rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

How does extortion affect political behavior? Extortion not only is a form of economic hardship; it also can be a form of traumatic victimization. Yet, existing literature has reached mixed conclusions about the relationships between political engagement and both economic hardship and victimization. For example, socioeconomic shocks may generate grievances and lead to increased turnout, or they may reduce participation because people have fewer of the resources required to engage in politics (e.g. Brady et al., 1995; Burden et al., 2017). Similarly, some research suggests that people exposed to violence will be more pro-social and thus more politically engaged (e.g. Bateson, 2012; Bauer et al., 2016). On the other hand, victimized individuals may lose faith in governmental institutions and participate less in public life (e.g. Collier and Vicente, 2014; Ley, 2018). In the aggregate, these studies may have varied results partly because of the difficulty disassociating traumatic experiences, whether related to violence or economic costs, from the economic and security environment in which the traumatic experiences occur. This environment can itself influence or correlate with unobserved factors shaping victim behavior. For example, violence and economic coercion are more likely to occur in places with high crime rates, and local criminal networks may shape how and to what degree one is willing to take civic action.

In this paper, we leverage unique data from Guatemalan deported migrants to disentangle the effect of extortion on political behavior from the potentially confounding local context in which extortion occurs. Deported migrants are a unique population for this inquiry because deportation involuntarily moves individuals, changing their local political and behavioral context so that it is distinct from where the extortion occurred. Furthermore, we show that extortion experienced by coyote-using migrants is quasi-random. Thus, we use the variation in experiences of extortion during the migration journey among individuals returned to Guatemala by the U.S. government to better isolate the relationship between extortion and civic engagement. We argue that extortion affects multiple processes that have opposite relationships with migration. On one hand, extortion increases social disengagement from fear of crime, which corresponds with decreased political participation and party identification. However, at the same time, extortion serves as a negative economic shock, and more negative economic assessments correspond with greater political engagement.

In addition to our theoretical contribution to the literatures on victimization, socioeconomic status, and political engagement, this paper also provides valuable insights into the political behavior of deported migrants, an important topic on which little data exist. In 2019 and 2020, the United States’ Immigration and Customs Enforcement (ICE) agency deported over 450,000 people (ICE, 2021a,b). Prior studies have analyzed the effects of remittances on 1
|**Table 3: Historical land expansio**<br>|**n and recent l**<br>|**and demand ex**<br>|**pressed in me**<br>|**dia reports**<br>||||
|---|---|---|---|---|---|---|---|
|**Region**|**Cultivated**|**land area (mil**|**lions of ha)**|**Annual c**|**hange (%)**|**Land de**|**mand 2009**|
||**1961**|**1997**|**2007**|**1961-1997**|**1997-2007**|**Mn ha**|**year eq**.|
|Sub-Saharan Africa|134.6|192.2|218.5|1.60|2.63|39.7|21.8|
|East Asia & Pacific|183.9|235.7|262.8|1.44|2.72|8.0|4.6|
|Eastern Europe & Central Asia|291.5|263.6|241.7|-0.77|-2.19|4.6|n.a.|
|Latin America|102.6|160.9|168.0|1.62|0.71|3.2|2.2|
|Middle-East & North Africa|77.9|91.3|89.0|0.37|-0.23|1.4|n.a.|
|South Asia|197.9|212.9|213.5|0.41|0.06|0.7|2.1|
|North America|235.3|232.5|225.3|-0.08|-0.72|0.2|n.a.|
|Western Europe|99.4|86.8|83.5|-0.35|-0.32|0.0|n.a.|
|Oceania|34.0|42.8|46.7|0.25|0.38|0.0|0.2|
|**World total **|**1,357.1 **|**1,518.6**|**1,549.0**|**4.49**|**3.04 **|**57.8**|**13.9**|

_Notes:_ Cultivated area is land under arable or permanent crops. ̳Land demand 2009‘ refers to intended or actual land acquisitions based on media reports over a period of 11 months (October 2008 – August 2009). The last column ( ̳year eq.‘) identifies this demand in terms of the number of years using average annual expansion over the 1961-2007 period.

