rafmacalaba/datause-encoder-data huggingface dataset:
liberalization scenario. The Doha scenario hardly affects rural poverty, but full liberalization decreases the rural headcount by an additional percentage point. Some simple calculations can give some more meaning to these figures: The 0.5 percentage point difference in P0 in the full liberalization scenario means that approximately 135,000 people are lifted out of poverty. The 1 percentage point difference implied by the full liberalization scenario reduces the number of poor people in rural areas by approximately 115,000. Considering the very small increase in non-agricultural unskilled wages this may be somewhat surprising, but it is the urban concentration that drives this result. Some more growth in urban areas lifts more people out of poverty than very high agricultural growth.

_<u>Table 19: Poverty impact of trade, by region</u>_

|Region|Population<br>%<br>2001|P0<br>%<br>initial levels|contr. to<br>P0|BaU 2001-<br>15 P0<br>change|Doha % of<br>BaU P0<br>change<br>2015|Full % of<br>BaU P0<br>change|
|---|---|---|---|---|---|---|
|North|5.7|34.0|8.2|-7.9|101.0|107.7|
|Northeast|28.5|45.4|54.8|-9.3|102.6|109.5|
|Southeast|43.5|12.4|22.8|-3.7|101.9|107.9|
|South|15.2|14.7|9.5|-4.6|101.4|107.8|
|Center-West|7.1|16.0|4.8|-5.0|119.4|121.3|

Source: Authors’ calculations.

Due to the regional differences both in factor endowments and specialization patterns, we might expect poverty reduction patterns to differ substantially between the regions for the BaU as well as for the trade shocks. The reduction in the headcount for the BaU confirms this expectation, as poverty declines more strongly in the Northeast, South, and CenterWest, the regions with the highest shares in agricultural employment. The Doha round has negligible effects across all regions although the figures in Table 19 suggest a different story for the Center-West. Yet, a look at the changes of P1 and P2 (not reported) demonstrates that this strong effect is due to many households being just below the poverty line in this region.<sup>38</sup> The Northeast, the region with the highest incidence of poverty where more than 50 percent of the Brazilian poor reside, benefits most from the Doha liberalization and about 50 000 individuals are lifted out of poverty in this region. In the same region, full liberalization helps about 175 000 individuals to escape poverty. The poor in the North, another region with worryingly high poverty rates, gain relatively

> 38 This is a case that illustrates why we usually report not only the headcount index, as this indicator can be quite misleading in some instances.

48
analysis which enables us to control for confounding variables such as age and gender. Table 3 reports on the monthly income for those classified as severe-OSD and non-OSD, disaggregated by employment type.

**Table 3. Monthly Income (in birr) and Days Worked by Employment Type and OSD Severitya'b**

||N|_Permanent_<br>Monthly Income|<br>Days Worked|N|_Temporary_<br>Monthly Income|Days Worked|
|---|---|---|---|---|---|---|
|Non-OSDc|58|172.0<br>(9. 11)|28.8<br>(0.24)|99|76.3<br>(2.41)|21.8<br>(0.42)|
|Severe-OSD<br>0|63|142.3<br>(6.15)|26.9<br>(0.41)|33|<br>80.5<br>(4.22)|22.2<br>(0.80)|
|Differencec d||29.7*<br>(10.99)|1.9*<br>(0.48)||-4.2<br>(4.86)|-0.4<br>(0.90)|
|Wilcoxon Rank-Sum<br>Statistice||3.180*|3.697*||-0.941|-0.599|

a. Monthly income is the average gross monthly income for the latest 12 months of available data.

b. Days worked refers to the average number of days worked per month in the latest 12 months.

c. Standard errors in parentheses.

d. "*" represents statistical significance at 5% based on a t-test with unequal variances.

e. "*" represents statistical significance at 5% based on the Wilcoxon rank-sum test for equal medians.

As we can see from Table 3, severe-OSD permanent employees earned on average almost 30 birr less per month and worked almost 2 fewer days per month when compared with non-OSD workers in the same employment category. As reported, this difference is statistically significant for both variables. On the other hand, severe-OSD does not appear to cause a significant decline in the monthly average income or of days worked for temporary workers.

Arguably, though, the most pertinent measure of economic productivity would be daily wages earned, since that controls income for days worked. 4 Table 4 reports on daily wages earned by severe-OSD and non-OSD employees, disaggregated by type of employment.

