rafmacalaba/datause-encoder-data huggingface dataset:
border closure between the Palestinian territory and Israel for the Palestinian labor market.<sup>4</sup> By making it more difficult for the Palestinians to reach the Israeli labor market, Israeli closures lower the demand for Palestinian workers in Israel; at the same time, they increase the supply of workers in the Palestinian labor market. That is an important issue given the dependence of the West Bank’s and Gaza’s labor markets on Israel especially before the outbreak of the second Intifada.

Miaari and Sauer (2011) find that the tight border closure policy enacted by Israel at the beginning of the last decade negatively affected Palestinian employment levels in Israel, which was mainly replaced by a surge in the inflow of foreign workers.<sup>5</sup> They show that a 10% increase in foreign workers in Israel reduces employment rates for Palestinians from the West Bank by 6.8%, while doubling the number of closure days reduces it by 5.2%. That is consistent with the theoretical model by Rupper Bulmer (2003), which predicts that in the short run border closures translate directly into spikes in unemployment in the absence of a wage adjustment in the Palestinian labor market. However Mansour (2010) finds some wage decline for unskilled workers in the West Bank as a response of increases in the supply of skilled workers expelled by the Israeli labor market, suggesting that those skilled workers competed locally on low-skilled jobs.<sup>6</sup> Benmelech et al. (2011) find evidence of adverse labor market effects of Palestinian suicide attacks inside Israel in the perpetrators’ district of origin. They associate these results at least in part to increased Israeli restrictions on the district, including movement restrictions, following the attacks. We complement this literature by specifically examining the effects of mobility restrictions within the West Bank while controlling for the concomitant effects of external closure as well.

Finally this paper is also related to the literature on the economic impact of the changes in transport and trade costs within countries, whose main focus is on the impact evaluation of transport infrastructure improvements. A natural outcome of such improvements should be the increase in trade between the newly connected regions, which in turn has implication for welfare

> 4 Angrist (1995 and 1996) provide some seminal analyses of the Palestinian labor market.

> 5 According to the Israeli Central Bureau of Statistics, about 60% of foreign immigrants have been allocated in the same industrial jobs used to be occupied by Palestinians. This pattern was substantial in the period 1995-2005, and explains a good part of the decrease in demand for unskilled Palestinian labor in Israel after the second Intifada. 6 Some evidence is also emerging on other labor market effects of the conflict. For example Miaari, Zussman and Zussman, (2012a) find that the second Intifada increase the extent of job separation between Arab and Jewish workers within Israeli firms.

5
This paper contributes to several strands of the migration and development literature. The first is work on barriers to information acquisition by potential migrants, and the relevance of information asymmetries even in transnational households (McKenzie et al., 2013; Ashraf et al., 2015; Batista and Narciso, 2018). Potential migrants may receive imperfect information from others in their network, and this lack of recent information could be more serious in the context of irregular migration from Africa to Europe, where de facto European immigration policy changes rapidly and information flows to isolated rural areas are scarce.<sup>5</sup> Several papers have used randomized controlled trials to investigate the role of information in migration decisions (Bryan et al, 2014; Beam, 2016; Beam et al, 2016; Shrestha, 2020; Baseler, 2019). Generally, the aim of these studies was to assess to what extent the provision of information facilitated migration in contexts where mobility is low despite being welfare-enhancing. What is made clear from this literature is that information matters both in internal and international migration contexts, and that lack of information contributes to explain why migration from some areas remains at suboptimal levels. More recently, lab-in-the-field experiments have also been conducted to investigate the role of various factors (including incomplete information) in migration decisions (Batista and McKenzie, 2018; Barnett-Howell, 2018; Lagakos et al, 2018; Bah and Batista; 2018). These studies are also supportive of the hypothesis that information is an important driver of migration decisions in different contexts.

While most experiments have investigated to what extent providing information _facilitates_ migration, information campaigns launched in Sub-Saharan Africa in recent years have mainly sought to _deter_ migration, irregular migration in particular, by emphasizing the deadly dangers associated with it.<sup>6</sup> Until recently there was very little evidence on the effectiveness of these information campaigns. Tjaden et al. (2018) conducted a systematic review of 60 studies of

> 5 For example, as we were launching the program in 2019, the context in which migrants cross the Mediterranean Sea was becoming increasingly riskier, with migrant smuggler boats likely to be intercepted by Libyan army boats with migrants then being kept in Libyan migration detention centers in vulnerable conditions, while humanitarian support had virtually disappeared from the Mediterranean, likely resulting in increased death risks, according to reports by the United Nations. <u>https://www.theguardian.com/world/2019/jun/09/mediterranean-sea-of-blood-migrant-refugeerescue-boats-un-unhcr</u> [last accessed on July 23, 2019] <u>https://www.france24.com/en/20190704-libya-un-securitycouncil-attack-tajoura-libya-migrant-centre</u> [last accessed on July 23, 2019] <u>https://www.theguardian.com/world/2019/apr/25/libya-detention-centre-attack-footage-refugees-hiding-shooting</u> [last accessed on July 23, 2019]

6 Nieuwenhuys and Pécoud (2007); Carling and Hernández-Carretero (2008, 2011).

6
Mundaca, G., 2010. “Exchange rate uncertainty and optimal participation in international trade,” unpublished manuscript.