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16

# FIgure 4 Real Interest Rate, Exchange Rate, Growth of GDP 1977-1982

**Arienuina**

Chile

**Urugua**

<!-- Start of picture text --> 140<br>lw ~~N<br>1977 1962 1977 1982 19r7 1982<br><!-- End of picture text --> Left scale:

- ~~Real interest rate (five-quarter moving average of annual lending rate in

percent) Real GDP (fourth quarter 1978 = 100) Right scale: Real exchange rate (fourth quarter 1978 = 100) Sore Corbo and de Melo (1987).
<!-- Start of picture text --> (a) Share divorced women - Morocco<br>0,16 i<br>0,14 i<br>!<br>-<br>0,12 4 —== -<br>0,08 = --7 - pane<br>0,06 -_-<br>0,04 oe> _---4-1<br>- !<br>0,02<br>!<br>) !<br>1992 2003 2004 2007 2014<br><!-- End of picture text --> <!-- Start of picture text --> (b) Share divorced women - Egypt<br>0,16<br>0,14 '<br>1<br>7 XN Il<br>0,12 Ay<br>0,08 fy \ Sg-7 Pd ob _ — ~<br>7 XX + - ~~<br>0,0 46 \y-i ~.<br>!<br>0,02<br>!<br>0 1<br>1989 1992 2000 2005 2008 2014<br><!-- End of picture text --> <!-- Start of picture text --> (c) Share divorced women - Jordan<br>0,12<br>0,1<br>0,08 4<br>0,06 h -7<br>‘N<br>0,04 \ - v—a —<br>0,02 ~ a‘N XN f-o- =- - 4 7 y, ~~. & =~ 4<br>0)<br>1990 1997 2001 2007 2009 2012<br>year<br><!-- End of picture text -->
60. In line with the Standard Model, the analysis finds that countries with private-sector participation in distribution are more likely to have achieved cost recovery. Indeed, the country case studies show a distinct trend of cases with private sector participation in distribution companies achieving higher fullcost recovery levels and being responsible for almost all of the net improvement since the pre-reform period (see Figure 16a-b).

**Figure 16: Utility Ownership and Cost Recovery**

<!-- Start of picture text --> a) Average Full-Cost Recovery Level 2010-17 b) Change compared to Pre-Reform<br>by Type of Utility (C3, %) Period (Percentage Points)<br>Private Reformed (Fully Private) 26.5%<br>Reformed (Public and<br>Mixed 34.3%<br>Private)<br>Public Remained Public -0.4%<br>0% 20% 40% 60% 80% 100% 120% -10% 0% 10% 20% 30% 40%<br><!-- End of picture text --> _Notes:_ ‘Mixed’ refers to the presence of both privately and publicly owned distribution companies in the country/state. _Source:_ World Bank staff.

61. This quantitative finding is borne out by the qualitative evidence, which suggests several underlying mechanisms including improved efficiency and commercialization (and thus depoliticization) of the power sector. Colombia’s privatization of utilities resulted in lower losses, higher collections, and full financial cost recovery for CODENSA (one of the privatized utilities). In Uganda, the private sector participation in UMEME under a concession contract brought immediate results as collections rose and distribution losses declined, improving cost recovery. The concession also guaranteed the investor 14 percent ROR, so the government increased tariffs to reflect costs. The impact of utility governance reforms on cost-recovery is robust also when looking at broader indicators of sector-wide private-sector participation and comprehensive indicators of utility governance (see Figure 17a-b). This is in line with the (still relatively limited) empirical literature on the impact of power sector reforms on utility finances, which finds correlations between independent regulators, vertical unbundling, competition, and private sector participation on the one side and tariffs and cost recovery on the other (Huenteler et al., 2017).

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direct impact on rice yield but also an indirect impact through the increase in fertilizer application.