> 14 Daily wages earned at the plantation correspond directly to economic productivity. Workers are paid according to output produced. There are penalties for not achieving output quotas and bonuses if they can produce more than the minimum set for any given activity. However, the wages paid for a given activity are lower for temporary workers, hence in our analysis we separate the sample based upon employmen **t** ype (i.e., permanent or temporary).

6
Ninis, which doubled from nearly 0.66 to 1.28 million between 2000 and 2010, combined with the stabilization of the number of female Ninis at around 3.50 million during the 2000s. In relative terms, the proportion of Ninis in Mexico has been stable since the beginning of the 2000s, at around 22.7 per cent, due to the sizeable increase in the percentage of male Ninis, which went from 7.5 to 12.0 percent between 2000 and 2010, and the slight decrease in the percentage of female Ninis, which went from 36.3 to 33.7 percent during the same period.

The literature has produced several empirical insights into the phenomenon of Nininess in Mexico. Arceo-Gómez and Campos-Vázquez (2011) apply cross-sectional analysis to a combination of census data, household expenditure surveys, and labor surveys covering the period from 1990 to 2010 to study Ninis aged between 15 and 19 years old. In line with the results of De Hoyos et al. (2014), they find that the percentage of Ninis decreased during these two decades. They also find big differences by gender, as well as differential impacts of recessions - which disproportionately affect the young - on the proportion of Ninis. ArceoGómez and Campos-Vázquez (2011) estimate that in 2010 there were 8.6 million Ninis aged 15 to 19 in Mexico (approximately 29 percent of this age group), of which 6.5 million were women. Analyzing the reported reasons for not working, they find 70 percent of women with children do not have anyone who can look after them. This finding, combined with high pregnancy rates in Mexico (Azevedo et al., 2012), supports the hypothesis that teenage pregnancy limits the labor force participation of women and, by motivating their decision to instead spend time on housework, increases the proportion of Ninis in the population. Finally, the authors conclude that household income is an important correlate of being a Nini for men but not for women. Using different sources of cross-sectional data, Águila et al. (2013) find similar results, but emphasize the fact that the majority of Ninis are women with children, living in high poverty contexts. Using longitudinal data from the Mexican Family Life Survey and a cross-section from the 2011 Social Mobility Survey, Arceo-Gómez and CamposVázquez (2014) find evidence to substantiate this hypothesis. They observe that teenage pregnancy generates a decrease of between 0.6 and 0.8 years of schooling and lower school attendance, fewer hours of work, and a higher marriage rate in the short run. Moreover, in the long-term teenage pregnancy results in a loss of between 1 and 1.2 years of education, with the related consequences of later educational achievement and lower household income, heightening vulnerability and increasing the risk of falling into poverty.

Tuirán and Ávila (2012) analyze several hypotheses and preconceptions about Ninis in Mexico. Studying 12 to 29 year olds using data from the National Youth Survey (ENJ, by its acronym 4
Heckman, J. (1978), ‘Dummy Endogenous Variables in a Simultaneous Equation System’, Econometrica 46: 931-959.

Hossain, M. (1988), ‘Nature and Impact of the Green Revolution in Bangladesh’, Research Report No. 67. Washington DC: International Food Policy Research Institute.

Kishi, M., N. Hirschhorn, M. Qjajadisastra, L. N. Satterlee, S. Strowman and R. Dilts. (1995), ‘Relationship of Pesticide Spraying to Signs and Symptoms in Indonesian Farmers’, Scandinavian Journal of Work & Environmental Health 21: 124-133.

Maddala, G. S. (1983), Limited Dependent and Qualitative Variables in Econometrics. Cambridge: Cambridge University Press.

Meisner, C. (2004), ‘Report of Pesticide Hotspots in Bangladesh’, Report to the Canadian International Development Agency (CIDA). Mimeo. Washington DC: World Bank.

NOVIB (1993), ‘Pesticides Misuse in Bangladesh’, The Pesticides News, No. 22, Dec. 1993. The Pesticides Trust. London, U.K.

Pimental, D., H. Acquay, M. Biltonen (1992), ‘Environmental and Economic Costs of Pesticide Use’, Bioscience 42, 750-60.

Ramaswamy, S. (1992), ‘Pest Control and Environment’, Notes for discussion at a seminar on environment and agriculture. Agriculturalist Association of Bangladesh, Dhaka, p.19.

Rasul, G. and G. Thapa (2003), ‘Sustainability Analysis of Ecological and Conventional Agricultural Systems in Bangladesh’, World Development 31 (10): 1721-1741.

