rafmacalaba/datause-encoder-data huggingface dataset:
Rather than analyzing the impact of migrants arrival on crime rates, we explore how the enactment of a migration reform influenced migrants’ behavior, particularly their willingness to commit and report crimes.

The papers most closely related to our work are Comino et al. (2020), J ́acome (2022), and Pearson (2024), which analyze changes in crime reporting behaviors following programs that enhanced the rights of undocumented migrants in the United States.<sup>6</sup> Consistent with our findings, these studies show that improving rights for undocumented migrants increases their trust in and cooperation with local authorities, leading to higher rates of crime reporting. This effect is partly driven by a reduction in victims’ fear of deportation (Comino et al., 2020 and Pearson, 2024) and greater trust in law enforcement (J ́acome, 2022).

Our study complements this literature in two important ways. First, while we also examine the effects of a regularization program that enhanced undocumented migrants’ rights, our focus is on a developing country context, characterized by high levels of informality, widespread crime, and relatively low deportation rates. Unlike in the United States, undocumented in Colombia are less to fear as migrants likely deportation, they typically work in the informal sector with minimal detection risk. Since over 75 percent of forced migrants reside in developing countries, this context provides a critical perspective and highlights dynamics that may not translate directly from high-income countries.

Second, our analysis emphasizes gender dynamics and provides a more detailed view of the types of crimes affected by these programs—not only aggregated or property crime. We find that women, particularly victims of sexual crimes and domestic violence, are key drivers of the observed increase in crime reporting in developing countries. This insight is essential for understanding the victimization of vulnerable populations and for

> 6Comino et al. (2020) examines the effects of the 1986 migratory amnesty, J ́acome (2022) the effects of the 2015 Priority Enforcement Program, and Pearson (2024) the effects of the 2012 Deferred Action for Early Childhood Arrivals. Another close contribution is Xie and Baumer (2021), who offer a descriptive study using data from the National Crime Victimization Survey.

6
# Appendix A6: Sentiment Analyses with News Media

Sentiment analysis refers to the task of extracting a text’s opinions, whether its general stance or its attitudes towards specific topics or persons. Natural language processing provides three general approaches for computing sentiment: 1) knowledge-based techniques, 2) statistical methods, and 3) hybrid approaches. Knowledge-based techniques categorize words with unambiguous effects, such as ‘good’ and ‘bad’ (Ortony et al., 1988). Statistical methods utilize techniques from machine learning, such as support vector machines and deep learning to classify sentiment (Turney 2002). Finally, hybrid approaches combine elements from the previous two.

In our analysis, we utilize knowledge-based techniques to compute and compare the sentiment of articles covering the GCC and its economic reforms. Specifically, we use the python package ‘polyglot’ for Arabic and English articles. The package provides extensive polarity lexicons—dictionaries classifying words as positive, negative, or neutral—in 136 languages, including Arabic and English. We calculate article k’s ‘support’ by taking the number of positive words minus the number of negative words, scaled by the total word count in the article: =<sup># RppRRppiittiivvRR wwRIIpp−# RIIRRppaattiivvRR wwRIIpp</sup> ppRRIIttiiR IItt<sup>kk</sup> In addition to measuring and analyzing overall sentiment, we also examined the most used charged ttRRttaavv # R wwR IIpp iiII tthRR aaRRttiissvvRR/IIRRssvvR IItt In addition to measuring and analyzing overall sentiment, we also examined the most used charged words—both positive and negative—to understand sentiment drivers. The most used positive words in English articles are similar across GCC countries, with ‘important’, ‘strong,’ ‘stable’, and ‘significant’ being most used. There are minor differences related to the relative frequency of specific words. Some examples include ‘stability’ being relatively larger in Oman, ‘ambitious’ for Saudi Arabia, and ‘sustainable’ for the UAE. However, the overall picture is similar. For the English articles, the most used negative word across most GCC countries is ‘crisis.’ There are notable cross-country differences in other major words. For instance, ‘opposition’ and ‘protests’ are the most used negative words in Bahrain, while ‘debt’ and ‘risk’ are major concerns in Oman, Kuwait, and the UAE. At the same time, Saudi Arabia sees heavier usage of ‘conservative’, ‘corruption’, and ‘terrorism.’ As observed when examining the most common words across the whole text in English articles, for the Arabic articles, the most used positive adjectives to describe reforms are similar across GCC countries. The most common adjectives are ‘new’, ‘wide’, ‘national’, ‘big’, ‘structural’, ‘deep’, ‘comprehensive’, and ‘legal.’ Minor differences include the word ‘competitive’ being relatively larger for articles on Oman, and ‘ambitious’ for those on Saudi Arabia.

