rafmacalaba/data-use-mentions-extended huggingface dataset:
- _4.3 Country-specific poverty estimates in LAC in 2011 PPPs and comparison with currentlyused poverty rates in 2005 PPPs_

It is also key to analyze whether the new poverty rates in LAC countries differ considerably with respect to the current country-specific poverty rates based on 2005 PPPs. In this section we select a poverty line in Table 3 as an example to illustrate the value of the poverty rates in LAC countries. As we showed in Section 4.1, the difference in the number of subnational poverty lines by country, the presence of outliers, and the size of the subnational population have a material effect on the value of the poverty line. Therefore, the following analysis uses the extreme and moderate poverty lines that result from weighting all subnational poverty lines by the number of individuals residing in their corresponding urban subnational region to account for the size of the population and control for the different number of subnational poverty lines across countries. The analysis also considers the specification based on the _median_ of subnational urban poverty lines to account for outliers. The corresponding extreme and moderate poverty lines that result from those controls are $2.5 and $5.3 per person per day, respectively (first row of columns 3 and 4 of Table 3). This is the most conservative set of updated regional poverty lines. Therefore, poverty rates that emerge from these two lines can be seen as the lower bound of the changes resulting from updating the PPPs.

Table 4 presents the country-specific poverty estimates in 2013 based on these selected extreme and moderate poverty lines and the 2011 PPPs. Poverty headcounts vary considerably across countries in LAC. For instance, Chile has the lowest extreme and moderate poverty rate in LAC, with approximately 1.4 percent and 8.0 percent of the population living with a per capita income lower than $2.5 and $5.3 per person per day in 2013, respectively. On the other extreme, Honduras has the highest extreme and moderate poverty rate; approximately 27.3 percent and 56.6
The willingness of suppliers to extend credit to their customers, according to RPED survey respondents, depends on the risk of nonpayment (Biggs and Srivastava 1996). Given the inadequacy of courts, risk of nonpayment depends on the amount of information suppliers have about customers and the ability to enforce the contract informally. Thus, a customer looking for credit has to establish a trust-based relationship with the supplier either by way of long-term repeat interactions or by way of connections. Public information about credit histories of firms, which could alleviate some of the concerns about information and enforcement problems, is unavailable in most African countries, and financial institutions and other firms are generally unwilling to share this type of information because it is a source of rents. So firms must establish a relationship with each potential source of supplier credit, as we discussed earlier.

Networks overcome many of these problems by sharing information about credit histories within the group and enforcing contracts within the group. But the consequences of these network externalities are that non-members are left to the long process of getting credit via repeated interaction or not getting supplier credit at all.

As upstream industries in many SSA countries are controlled by minority ethnic networks, it is the downstream small and medium indigenous-African producers that are excluded by network effects in most cases. They have few connections to these minority communities and no equivalent multilateral reputation mechanism that facilitates the sharing of credit histories and enforcement. Minority ethnic suppliers also find it hard to differentiate between indigenous-African firms, as their payment records do not travel across ethnic boundaries. Indigenous-African SMEs are therefore subjected to statistical discrimination: they are all placed in the same high risk category by upstream suppliers. In addition, as we noted above, minority suppliers are apprehensive about enforcing contracts in the indigenous-African community because of political concerns. They also know that when the time comes to make the inevitable decisions about contract flexibility, it will be difficult for them to get enough information on indigenous-African firms to sort out late payers with legitimate business problems from late payers just being opportunistic.

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**Forced displacement indicators.** Our indicator of displacement status at the household level equals one if a household is an IDP household. Similarly, the displacement status indicator at the individual level equals one for individuals in IDP households. Indicators of displacement _experiences_ include a range of displacement characteristics, such as displacement date, number of times the household has been displaced, reason for displacement, and livelihood and education before displacement.

**Violence indicators.** Our indicator of exposure to violence equals one if an individual or household resides in an area where violent events occurred in the 12 months prior to the study. We prefer a binary indicator because there is a significant number of enumeration areas, in which no violent events occurred, it helps to deal with outliers with a high number of events, and it reduces issues of measurement error. For rural areas, we use an indicator of local violence at the LGA level, while for urban areas we use an indicator at the more fine-grained ward level. The reason is that the underlying contours of violence differ across rural and urban areas. The rural areas are very large compared to the urban areas and were characterized by relatively low levels of conflict intensity (compared to urban areas). Urban and peri-urban areas are much denser and were predominantly characterized by high-intensity violence that was highly concentrated spatially. We thus opted to choose the ward level in urban areas and the LGA level in rural areas as the appropriate administrative units of analysis.