On the other hand, by African standards, fertilizer use in the Sahelian schemes is remarkably high. In fact, in Niger, the average fertilizer application rates are well above the recommended rate, which is 400 kg/ha. This can be partly attributed to a relatively low fertilizer price and institutional support in these countries. According to national statistics, the ratio of urea price to paddy price is 2.5 in Burkina Faso, 1.3 in Mali, and 1.6 in Niger, which are close to the fertilizer price ratios in Table 3.<sup>8</sup> Note that the countries with strong institutional supports for fertilizer (see Mali, Niger, and Senegal in Table 2) show very low fertilizer price ratios. This ratio is not disadvantageous at all compared with the ratio of about 3.5 in Laguna in 1976-82 and 2 in major rice producers in Asia such as India and Pakistan in 2001 (Minten et al. 2006).

Related to this, we would like to stress that farming practices appear to be homogeneous in the Sahelian schemes. Regarding chemical fertilizer application, the standard deviation relative to the mean (i.e., the coefficient of variation) is much smaller than that in Doho and Chokwe. This may be one of the benefits of a well-managed irrigation scheme. This homogeneity implies that a serious constraint to rice production in the Sahel relative to Uganda and Mozambique might not exist, and thus many farmers use a large amount of chemical fertilizer to achieve yield comparable with yield in Asia.

Table 3 also shows labor use and the real daily wage in terms of kilograms of paddy.<sup>9</sup> A notable feature is found in Chokwe. The wage rate is higher and the labor

> 8 Chemical fertilizer reported in Table 3.3 consists of urea and other kinds of complete fertilizer packages.

> 9 Note that these are the real wages in terms of paddy. If we compare wages in US$ at official exchange rates of the survey years, they become 2.94 (Doho, Uganda), 1.73

9
Figure 7 Residual plots, ECM for total wages <!-- Start of picture text --> DLTot_w=.<br>Fitted=. ., Residual=_<br>. 25 2.?2<br>.1 ~~~~~~~~~~~9<br>* ~~~~~~~~-1.8<br>1975 1985 1995 1975 1985 1995<br>Corzrelog9XaM DLTotw= . Cumulative DLTot_w=_ DensitgNorzmal= ,,_....<br>.6 .8 t<br>.4 4<br>.2 _..6 /<br>................................. .4/<br>-. .2 /<br>-. 8p<br>1 2 3 4 5 -4 -3 -2 -1 0 1 2 3 4 5<br>Figure 8<br>Recursive graphics for total wages<br>_- 4_<br>......... . .\ ,-............... '......... .-<br>1980 1985 1998 1995 1986 1985 1998 1995<br>DLTot_ _ LP___= _<br>t 2°*S.E. = t 2NSE.=,<br>2<br>2_ ..-.. .......... .......4<br>eLP / ~ . __ ~ . . ~ ......... . .... -'_ 198 _..... 198 1995.. ,'<br>-4 -2<br>198e 1985 199e 1995 ls8e 1985 lsse 1995<br>:k 2 nS .E.-,::<br>3-<br>1986 1985 1998 1995<br><!-- End of picture text --> 19
# **3. Vulnerability Analysis and Mapping (VAM)**

VAM is a tool to identify the degree of food insecurity and vulnerability at the municipal level in Bolivia. It assigns a value from 1 to 5 to each municipality according to level of food insecurity and vulnerability, where 1= very low, 2 = low, 3 = medium, 4 = high, 5 = very high.<sup>5</sup> Developed by the Universidad Privada de Bolivia and Social and Economic Policy Analysis Unit ( _Unidad de Analisis de Políticas Sociales y Ecónomicas_ —UDAPE) in the Ministry of Development Planning, the VAM uses a World Food Program methodology (WFP, 2010)<sup>6</sup> and it is widely accepted in Bolivia as a formal measurement of food insecurity. VAM measures are available for 2003, 2006 and 2007 for all 327 municipalities across the nine departments that constitute Bolivia.