45
# **E. SUMMARY OF R_EARCH METHODS**

Duririg the three-week study, the team collaborated<sup>closely with the members of the GOZ</sup> Commission. The team conductece interviews<sup>with officials from various government and</sup> non-governmental agencies, including<sup>the World Bank office and the Programme</sup> d'adjustement des Secteurs Sociaux (PASS)<sup>project in Kinshasa, USAID and the USAID-</sup> sponsored Sante Rural (SANRU) Basic Rural<sup>Health Project, Zaire's Expanded Immunization</sup> Program (EPI), the Cooperation Medicale<sup>Belge au Zaire, Projet Sante Pour Tous, Kinshasa</sup> (SPTK), and the Center for Integrated Development.

The team reviewed data already collected<sup>by the GOZ Commission, in particular the opinion</sup> survey and sur-cy of existing mutual associations. Through this review, four health insurance plans were selected for in-depth<sup>study:</sup> two offered by rural health zones (Bwamanda arnd Bokoro) and two which cover<sup>ambulatory care services in Kinshasa (CASOP,</sup> offered by the Syndicat National UNTZA,<sup>and St. Alphonse, a community-initiated plan</sup> assisted by SPTK and the Catholic Church).<sup>More information about how the case studies</sup> were selected is presented in Section 11.

Members of the team visited Bwamanda and<sup>Bokoro, interviewing medical staff there to</sup> obtain detailed information about organization<sup>and management of the insurance plans,</sup> premiums and enrollment rates, etc. 71 hospital<sup>patients in Bwamanda and Bokoro, and 26</sup> residents in Bokoro Zone were surveyed to<sup>obtain information about satisfaction with the</sup> program, and to compare characteristics<sup>of members and non-members. Financial and</sup> utilization records were reviewed in both zones,<sup>to evaluate evidence of moral hazard and/or</sup> adverse selection, and to measure the effects<sup>of health insurance on the financial condition</sup> of health facilities and the health status of<sup>the population.</sup> In Kinshasa, team members collected descriptive<sup>information and reviewed financial and</sup> utilization data from the facilities in which<sup>these plans were based (St. Alphonse and CASOP).</sup> A 205-patient survey was also conducted. Finally, team members visited several other insurance systems in the Kinshasa area.

To supplement information about formal hcalth<sup>insurance plans, information was gathered</sup> from 22 informal savings associations (uuikelemba'<sup>and wmoziki'), which sometimes come to the</sup> aid of members with catastrophic illness.

7
Madrigal (2008), Perry et al. (2007), Camacho et al. (2009), Maloney (2004, 1999), Maloney and Bosch (2006), Maloney, Goni and Bosch (2007), Kugler and Kugler (2009), Mondragón et al. (2010), have shown that provisions of social protection, notably non-wage costs, incentivize informality. Only a few authors, such as Kugler and Kugler (2009), Mondragón-Velez, Peña and Wills (2010) and Camacho, Conover and Hoyos (2009), conduct econometric analysis of the occupational choice in Colombia, typically as an aggregated indicator in the economy or as a decision within firms and across sectors. To the best of our knowledge, no study analyses these effects either as an individual worker decision or within the context of transition or mobility trends.