# **3.3 Econometric specifications**

For our baseline estimates of additive and interactive impacts of gender and displacement status (discussed in Section 5), we use linear fixed-effect models that estimate the coefficients of interest, βand δ, by OLS regression: Here, Y is a coping behavior variable, D is the female indicator; E is the displacement status indicator, X is a flexible vector of control variables (including local fixed effects); and ɛ is the error term. In our main specifications, we cluster our
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**Figure 1: Contrast of IELFS 2019–20 and AWMS sociodemographic variables, various rounds**

<!-- Start of picture text --> IE-LFS 2019/20 AWMS - Round 3<br>80<br>54.6<br>70 49.8<br>60<br>50<br>25.1<br>40 17.4 20.0 20.3 20.6 21.7 17.4 21.9<br>30 13.8 13.9 8.4<br>20<br>10 25.0 20.0 20.0 20.0 20.0 20.0 67.0 9.4 15.2 7.8 49.0 20.2 7.3<br>0<br>Location Household welfare Household head education Individual characteristics<br>Urban share Poorest* Quintile 2 Quintile 3 Quintile 4 Richest Uneducated * Primary schooling * Secondary schooling Tertiary schooling * Female share Average Age* Members*<br><!-- End of picture text -->

**Note:** An asterisk (*) indicates that differences in means are significant at 1%. AWMS R2 only collected information on the household head. **Source:** Elaboration based on IELFS 2019–20 and AWMS round.

# The SWIFT-plus approach

Over the past decade, significant advances have been made in the research on survey-to-survey imputation techniques. Lack of household survey data during or after a crisis is not unique to Afghanistan. Not surprisingly, the research on how to fill knowledge gaps on welfare has been growing considerably over the past decade in response to a growing need for real-time welfare monitoring data and the cost and time challenges involved in implementing standard household expenditure surveys. The Survey of Well-Being via Instant and Frequent Tracking (SWIFT) methodology (Yoshida et al., 2015) provides poverty estimates in situations where no expenditure and consumption surveys are available.

SWIFT-plus
**Table 8: Supply of rice to the Sierra Leone market (2009 – 2018)**

|**Year**|**Total Supply**|**Domestic**<br>**Production**|**Imp**|**orts**<br>|
|---|---|---|---|---|
||_Volume(Mt.)_|_Volume(Mt.)_|_Volume(Mt.)_|_Value(US$’000)_|
|2009|698,840|592,570|106,270|45,169.00|
|2010|685,569|616,003|103,500|43,950.00|
|2011|833,011|677,603|155,408|84,726.48|
|2012|941,906|684,850|257,056|111,395.54|
|2013|1,085,819|753,335|332,484|129,385.30|
|2014|897,066|489,908|407,158|120,719.50|
|2015|917,560|597,216|320,344|141,490.03|
|2016|1,029,709|695,255|334,454|96,886.67|
|2017|1,149,547|538,241|611,306|191,380.19|
|2018|959,513|551,751|407,762|120,083.47|

Source: Based on data from Ministry of Agriculture and Food Security and Bank of Sierra Leone

# _Rice price_

Government's policies with respect to the incentive framework have had a serious negative effect on agricultural production. The overvalued leone imposed low producer prices for rice, since imports
5. **Colombian citizens express concern about high inequality and believe that it is the role of the state to address it.** According to the 2010 Latinobarametro survey, 32.2 percent of Colombians find that the distribution of income is „very unfair‟, while 52.5 percent find it „unfair‟. In the 2010 LAPOP survey, 45.5 percent of respondents agreed „very much‟ with the statement that „the Colombian state should implement firm policies to reduce inequality‟, while 25.9 percent „agreed‟. The latter survey also reveals that there is willingness to pay more taxes to give more to those who have less (42 percent)<sup>5</sup> . All else equal, Colombians who are relatively better off economically, those aged 50 and below, those with better education, and those whose ideologies align with state intervention express higher support for paying more in taxes to help the poor (Plata, 2011).