20
remittances are a composite of the number of migrants who choose to remit, and the amount remitted by those who send back some money. The total remitted ranges from $227 in the Australian LSIA survey of new immigrants to $3089 in the Spanish NIDI survey which surveyed Moroccans and Senegalese through a combination of geographic targeting and referrals. Because of the differences in survey methodology and target population across countries, neither figure should be used to make statements of the nature “Africans in country X remit more than Africans in country Y”. Rather the purpose of showing these figures is to give the reader a sense of the range in variation across destination country samples, and so that this heterogeneity can be kept in mind when examining differences across origin countries.

We next summarize remitting patterns by country of origin. For the pooled African sample as a whole, we find one-third of migrant households in the OECD remitting, with the mean (median) amount remitted conditional on remitting equal to US$2,638 (US$1,088) annually. Combining the extensive and intensive margins gives a mean annual amount remitted of US$1,263, twice as large as the value for the rest of the developing world’s migrants (US$668). Table 2 shows in more detail the distribution of the amount remitted for Sub-Saharans, North Africans, and the rest of the developing world.<sup>3</sup> We see few migrants remit amounts of $5,000 or more annually – 5 percent of Sub-Saharans and 7 percent of North Africans, while 80 percent of Sub-Saharan migrants and 72 percent of North African migrants remit $500 or less per year.

> 3 This table is based on the set of surveys and observations which report the amount remitted. It therefore differs from the extensive margins in Figures 1 and 3 which also use surveys that collect information just on the extensive margin. This makes little difference for the SSA sample, but there are more North Africans and rest of the developing world migrants remitting in the surveys which also measure how much is remitted than in the surveys which just measure whether individuals remit.

7
# **1. Introduction**

The central role of secure property rights in growth has long been recognized (Coase, 1960; Demsetz, 1967; North, 1981). Property rights protect individuals against expropriation by neighbors and other agents, as well as against the state, offering incentives for long-term investments in assets. Institutions such as property rights have been shown to be an important factor in explaining growth and the lack thereof in parts of the world (North and Weingast, 1989; Acemoglu and Johnson, 2003). In this paper, we add to the limited micro-evidence on linking insecure property rights to capital accumulation. We focus on Africa, the region were growth has been lagging most strikingly in recent decades and where risk to assets has been put forward as a crucial determinant of this growth failure (Collier and Gunning, 1998). More specifically, we study Ethiopia, one of the poorest countries in the world, and the role of insecurity in property rights for land on long-term investment in land-specific assets, such as trees and shrubs. Our study uses longitudinal plot-level and household data to provide microlevel evidence on the link between transfer rights and perceptions of the threat of expropriation, and capital accumulation.

This paper adds to the literature in a number of ways. First, it builds on Besley’s (1995) paper on investment in trees in Ghana by using panel data on the accumulation of capital, rather than a cross-section data set. It also extends this work by adding perceptions of the threat of expropriation by the state to the analysis. In this way, it is not dissimilar to the study by Jacoby et al. (2002) on China, but with a key difference that rather than calibrating ‘objective’ risks of appropriation based on past data, we have access to perceived threats, arguably more important for forwardlooking investment behavior. We find that both perceptions related to the threat of expropriation, as well as the perceived rights to transfer land to others are important for investment on the land, with crucial efficiency and growth implications. The institutions of property rights matter to understand limited investment and growth, this time based on evidence from one of the poorest countries in the world. Unpacking these institutions, we find that perceived transfer rights, rather than a relatively short-term threat of expropriation, are quantitatively the more important factor explaining relatively low investment.

2
**Table 1.  Summary Statistics for the Pooled Sample.**

||N|mean|sd|min|max|
|---|---|---|---|---|---|
|I/lagged K|11,659|0.338|0.882|0.000|7.086|
|Ln(GDP per capita)|11,699|8.992|0.649|7.517|10.511|
|Ln(city population)|11,699|6.229|0.560|4.840|7.927|
|Firm age|11,694|13.652|13.545|3.000|140.000|
|Non-state|10,756|0.780|0.414|0.000|1.000|
|Foreign|10,756|0.121|0.326|0.000|1.000|
|Loan|11,697|0.595|0.491|0.000|1.000|
|Tradecredit|11,699|0.086|0.195|0.000|1.000|
|UCA|11,563|0.109|1.638|-15.830|1.000|
|Sales/lagged K|11,566|6.620|13.646|0.004|162.523|
|CF/lagged K|11,438|0.608|1.425|-1.628|15.887|
|Lagged sales growth|11,404|0.533|1.279|-0.549|15.038|
|Tobin’s Q|11,699|1.205|0.138|0.909|1.831|