This paper analyzes the magnitude, direction and composition of labor transitions in Colombia between 2008 and 2009, using recently available data from the latest two rounds of Fedesarrollo’s Social Longitudinal Survey, FSLS (Fedesarrollo 2008, 2009), a household survey panel data representative of the 13 main metropolitan areas of the country. The paper also provides evidence on the personal and professional characteristics of those who transition across occupations and jobs, including their personal circumstances, motivations, preferences and exposure to and strategies against risks. Finally, the paper explores econometrically how these factors contribute to observed labor transitions. Even though the analysis does not establish causal links between the crisis and labor transitions, results confirm that between 2008 and 2009 there were large and asymmetric transitions among occupations in Colombia. Asymmetric transitions mask different mobility patterns across occupations: formal salaried workers may first try to move to the informal sector rather than transitioning into unemployment or out of the labor force, while an informal self-employed worker may more likely move into unemployment and out of the labor force. This result may be picking up not only period-specific labor dynamics but also a more structural phenomenon whereby workers voluntarily opt into salaried or self-employed occupations. Colombian workers appear relatively insensitive to a set of risks—even economic risks—during the 3
|Urbanization (%)|2.141***|1.306*|1.400*|1.189|
|---|---|---|---|---|
||(0.492)|(0.755)|(0.762)|(0.808)|
|WBL Index|12.840|5.369|6.806|9.124|
||(8.897)|(11.969)|(11.956)|(12.614)|
|Women can work the same night<br>|3.770|-3.051|-2.936|-3.790|
|hours as men Y:1 N:0|||||
||(3.457)|(4.147)|(4.105)|(4.175)|
|Women can work in the same|-0.319|-4.090|-4.243|-3.966|
|industries as men Y:1 N:0|||||
||(2.323)|(2.741)|(2.785)|(3.024)|
|Ratio of Female to Male Population|2.739***|9.206|10.477|9.140|
||(0.712)|(6.852)|(7.043)|(7.425)|
|Fertility Rate|-16.438***|2.041|3.243|4.626|
||(4.520)|(4.315)|(4.408)|(4.740)|
|Starting a Business (DTF)|0.108**|0.056|0.071|0.024|
||(0.046)|(0.063)|(0.064)|(0.079)|
|Inflation (%)|-0.012|-0.084|-0.084|-0.090|
||(0.073)|(0.071)|(0.072)|(0.079)|
|Labor Productivity (in 2009 USD,|-0.316|-0.522*|-0.598**|-0.585*|
|logs)|||||
||(0.256)|(0.292)|(0.299)|(0.303)|
|Quality Certificate Y:1 N:0|0.857|0.768|0.269|-0.042|
||(0.880)|(0.890)|(0.911)|(0.936)|
|Employment Growth (%)|-0.052***|-0.050***|-0.051***|-0.050***|
||(0.016)|(0.018)|(0.017)|(0.018)|
|Legal form of the firm fixed effects||Yes|Yes|Yes|
|||(2.513)|(2.570)|(2.629)|
|Firms' Top Manger is Female Y:1||8.659***|8.694***|8.703***|
|N:0|||||
|||(1.029)|(1.026)|(1.055)|
|Firm Spent on Security Y:1 N:0||0.012|0.011|0.012|
|||(0.007)|(0.008)|(0.008)|
|Firm Has Own Website Y:1 N:0|||1.601*|1.279|
||||(0.854)|(0.857)|
|Firm Provides Training to Workers|||1.266*|1.506*|
|Y:1 N:0|||||
||||(0.751)|(0.771)|
|Polity index||||1.461|

42
**Table 3** : The global middle class is growing, its composition changing (percentage shares)

||200<br>|5<br>|203<br>|0<br>|
|---|---|---|---|---|
||Pop|Income|Pop|Income|
|**World lower class**|82.3|25.9|60.1|14.1|
|**World middle class**|14.3|40.0|30.4|39.3|
|High income countries|9.1|29.8|5.8|10.1|
|Low and middle income countries|5.2|10.2|24.6|29.3|
|Central asia and Eastern Europe|1.2|2.1|3.1|4.0|
|China|0.9|1.6|13.1|16.6|
|East Asia|0.3|0.5|1.6|1.6|
|South Asia|0.0|0.1|1.6|1.2|
|Middle East and North Africa|0.4|0.8|0.9|1.0|
|Sub saharian Africa|0.2|0.4|0.5|0.5|
|Latin America and the Caribbean|2.2|4.8|3.9|4.4|
|**World upper class**|3.4|34.1|9.6|46.6|
|Total|100|100|100|100|

_Source:_ World Bank staff calculations.

