant political parties.<sup>1</sup> This might explain the reticence and cautionary actions of political parties regarding actions to facilitate migrant integration. Yet, migration reforms related to economic have different effects than integration might migration inflows per se. For example, voters may lack information about regulations concerning migrant integration, may believe those regulations do not affect them or change overall migration inflows into the country, or may support migrants now in the country as long as more migrants do not come because of those regulations.

We examine the electoral effects of the _Programa Especial de Permanencia_ (PEP) in Colombia. The PEP was a temporary economic integration program for undocumented forced migrants. It was known regionally as a type of regularization program. It was offered to half a million Venezuelans in Colombia for to two The approximately living up years.<sup>2</sup> PEP granted working rights and access to public programs, such as full health and education services, plus access to financial services. It was offered to every Venezuelan in Colombia who registered in a census of irregular migrants between April and June 2018. As documented by Urbina et al. (2023) and Ib ́a ̃nez et al. (2024), the PEP program affected profoundly the well-being of treated Venezuelan migrants through improvements in their

> 1See, for example, Gerdes and Wadensj ̈o (2008); Otto and Steinhardt (2014); Mendez and Cutillas (2014); Barone et al. (2016); Harmon (2017); Halla et al. (2017); Dustmann et al. (2016). In one notable exception regarding the Global South by Zhou and Grossman (2021), the authors show migration inflows are related to greater support for the incumbent candidate since they elicited significant international aid.

> 2More than 5.6 million refugees have fled Venezuela’s economic and humanitarian crises (UNHCR 2023). Colombia is the most common destination of Venezuelan refugees; by mid-2022, it hosted almost 2.5 million such migrants. Moreover, Colombia has committed to policies that promote the rapid social and economic integration of migrants.

4
trading partners that are targeted by antidumping activity as a result of implemented PTAs. They report that PTAs decrease the incidence of bilateral antidumping filings between members by 33-55 percent, and PTAs increase the incidence of bilateral antidumping filings against non-members by 10-30 percent. The results are more sizable for PTAs that also contain specific antidumping provisions.

These results are somewhat different from an earlier and more narrowly defined empirical analysis by Blonigen (2005) that was designed to examine whether NAFTA was reining in United States antidumping and countervailing duty activity against Canada and Mexico. That research examined data from 1980-2000 and found little evidence that NAFTA increased import volumes or that the new provisions in the PTA – i.e., NAFTA’s Chapter 19, in which partners could use a special dispute settlement process to challenge each other’s use of antidumping and countervailing duties – affected the frequency of US antidumping initiations or US government decisions in cases against Canada and Mexico.

Despite empirical progress in this line of research, there are still a number of unsettled issues regarding the potential role of other non-PTA factors, which may be confounded with the rise of PTAs. One example can be inferred from the results described earlier; the Estevadeordal, Freund, and Ornelas (2008) results remind us that much of the liberalization taking place during the 1990s was not only through the implementation of PTAs but also through cuts to applied MFN tariffs. Distinguishing the impact on TTBs arising from PTA liberalization from the impact arising from multilateral liberalization is likely to be an important, albeit difficult task for future informative research.

A second and related example of the last twenty years that has also undoubtedly given rise to a substantial amount of TTB use has been China’s export growth and increasing competitiveness in global markets. Even before its WTO accession in 2001, China had become the top foreign target of many highincome and emerging economy TTB users (Messerlin 2004; Bown 2010).<sup>13</sup> Indeed, a comparison of

> 13 A similar phenomenon took place during 1955-1970 for Japan, a major economy that undertook a similar, exportled growth country strategy during the period that it was newly acceding to the GATT. During that period, a number of existing GATT Contracting Parties invoked Article XXXV to not apply the agreement to Japan.

13
Table 1a: Population coverage by data source and region (%)

|Region|Surveys|NSO|Phone<br>surveys|Literature|Sectoral<br>growth|Nationa<br>lgrowth|Regional<br>average|
|---|---|---|---|---|---|---|---|
|East Asia & Pacific|15|72|7|0|0|2|3|
|Europe & Central Asia|23|7|14|43|12|1|0|
|Latin America &||||||||
|Caribbean|78|0|5|0|4|7|6|
|Middle East & North||||||||
|Africa|0|0|4|18|51|15|12|
|North America|0|100|0|0|0|0|0|
|South Asia|0|0|0|74|24|0|2|
|Sub-Saharan Africa|0|0|59|21|18|1|0|
|World|14|28|13|27|13|2|3|

Table 1b: Country coverage by data source and region (number of countries)

|Region|Surveys|NSO|Phone<br>survey<br>s|Literature|Sectoral<br>growth|Nationa<br>l growth|Regional<br>average|
|---|---|---|---|---|---|---|---|
|East Asia & Pacific|2|5|5|0|5|8|13|
|Europe & Central Asia|6|1|7|21|12|2|9|
|Latin America &||||||||
|Caribbean|12|0|3|0|5|4|18|
|Middle East & North||||||||
|Africa|0|0|2|1|7|5|6|
|North America|0|2|0|0|0|0|1|
|South Asia|0|0|0|1|6|0|1|
|Sub-Saharan Africa|0|0|20|3|21|2|2|
|World|20|8|37|26|56|21|50|

_Note:_ This table reports the share of regional population (panel a) and the number of countries (panel b) covered by the various data sources used to derive the 2020 welfare distribution. The sources are ordered with the most preferred on the left to the least preferred on the right.