6. **The Santos Administration is aware of the country’s high level of income inequality and has set targets and introduced policy measures to address it.** Colombia‟s National Development Plan 2010-14 targets a reduction of 1 percentage point in the Gini coefficient per year (from 0.58 to 0.54)<sup>6</sup> . The Plan does not explicitly explain how to reach this goal aside from highlighting the importance of necessary conditions such as economic growth and improving the equality of opportunities. Nonetheless, the Santos Administration has taken several steps which may well have a positive impact on inequality in the medium term. The recently approved Victim‟s Law is likely to improve land distribution. A tax reform, approved in 2010, enhanced the rate and base of the wealth tax paid by corporations and individuals. The Government is committed to providing universal and equal access to health while maintaining fiscal sustainability. Finally, a recent reform of the system for royalties from natural resource production has improved
urban populations. This distinction is of interested, as there are significant gaps between urban and rural areas in El Salvador (Robayo-Abril and Barroso, 2022). In addition, we estimate vulnerability to poverty for all of the 12 departments included in the household survey. This analysis might generate important insights on where investments in resilience and adaptive responses to shocks are most needed.

**As the model relies on several assumptions, our findings are subject to a number of caveats.** First, the model assumes that household consumption/income variation over time can be proxied by the variation in household consumption/income observed for households with the same characteristics. Moreover, it assumes that there is no measurement error in the measurement of consumption/income.<sup>13</sup> The downside of the approach is that it cannot identify the effects of specific shocks, like a drought or flood. Moreover, the vulnerability tool applied in this paper falls short of identifying which exact shocks are the specific drivers behind vulnerability in the country.

# **5. Results**

**Table 2 shows the results of the multilevel mixed-effects estimation.**<sup>**14**</sup> Nearly all variables besides the occupation rate, share of employed in the public sector, and child assistance rate at the municipality level are significant at the 1-percent level. The unemployment rate, being female, self-employment, the number of children, informality, the number of household members out of the labor force, and perceived security, as well as environmental damages, have a negative correlation with per capita household income. The share of self-employed entrepreneurs, human capital accumulation, working in the public sector, and having a financial account positively influence the per capita household income. The number of children attending school is also positively correlated with per capita household income. Higher exposure to crime goes hand in hand with higher per capita household income. This could be due to those with more income being more exposed to
# **I INTRODUCTION**

Migration is a divisive issue. Anti-immigrant sentiments are widespread and recent opinion surveys suggest that natives worldwide have become less accepting of migrants.<sup>1</sup> Prejudice against migrants can preclude integration and lead to their social exclusion, inducing large economic and social costs in hosting societies. It is necessary to understand how natives alter their attitudes towards migrants in response to different stimuli in order to design policies and programs more effectively.

We examine how crises affect native altruism and attitudes towards migrants. When facing a common threat, natives may feel solidarity or even kinship with migrants in the same situation, or with society as a whole. We call these positive responses the _empathy channel_ . Alternatively, a crisis could increase competition for scarce resources and trigger native resentment towards migrants, a response we call the _resentment channel_ .

We also study whether individuals between 18 and 25 years old respond differently to migrants during crises. According to a vast literature in social psychology and economics, core attitudes, beliefs, and values are formed mostly during early adulthood and change only slowly after this critical period. The theory of the _impressionable years hypothesis_ posits that people 18 to 25 years old have superior mental plasticity. For instance, several studies have shown that the historical and economic contexts of this time shape basic attitudes, values, and world views (e.g., Greenstein, 1965; Easton et al., 1969; Cutler, 1974; Sears, 1983; Giuliano and Spilimbergo, 2014; Torney-Purta, 2017;). Glenn (1980) and Spear (2000) have proposed scientific explanations for this phenomenon. Glenn (1980) suggests that people are flexible in reacting to social circumstances when they are young but become gradually less so as they age. This decrease in flexibility is due to a drop in energy, loss of brain tissue, disengagement, and less interest in events distant from one’s immediate life, as well as to the accumulation of friends with similar world views
|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Each column corresponds to a separate OLS regression using a sample of communities within 30 kilometers of the former homeland borders (on both sides). The dependent variable is the difference in log per capita income for 20111996. The share of unconditional employment in mining for 1996 is defined as the number of individuals employed in mining over total population. 1996 controls include: linear distance to former homeland border, share of white, colored and Indian/Asian population, share by age group (0-9, 10-19, 20-39, 40-59, 60+), household head education, share employed, share male, per capita income, total population, share connected to electricity grid, share no refuse, share with piped water. 2011 controls include: same as 1996 except for per capita income (which is excluded). All control variables are interacted with the homeland indicator. Regressions are weighted by community population in 1996. Heteroscedasticity robust standard errors in brackets clustered at the municipality-homeland level. ***p<0.01, **p<0.05, *p<0.1
functions on the grounds that we are dealing with very finely disaggregated trade data for which relatively high elasticities of substitution might be expected.