The VAM is constructed with community-level data using principal component analysis. Among the components used for all three iterations are: urbanization rate, rural population density and its square, proportion of institutionally attended births, schooling years, log of per capita consumption, under-five malnutrition rate, altitude, rainfall and a flood propensity categorical variable (four values). Components used only in the 2003 VAM and not used in subsequent updates include: dependency rate, life expectancy, agricultural potential (a categorical four-point scale variable capturing soil capacity), forestry potential, road density, draught frequency, frost days per year, low weight at birth and per capita household food expenditures.

Although changes in VAM scores between 2003 and 2007 were not uniform, the average vulnerability status by department decreased over time, suggesting that, on average, municipalities within each department have reduced their vulnerability to food insecurity (Figure 1). The exception is Cochabamba, the only department where average vulnerability to food insecurity increased over time. Tarija and the three _llanos_ departments of Santa Cruz, Beni and Pando registered, on average, between moderate and low vulnerability in 2007.<sup>7</sup>

> 5 Specifically, the methodology estimates each municipality‘s probability of pertaining to each one of these vulnerable categories, that is, five probabilities per municipality, and the largest of which determining the final vulnerability status the municipality is assigned to. Thus, if the estimated probability – conditioned to a number of controls– of a given municipality to pertain to VAM=5 is 85%, that municipality is said to have a very high vulnerability status to food insecurity. These probabilities are used below for the correlation analysis – see Figure 5. 6 WFP (2010) identifies five categories of food security situations. Phase 1 refers to generally food secure; Phase 2 - moderately/borderline food insecure; Phase 3 - acute food and livelihood crisis; Phase 4 - humanitarian emergency and Phase 5 - famine/humanitarian catastrophe. See http://fsa.wfp.org/special_documents/FSA_Factsheet_EN.pdf

> 7  Similar results are obtained comparing VAM 2003 and VAM 2006.

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changes in resource allocation, as PASSPA is the smallest of all the food and nutrition programs currently in place.

The estimates of purchasing power currently trqnsferred to the targeted groups through food support programs show a bias against the rural areas. The Conasupo rural stores carry an average benefit of $ 10 a year per family (assuming only poor families have access to theml), while an urban family who participates in both the Liconsa milk and the tortibono programs can potentially receive up to $ 145 annually. This is not only inequitable given the distribution of extreme-poverty between rural and urban areas (recall from table 7 that between two-thirds to three-fourths of total extreme-poverty is accounted for by rural regions), but perpetuates the incentives to migrate to the urban squatter areas<sup>129</sup> . Differently put, the current pattern of directly targeted benefits is rather different from what one would like to see. This is not to argue that benefits to the urban areas should be reduced, but to point out, rather, the need for additional benefits for the rural poor. It may be relatively more difficult to have benefits reach the rural poor, but this is what a 'frontal attack' on poverty requires. Moreover, the ruralurban imbalance is exacerbated by the mix of rationing coupons and generalized price subsidies. The tortibono and Liconsa milk programs in the rural areas work with coupons, while the Conasupo rural stores offer price discounts. Clearly, there are non-poor inhabitants in the rural areas who might be benefiting from the price subsidies. Thus, it is not clear that the few resources channeled to the rural areas reach only the extremely-poar.

The distribution of resources devoted to food support also appears somewhat urnbalanced. Out of the $ 900 million spent on food support programs in 1988, more than half, or about $ 500 million, were allocated to untargeted food subsidies. The World Bank (1990a, p. 6) estimates that more than 80X of these subsidies go to families who earn more than 1.5 times the minimum wages. This implies, roughly speaking, that of a total of $ 900 million spent on food and health support by the programs described above, only about $ 500 million benefit the extremely-poor130. From the point of view of alleviating extreme129Torche (1990, p. 25) points out that 55X of the beneficiaries of the tortibonos program live in the Federal District and surrounding metropolitan area.