8
and the average level of EXT across the business functions of a firm is concave, with the maximum predicted productivity being attained when firms reach a level of EXT of 4 that corresponds to the 95<sup>_th_</sup> percentile of the distribution of average EXT across firms. Column 3 splits EXT between MOST and GAP. We find that both are positively associated with firm-productivity and that the relationship between MOST and productivity is quadratic. In regressions not reported in the table we find that the coefficient on the quadratic term of GAP is insignificant.

Table 8: Productivity and Technology Sophistication

|VARIABLES|(1)<br>ln(VAPW)|(2)<br>ln(VAPW)|(3)<br>ln(VAPW)|(4)<br>ln(VAPW)|(5)<br>ln(VAPW)|(6)<br>ln(VAPW)|(7)<br>ln(VAPW)|(8)<br>ln(VAPW)|
|---|---|---|---|---|---|---|---|---|
|ABF EXT|0.64***|2.51***||2.14***|||2.04***||
||(0.07)|(0.33)||(0.37)|||(0.36)||
|ABF EXT<sup>2</sup>||-0.32***||-0.27***|||-0.22***||
|||(0.06)||(0.06)|||(0.06)||
|ABF MOST|||2.67***||2.18***|2.11***||2.27***|
||||(0.48)||(0.49)|(0.48)||(0.47)|
|ABF MOST<sup>2</sup>|||-0.45***||-0.34***|-0.33***||-0.30***|
||||(0.10)||(0.10)|(0.10)||(0.10)|
|ABF GAP|||0.54***||0.45***|0.43***||0.72***|
||||(0.11)||(0.11)|(0.11)||(0.13)|
|Var(ABF EXT)||||0.24**|0.34***|0.72***|1.05**|1.44***|
|||||(0.11)|(0.10)|(0.27)|(0.42)|(0.38)|
|Var(ABF EXT)<sup>2</sup>||||||-0.16<br>(0.10)|||
|Var(ABF EXT)*ABF EXT|||||||-0.28**<br>(0.13)|-0.39***<br>(0.12)|
|Firm Characteristics|_~~√~~_|_~~√~~_|_~~√~~_|_~~√~~_|_~~√~~_|_~~√~~_|_~~√~~_|_~~√~~_|
|Observations|2,746|2,746|2,746|2,746|2,746|2,746|2,746|2,746|
|R-squared|0.49|0.50|0.49|0.50|0.50|0.50|0.50|0.50|

Note: Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. The regressions controlled for observable firm characteristics including size (i.e., small, medium, and large), sector (i.e., agriculture crops, livestock, food processing, wearing apparel, pharmaceutical, moto-vehicle, wholesale and retail, finance, land transport, hospital, other manufacturing, and other services), age (i.e., 0-5, 6-10, 11-15, and 16 or more), export, and multinational status.

Next, we move beyond firm-level averages of technology across business functions, and explore the relationship between the within-firm variance of technology and firm productivity. Columns 4 and 5 report a positive conditional association of the within-firm variance in the EXT measure (across all business functions) and firm productivity.<sup>43</sup><sup>_,_44</sup> Column 6 through 9 further explore the conditional relationship between firm productivity and within-firm variance. Column 6 includes the quadratic term of within-firm variance in technology and finds

> 43This finding is distinct from the positive association between within-firm variance and the average technology level, as regression (6) includes measures of the average technology (in MOST and GAP) as controls.

> 44We obtain similar findings if instead of the within-firm variance of EXT, we include the within-firm variance of MOST or GAP.

33
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10

# **3-** **_Data and Sample Truncations_**

The data for this study are based on the 2005-06 HICES conducted by the Ethiopian government’s Statistical Authority, consisting of over 21,299 households, and covering the entire country. The samples employed in this study, however, are smaller. The sample selection path I have employed is a result of two main types of truncations. By far the most important is the restriction of the samples to nuclear families. The definition of a nuclear household I employ for sample truncation is household head and the spouse and at least one child under 15 years of age; thus household containing children above that age are excluded. Table 1 shows the percentages for different types of households. Nuclear households, with at least one child, account for 52 % of the total. However, about a third of such nuclear households, accounting for a 17% of the entire survey households, are those with non-biological children of the head or spouse. Moreover, The HICES 2005-6 suggests Ethiopia has a significant orphan children problem. The survey defines these as children with dead parents, or children living in households with absent biological parents. Technically, only the former children qualify as orphans, and they constitute about 12% of all children in the sample, increasing to 31% of all children if the broader survey definition of orphan child is adopted. Some studies suggest that parents discriminate among children by their degree of “genetic relatedness”, resulting in non-biological children receiving poorer treatment than biological ones, see Case, et al. (2000). Examination of this question would introduce additional issues into the analysis of child gender inequality best to set aside in a study concerned mainly with a comparison in the treatment of children by gender in a standard biological parents-children set-up. I have therefore confined the sample employed in this study to the first type of nuclear families, i.e. those with biological children of the head and spouse. This leaves out many important issues affecting child gender bias in Ethiopia that can be analyzed within the current framework of nuclear household once the basic evidence is established, but makes the basic task manageable in a study of this type.