While the analysis in the earlier sections of this paper was undertaken for agriculture and for industrial products separately, in line with the negotiations at WTO, this approach would not be satisfactory for our welfare analysis. Second-best effects associated with reducing one set of tariffs, while leaving another set unchanged, might overwhelm the welfare effects of interest to us—those associated with different degrees of progressiveness in the reduction of high tariffs<sup>7</sup> . Finally, the goal of this section is primarily illustration. Therefore, we repeated the analysis using the extended Swiss formulas to reduce all tariffs in each sample country.

The tariff schedules of USA, and the European Union have been analyzed for the case of a 50 percent reduction in simple average tariffs. These results are presented in Table 5 for the three different values of the _b_ parameter identified in the earlier tables. The gains are, as might be expected, very small, given the very low levels of the _ad valorem_ tariffs included in the database. As might also be expected, the gains from reform for the United States rise as the tariff reduction formulas are made more strongly tops-down. However, the differences between the gains with different degrees of flexibility are quite small. A similar pattern emerges for the European Union. In both cases, moving from the standard Swiss formula to a Swiss formula with a flexibility parameter of 1.5 causes only a very small reduction in the welfare gains from liberalization.

What do these results suggest? The results are of course very partial, reflecting only the efficiency gains from own-country liberalization. Further, they are based on tariff information that excludes the effects of specific, compound and mixed tariffs, and other charges such as antidumping duties. However, they are based on very widely dispersed real-world tariff data collected at a fine level of disaggregation, and probably do have some relevance for inferences about
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growth_**

How important is product market competition in stimulating innovation-driven growth? We explore this important policy question at two levels given our available data: the first based on firm-level proxies for the intensity of active competition; and the second based on nationallevel assessments of the openness of the business environment to competition.

# **_Firm-level intensity of competition variables_**

We test whether two measures of firm-level competition—the self-reported number of competitors faced by a firm and whether the firm faces a foreign competitor—are correlated with R&D investment, product and process innovation, TFP and employment growth. Unfortunately, however, these variables were not collected in all the country surveys that comprise our full sample. Accordingly, we restrict our analysis of these firm-level competition variables to the sample of firms for which they are available, constituting less than 50 per cent of our full allcountry sample. We do not find any statistically significant effects at the level of the enterprise in any of the four firm-level R&D, innovation, TFP and employment growth equations for either the self-reported number of competitors, the log of this measure or an indicator of the presence 22
Figure 8: All-India Total Factor Productivity: By Firm Size <!-- Start of picture text --> All−India Total Factor Productivity All−India Total Factor Productivity<br>For firms with fewer than 20 employees For firms with between 20 and 100 employees<br>Year Year<br>Aggregate Average Aggregate Average<br>Reallocation Reallocation<br>(a) Firms with Fewer than 20 Employees (b) Firms with 20-99 Employees<br>All−India Total Factor Productivity<br>For firms with more than 100 employees<br>Year<br>Aggregate Average<br>Reallocation<br>19851986198719881989199019911992199319941995199619971998199920002001200220032004 19851986198719881989199019911992199319941995199619971998199920002001200220032004<br>19851986198719881989199019911992199319941995199619971998199920002001200220032004<br>.2 .2<br>.1<br>.1<br>0<br>TFP TFP<br>0<br>−.1<br>−.2 −.1<br>.2<br>.15<br>.1<br>TFP<br>.05<br>0<br>−.05<br><!-- End of picture text --> (c) Firms with 100 or More Employees Total factor productivity (TFP) decompositions for the population of firms, divided by firm size, conducted at the state-industry level, using the OP method. “Aggregate” indicates market-share weighted mean productivity, “Average” indicates unweighted mean productivity, and “Reallocation” indicates the covariance between market share and productivity. Source: Authors’ calculations based on Annual Survey of Industries data.

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