These results are due to the fact that per capita income growth in China is forecast to be more than five times the growth in Sub-Saharan Africa, easily offsetting the decline in the former’s population share. The comparison of LAC with South Asia is also quite interesting: while the population share in the developing world remain constant for both of these regions, South Asia is expected to raise its share of middle class member by seven times while LAC is expected to decrease it more than twice. This can be explained by the fact that South Asia GDP per capita almost could triple during the 2005-2030 period while the LAC GDP increases by only 75%.

**Figure 9** : **Composition of the middle class across developing countries (percentage shares)**

<!-- Start of picture text --> Central asia and<br>Eastern Europe<br>China<br>Sub saharian Africa<br>Middle East and<br>North Africa<br>South Asia<br>East Asia<br>Latin America and<br>the Caribbean<br>2005 2030<br>100<br>80<br>60<br>percent<br>40<br>20<br>0<br><!-- End of picture text --> _Source:_ World Bank staff calculations.

23
or other markets. Further, it deals with the consumer market only and does not include investment and intermediate products markets. But as we have shown above, the latter markets are often in disequilibria in CPEs.

Due to space limitations, the following discussion refers only to most recent estimates (Rudlovcak [1987, 1988]; Diouhy [1989]). Both writers found that there had not been any systematic excess demand in the Czechoslovak consumer market in the periods they studied, 1960-1984 and 1975 -1988, respectively. Even though consistent with the official data noted above and intuitively appealing, the findings contradict other indicators, such as "conjuncture tests" and derivatives of savings, which reveal permanent, though not substantial, disequilibrium in the consumer market. These methods are discussed in the next section."<sup>6</sup> Both Rudlovcak [1987, 1988] and Dlouhy [1989] used Houthakker-Taylor demand function that had been previously applied to Czechoslovak savings data by Klaus and Rudlovcak [1977]. The demand function was not a source of differences among various estimates within the disequilibrium framework; it was typically based on household incomes and their savings. Major disputes were concerned principally with specifications of the supply function. Rudlovcak incorporated into the specification of the supply function the household total income and expenditures, which were specified in the government plan, and a proxy variable to reflect the state of equilibrium in the market and the likely response of the planning center. Dlouhy [19891 - reestimating the Portes-Winter model - used their original specification based on the assumption that the planning center responds mainly to fluctuations in gross agriculture production and household money balances.'<sup>7</sup> As noted above, both Dlouhy and Rudlovcak found that there were regimes of excess demand as well as of excess supply. The problem is that periods which were considered by most observers to be years of large shortages, such as years 1969, 1974, and 1978, were modelled as periods with excess supply. The interpretation is, therefore, very difficult and one must use their results with caution. In contrast, the estimates 16Strictly speaking, the findings are not contradictory if the findings based on the disequilibrium model are interpreted as flows while the findings based on 'conjuncture tests" and on savings derivatives as stocks. The latter - implying the presence of rationing systems - could indeed be a permanent feature in markets with no clear trend of suppressed inflation as defined in the text above.

17Dlouhy also reestimated the Burkett model in which he tried to take into account the reservations to the min-condition noted above. Burkett's modified model assumes that the mincondition holds at micro-level and that the aggregation comes afterwards. The impact on results is not substantial.

12
The model estimates in the top panel of Table 2 clearly indicate that lobbying significantly influences the overall NTM coverage ratio. The bottom two panels of Table 2 indicate that price NTMs (PRICE), which comprises a substantial part of the overall NTM coverage, might be driving the NTM results. Threat NTMs (THREAT) produce conflicting results about the influence of lobbying.

The quantitative implications of the estimates are considerable. They indicate a sizable association of lobbying and the nontariff barrier measures. Consider the 1993 NTM model with %PACEXP. The coefficient of 2.694 shows that for every percentage point increase in %PACEXP, the NTM coverage ratio is associated with an increase of 0.027. This is both an economically and statistically significant result. The estimated coefficient of 0.754 on RANKPACEXP in the second NTM model shows that the 1993 results are robust to the two lobbying measures. The 1996 and 1999 NTM results are not as robust. Whereas the coefficients in 1996 indicate an association between lobbying and NTMs, the other coefficients are not statistically significant.