42
Table 1: Survey response, treatments’ take-up and objective compliance

||_All arms_<br>(1)|_Treatment 1_<br>Drama<br>(2)|_Treatment 2_<br>Documentary<br>(3)|_Control_<br>Placebo<br>(4)|
|---|---|---|---|---|
|**Panel A: Survey response**|||||
|Baseline respondents|5,229|1,791|1,783|1,655|
|_Share (% out of baseline)_|_100_|_100_|_100_|_100_|
|Short-term survey (1 week) respondents|606|258|128|220|
|_Share (% out of baseline)_|_11.59_|_14.41_|_7.18_|_13.29_|
|Medium-term survey (4 months) respondents|619|212|200|207|
|_Share (% out of baseline)_|_11.84_|_11.84_|_11.22_|_12.51_|
|**Panel B: Overall take-up and compliance**|||||
|Self-reported to complete intervention|2,328|939|584|805|
|_Share (% out of baseline)_|_44.52_|_52.43_|_32.75_|_48.64_|
|Self-reported to watch half or more|3,153|1,169|831|1,153|
|_Share (% out of baseline)_|_60.30_|_65.27_|_46.61_|_69.67_|
|Actually played half or more|1,849|762|345|742|
|_Share (% out of baseline)_|_35.36_|_42.55_|_19.35_|_44.83_|
|**Panel C: Short-term sample compliance**|||||
|Share of respondents who played half or more (%)|78.38|81.01|67.97|81.36|
|Mean duration of intervention (days)|8.9|8.4|10.0|8.8|
|**Panel D: Medium-term sample compliance**|||||
|Share of respondents who played half or more (%)|66.07|75.00|49.00|73.43|
|Mean duration of intervention (days)|8.2|7.0|10.6|7.2|

Notes: Panel B reports numbers for all respondents who completed the baseline survey. Panels C and D restrict the attention to respondents who completed the short-term and medium-term surveys, respectively. Treatment compliance is objectively measured by the bot. In particular, for each respondent and video, the bot recorded specific browsing events such as play, pause and end.

spondents who completed the short-term and medium-term follow-up surveys, respectively. Overall, we find no evidence that the high attrition rates led to a differential self-selection into study completion across treatment assignments along observable characteristics. In fact, we generally observe baseline balance in both outcome and control variables between treatment and control groups for both follow-up samples, although this does not rule out potential imbalances across experimental groups in latent characteristics.<sup>26</sup> Pre-treatment differences between experimental groups are generally small and not statistically significant, including outcomes such as justification of violence and acceptance of pre-marital sex. For the few statistically significant differences, these tend to be small in magnitude and are accounted for in the analysis by the inclusion of the Baseline Stance Index.<sup>27</sup> Taken together, we find

> 26Annex Table A4 shows balance among experimental conditions in the full baseline sample too.

> 27For standalone outcome variables that were also collected at baseline, we include their baseline values (instead of the Baseline Stance Index) on the RHS of equation (1) to control for potential pre-existing

14
results, they find “more than half of these results show no relationship and the remainder shows both positive and negative relationships”. Our results are consistent with this work and extend it to refugee flows in low- and middle-income countries. Our findings also relate to the meta-analysis by Verme and Schuettler (2019) who find little average effect of refugees on hosts’ labor market outcomes, which could then influence attitudes.

The paper proceeds as follows. Section 2 reviews the context, and Section 3 outlines literature on the effects of refugee movements both on attitudes and political outcomes. Section 4 describes the data and outlines our empirical approach. Section 5 presents the results after which Section 6 concludes.

# **2 Context**

More than 80 million people were forcibly displaced worldwide at the end of 2020. Over 26 million people crossed international borders as refugees, with 68% of all refugees worldwide coming from only five countries – Afghanistan, Myanmar, South – Sudan, Syria, Venezuela and 73% live in countries neighboring their country of origin (UNHCR 2021b).<sup>5</sup> According to UNHCR, 70% of refugees live in countries where their right to work is restricted, and 66% live in countries where their freedom of movement is restricted (UNHCR 2021a).