Another type of truncation is intended to remove the adverse influences of families with incomplete fertility. The predominance of households in the early phases of their fertility cycle in the sample does not allow sufficient scope to observe the gender bias in fertility behavior; particularly important for this study as marriage at very early
**Table 7:** Rainfall Shock and Household Consumption

||(1)<br>Monthly|(2)<br>(3)<br> Per Capita Consumption (Log)|(4)|(5)<br>Pr(BPL =|(6)<br> 1)|
|---|---|---|---|---|---|
|Positive Shock|-.005|-.006<br>-.007|.042<sup>_∗∗∗_</sup>|.043<sup>_∗∗∗_</sup>|.044<sup>_∗∗∗_</sup>|
||(.016)|(.016)<br>(.016)|(.016)|(.016)|(.016)|
|Zero Shock|.024<sup>_∗∗_</sup>|.023<sup>_∗∗_</sup><br>.021<sup>_∗_</sup>|.009|.009|.010|
||(.011)|(.011)<br>(.011)|(.009)|(.009)|(.009)|
|Positive Shock, Lag1|.056<sup>_∗∗∗_</sup>|.056<sup>_∗∗∗_</sup><br>.055<sup>_∗∗∗_</sup>|.008|.008|.009|
||(.020)|(.020)<br>(.020)|(.017)|(.017)|(.017)|
|Zero Shock, Lag1|.030<sup>_∗_</sup>|.030<sup>_∗_</sup><br>.029<sup>_∗_</sup>|-.003|-.003|-.002|
||(.016)|(.016)<br>(.016)|(.016)|(.016)|(.016)|
|Positive Shock, Lag2|-.012|-.013<br>-.014|.011|.012|.013|
||(.019)|(.019)<br>(.019)|(.017)|(.017)|(.017)|
|Zero Shock, Lag2|.017|.018<br>.016|-.004|-.004|-.004|
||(.012)|(.012)<br>(.012)|(.011)|(.011)|(.011)|
|Observations|224368|224368<br>224368|224368|224368|224368|
|R<sup>2</sup>|.35|.36<br>.36|.23|.23|.23|
|Dep Var Mean|323.88|323.88<br>323.88|.30|.30|.30|

This table shows the impact of rainfall shocks on rural households’ consumption and the likelihood of a household being situated below the poverty line (BPL). The unit of observation is rural households. Positive Shock is a dummy equaling 1 if the district’s annual rainfall incidence falls in the top 20th percentile of the district’s historical rainfall distribution; Zero Shock is a dummy equaling 1 if the district’s annual rainfall incidence falls between the 20th and the 80th percentile of the district’s historical rainfall distribution. Standard errors in parentheses, clustered by district.

48
4 Germany and neighboring countries that SHI began, and where arguably it has become most sophisticated; and it was in the UK and Scandinavia where the tax-financed Beveridge NHS model emerged and has developed most. By contrast, the countries examined by WMS—namely those in Central and Eastern Europe, and Central Asia—contain SHI countries that are still making adjustments to their new systems as well as tax-financed countries that have often struggled with the transition to a market economy following the breakup of the former Soviet Union. One might argue that the higher health spending but similar health outcomes associated with SHI unearthed by WMS could be due to a combination of immaturity on the part of the region’s new SHI systems and fiscal stress among the region’s tax-financed systems.

The present paper differs from the two aforementioned studies in another respect, namely that in its analysis of the impacts of SHI on health outcomes the present paper restricts its attention to causes of death (and age groups) identified by Nolte and McKee (2008) as amenable to medical care, i.e. causes where timely and effective medical care can result in a death being avoided. WMS looked at many more causes of death than are included in the present paper, of which five are among the 33 considered to be amenable to medical care; however, for three of the five causes of death, they used the standardized death rate, which reflects deaths among people aged 75 and over among whom deaths are less easily avoided. So, in addition to focusing on more mature SHI and tax-financed health systems, this paper focuses on causes of death that ought to be amenable both to medical care and to changes in health financing and delivery arrangements. If SHI systems do leave some groups uncovered or covered less well than others, this ought to get reflected in higher death rates from causes amenable to medical care. If SHI systems are better funded, or if they are more efficient in the purchasing and provision of health care, this too ought to get reflected in amenable mortality data.