130Assuming all 400 million allocated to the targeted programs indeed reach the extremely-poor, and adding to this figure 20X of the 500 million spent on untargeted food subsidies.
# 1.2: The host country problem

Illegal immigration poses a number of problems to the host country which, in the context of this paper, can be broadly classi fi ed into two groups. Firstly, illegal immigration may be a problem simply because it leads to excessive or unwanted immigration. Secondly, illegality per se may pose additional problems to the host. Immigration may be unwanted because it leads to unemployment in the host country (Ethier 1986, Winter-Ebmer and Zweimüller 1998), sectoral or regional displacement of natives (Hatton and Tani 2003), politically unfavorable factor price movements (Ethier 1986a,b), fi scal pressure (Myers and Papageorgiou 2000)<sup>7</sup> , sociocultural and linguistic differences which may adversely affect local cultures and life-styles (Bliss 1994, Schiff 1992).<sup>8</sup> These adverse effects from immigration are not due to the illegality of migration but simply because of excessive immigration. Thus, these costs are common to legal and illegal aliens and are referred to as the “social cost” from legal migration.

The additional cost of illegality most obviously relates to the greater likelihood of crime and the increased threat to national security. The cost of illegality to the host may depend on its policies towards illegal aliens. For example the denial of access to medical services can create a health hazard, denial of access to the formal labor market can push immigrants into the underground economy, and exclusion from labor unions can lead to social tensions.<sup>9</sup>

> 7 Also see, for example, Lee and Miller 2000, Auerbach and Oreopolis 2000, Storesletten 2000 and Borjas 1999.

> 8 Other studies which analyze the impact of immigration on local culture include Putnam 1995, Carlton 1995, Coleman 1987, 1988, 1990, Knack and Keefer 1997, etc.

> 9 For example, Costich (2001-02) argues that measures such as the Personal Responsibility and Work Opportunity Reconciliation Act and the Illegal Immigration Reform & Immigrant Responsibility Act which were implemented in 1996 and severely limited health care access to undocumented immigrants in the U.S., are likely to have adverse health consequences for all U.S. residents.

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analyze labor market flows (rather than traditional stock measures).<sup>1</sup> The analysis of job flows helps to better understand forces shaping labor market patterns. In particular, it sheds light on the nexus between the labor market and the business environment, as well as the relationship between employment trends and enterprise restructuring. As such, the analysis of job turnover helps to identify possible causes of observed labor market outcomes.

The rest of the paper is organized as follows. The first section shows key labor market outcomes in Moldova; the second section analyzes job destruction and job creation; and the third section attempts to explain the low rate of job creation in Moldova. The final section summarizes major issues and discusses policy implications.

# **I. UNDERUTILIZATION OF LABOR RESOURCES**

Unemployment is low in Moldova, but paradoxically, so is employment. Moreover, many of those who are formally employed are underemployed, and have menial or precarious jobs. Altogether, despite low unemployment, labor resources are substantially underutilized, job opportunities are scarce, and the labor market is depressed.

The ILO/LFS unemployment rate is around 7 percent in Moldova, which is less than in most transition economies of CEE, where unemployment often exceeds 15 percent. It is still lower – barely 2 percent – when one uses the unemployment register data. On the face of it, this relatively low unemployment rate suggests favorable labor market conditions. However in fact it masks a depressed labor market. The most general measure of the utilization of labor resources, the employment ratio, at 57%, is well below the OECD average of 66%.<sup>2</sup> This means that some 10 percent of the working age population in Moldova who could have been productively employed is without work. This translates into lower output and ultimately higher poverty, as those without work are disproportionately poor (Andrews, 2003).

> 1 The analysis is confined to the formal sector.  However the paper by Andrews (2003) demonstrates that there is not much dynamism in the informal sector, either.  Accordingly, the relevant message – that job creation is insufficient in Moldova – is robust to the inclusion of formal sector activities.

> 2 The employment ratio is the ratio of employment to population of working age, defined as age from 15-to 64.

2
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