One common characteristic of host countries’ policies is camps. While some camps restrict movement, others do not, with Uganda and Iraqi Kurdistan as two examples of the more open “settlement” model.<sup>6</sup> We find that countries with more restrictive labor market policies for refugees are also more likely to build camps (Table A11), but we treat camps and restrictive employment policy as distinct in our discussion and analysis.

> 5The three most common countries of asylum hosted people almost exclusively from one single country: Turkey (3.7 million Syrians); Colombia (1.7 million Venezuelans); and Pakistan (1.4 million Afghans) (UNHCR 2021b).

> 6We define camps “planned camp”s in UNHCR’s dataset, which includes open camps like in Uganda and Kurdistan.

3
**Table 7 : Analysis of Mekong and Zambezi Treaties with regards to types of complaints**<sup>**a**</sup>

|||Mekong<br>|||Za<br>|mbezi<br>||
|---|---|---|---|---|---|---|---|
|Type<br>|Quantity|Hydropower<br>|<br>Flood|Quantity|Quantity<br>|Hydropower<br>|<br>Flood|
|Agreement<br>year and<br>context|4/1995<br>Multilateral|8/1965<br>Thailand-<br>Laos|4/1995<br>Multilateral|5/1987<br>Multilateral|7/1987<br>Zambia-<br>Zimbabwe|7/1987<br>Zambia-<br>Zimbabwe|5/1987<br>Multilateral|
|Strong verbal<br>expressions<br>displaying<br>hostility in<br>|0|1|0|0|0|0|0|
|interaction||||||||
|Mild verbal<br>expressions<br>displaying<br>discord in<br>interaction|17|6|11|0|0|0|0|
|Total<br>conflicting<br>events<sup>b</sup>|17|7|11|0|0|0|0|
|Neutral or<br>non-<br>significant<br>acts for the<br>inter-nation<br>situation|6|1|6|0|0|0|0|
|Minor official<br>exchanges,<br>talks or policy<br>expressions,<br>mild verbal<br>support|94|19|90|3|1|1|3|
|Official verbal<br>support of<br>goals, values,<br>or regime|16|7|12|0|0|0|31|
|Cultural or<br>scientific<br>agreement or<br>support<br>(nonstrategic)|4|2|16|0|0|0|0|
|Non-military<br>economic,<br>technological<br>or industrial<br>agreement|33|7|32|1|0|0|1|
|Military<br>economic or<br>strategic<br>|0|0|0|0|0|0|0|
|support||||||||
|International<br>Freshwater|6|2|6|18|2|2|21|

35
12

**Figure 7  Deflated Benefits since 1948**

<!-- Start of picture text --> 2'500 9.0%<br>8.0%<br>2'000<br>7.0%<br>6.0%<br>1'500<br>5.0%<br>4.0%<br>1'000<br>3.0%<br>2.0%<br>500<br>1.0%<br>0 0.0%<br>Minimum Maximum Contribution Rates (as % of wage)<br>1948 1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005<br><!-- End of picture text --> Source: Own illustration

|**Table 3 Average Pensions, Income an**<br>|**d Replacem**<br>|**ent Rat**<br>|**es**<br>|
|---|---|---|---|
|Retirees AHV/IV|1990|1998|2000|
|Total average monthly income households (CHF)<br>|4,124<br>|6,070<br>|5,761<br>|
|Average monthly pensionpayments(CHF)|1,737|2,301|2,313|
|Average replacement ratio|42%|38%|40%|

Source: Bundesamt für Sozialversicherung (2004a), p.6 The number of contribution years, including those granted for childcare, is essential in the determination of future benefits. Some special rules apply for couples and survivors: Married couples’ entitlements are capped at 150 percent of a single benefit. This reduction takes into account that per capita expenses are considerably lower in a two–member than in a single household. It is also a compensation for the survivor insurance granted to married couples during the working period. The redistribution between single and married individuals is relatively small in this pillar.

Since 2002, individuals can also retire one or two full years earlier. The adjustment rate at which benefits are reduced is actuarially fair at 6.8 percent annually for men and women born in or after 1948. For women born between 1942 and 1947, the rate is only 3.4 percent per year to cushion the effect of the recent increase in the female retirement age. The benefits can also be delayed by one to five years at an annual adjustment rate of 5.2 percent.

# **2.2 The Second Pillar: Occupational Pension Plans**

# **_2.2.1 Overview_**

The Swiss second pillar, organized as an occupational pension system has known a long history, but became mandatory only in 1985. As Figure 8 shows, a sizeable fraction of the working force had already been covered before such plans were mandated. The figure