Quite surprisingly, the coefficients on M/Y and X/Y are not statistically significant in most NTM models for the 3 years. The exceptions are in 1996, where an increase in the export-output ratio is associated with a lower NTM, as one would intuitively expect in industries with intra-industry trade driven by intermediates goods trade. The import-output ratio is borderline statistically significant for the 1996 models, but not in the NTM models for other years.

The second panel on Table 2 displays estimates from the price NTM models for the 3 years. The association of lobbying spending with PRICE (e.g. antidumping duties, countervailing duties and other price-oriented NTMs) is significant. However, the coefficients fluctuate considerably across the 3 years. For example, %PACEXP has a coefficient of 2.264 for 1993, but it drops to 0.018 and 0.023 in 1996 and 1999, respectively. However, the coefficients retain their statistical significance. That points to the possibility that one or two large values of %PACEXP are determining the regression (which also explains why the fit for those years is abnormally good). Even though RANKPACEXP is is less susceptible to influential data points, it produces the same inference, that lobbying is strongly associated with the imposition of price NTMs. The drop in the estimates from 1993 is also indicative of the fact that price NTMs dropped over this period according to the 49
-14-

# 3.1.3. The Structural Level of Public Revenues

A key issue in the evaluation of the fiscal stance is the definition of a long-run level of revenues. In an industrialized country the long-run level of taxes w~juld be determined by an agreement between tax payers about the desirable fraction of permanent income that should be used to finance public expenditures. The value of this ratio is typically less than the maximum that is feasible, (the level at the top of the "Laffer curve"). In Cote d'Ivoire, the situation is simple in some respect since numerous policy studies reforms and experiments during the eighties have shown that the level of explicit taxes as a percentage of GDP cannot be raised. The ratio between explicit taxes and GDP can therefore be considered to be the maximum feasible, and defines for us the structural value of explicit tax revenues.

The main source of the fluctuations of the revenues comes from the implicit taxes. Consider cocoa for instance: Revenues of the implicit tax on cocoa fluctuate with the international price and the quantity of supply, which may be affected by droughts, as in the early eighties. In the definition of the structural level of the cocoa tax we have to take into account both effects. We define this level by the following formula: where Xt is the actual real output of the crop, Xt* is the trend value, Pt is the border price, pt* its long-run value, and ct is the producer price including processing costs. The value of pt* is taken to be equal to that in the reference year (1975), adjusted for inflation,
# **Figure 3: Disaggregate Food Insecurity Experience**

<!-- Start of picture text --> (a) (b)<br>.8 .8<br>.7 .7<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>(c) (d)<br>.8 .8<br>.7 .7<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>Tigray Amhara Oromia SNNP<br>Aug'19Apr'20May'20Jun'20Aug'20Sep'20Oct'20Nov'20Dec'20Jan'21Feb'21May'21 Aug'19Apr'20May'20Jun'20Aug'20Sep'20Oct'20Nov'20Dec'20Jan'21Feb'21May'21<br>Aug'19Apr'20May'20Jun'20Aug'20Sep'20Oct'20Nov'20Dec'20Jan'21Feb'21May'21 Aug'19Apr'20May'20Jun'20Aug'20Sep'20Oct'20Nov'20Dec'20Jan'21Feb'21May'21<br>Had to skip a meal<br>Worried about not having enough food<br>Ate few kinds of food<br>Unable to eat healthy/nutritious foods<br><!-- End of picture text --> In panel (b), relative to the pre-war period, the share of households in Tigray who report having to skip a meal in the last 30 days has shown a sharp rise from 19 percent to 45 percent – an increase of 26 percentage points. Panel (c) shows the share of households who reported consuming fewer varieties of food. Compared to October 2020, the decline in food varieties consumed after the outbreak of the war is significantly higher in Tigray (increased from 44 percent to 73 percent) than in the other three regions. Panel (d) shows the percentage of households who reported not being able to consume healthy and nutritious foods. In Tigray, the figure jumped from 51 percent in October 2020 to 76 percent in May 2021. For the other regions, the corresponding figures changed little (51 percent in October 2020 and 50 percent in May 2021). Overall, the results shown in Figure 2 and 3, attest to the dramatic deterioration in food security in Tigray in the aftermath of the breakout of civil war in November 2020.

17