Mussa, M. (1981). “The role of official interventions.” Group of Thirty Occasional Papers, No. 6, Group of Thirty, New York.

Naranjo A. and M. Nimalendran (2000). “Government interventions and adverse selection costs in foreign exchange markets.” Review of Financial Studies, 13, 453 – 477.

Nelson, D. B. (1991). “Conditional heteroskedasticity in asset returns: a new approach.” Econometrica 59, 347 – 370.

Olsen, R.J., (1980). “A least squares correction for selectivity bias.” Econometrica 48, 1815 – 1820.

32
**Table 7. Willingness to Recommend Interventions, Attitudes Toward the Chatbot (H5)**

||(1)|(2)|(3)|
|---|---|---|---|
||Confidence on<br>identifying<br>misinformation<br>after the game|Game difficulty|Recommend the<br>game|
|**_Panel A: Placebo as base group_**||||
|Control|-0.055|0.015|-0.069|
||(0.061)|(0.062)|(0.059)|
||[0.828]|[0.828]|[0.828]|
|Comprehensive Active|0.152***|0.286***|0.097*|
|Inoculation|(0.059)|(0.057)|(0.055)|
||[0.029]|[0.000]|[0.140]|
|Brief Active Inoculation|0.201***|0.122**|0.133**|
||(0.058)|(0.060)|(0.052)|
||[0.001]|[0.117]|[0.035]|
|Passive Inoculation|0.007|-0.007|-0.018|
||(0.058)|(0.058)|(0.056)|
||[0.999]|[0.999]|[0.991]|
|Controls|Yes|Yes|Yes|
|Observations|2,851|2,851|2,851|
|R-squared|0.041|0.026|0.092|
|**_Panel B: Passive Inoculation as base group_**||||
|Control|-0.062|0.022|-0.050|
||(0.060)|(0.061)|(0.062)|
||[0.868]|[0.909]|[0.909]|
|Placebo|-0.007|0.007|0.018|
||(0.058)|(0.058)|(0.056)|
||[0.999]|[0.999]|[0.991]|
|Comprehensive Active<br>Inoculation|0.145**|0.294***|0.116**|
||(0.057)|(0.056)|(0.057)|
||[0.044]|[0.000]|[0.098]|
|Brief Active Inoculation|0.194***|0.129**|0.151***|
||(0.056)|(0.059)|(0.055)|
||[0.005]|[0.102]|[0.026]|
|Controls|Yes|Yes|Yes|
|Observations|2,851|2,851|2,851|
|R-squared|0.041|0.026|0.092|

_Note._ OLS estimates of treatment effects. Control variables are age, education, gender, and vaccination status for COVID-19. Robust standard errors are shown in parenthesis, and Westfall-Young stepdown adjusted p-values in brackets (1,000 bootstrap replications). _*** p<_ 0.01 _, ** p<_ 0.05 _, * p<_ 0.1 28
# **Migration and Jobs: Issues for the 21st Century**

Luc Christiaensen, Alvaro Gonzalez and David Robalino<sup>**1**</sup>

**JEL classification:** H23, H53, J15, J61, J68

**Key words:** migration, jobs, labor, remittance, social insurance

> 1 Jobs Group, World Bank Group. The authors are grateful for comments from Margaret Ellen Grosh, Manjula Luthria, Dilip Ratha and two anonymous reviewers.
If the true test of the value of an economic theory is longevity, the human capital model passes with flying colors. Its basics are simple and empirically testable (and generally validated). An individual will invest in his or her human capital—an additional year of schooling or on-the-job training—as long as the marginal gain from that investment exceeds its added cost. The gains extend over a lifetime and are discounted to the present. If some of these gains accrue to others, governments need to stimulate individuals to take them into account in making decisions. Public action may also be needed if poor individuals cannot mobilize the resources to finance the investment now, despite a promise of big gains in the future.

This human capital framework has been a driving force for the huge investments in education in developing countries in the past 40 years. In addition to education’s social benefits, the recognition of its long-term economic benefits has spurred finance and budget ministries to action. In global terms, education spending has mushroomed from 3.7 percent in 1970 to 4.5 percent of GDP in 2002 (see Figure 1). This has led, by some accounts, to profound increases in the number of young people going to school, particularly at primary levels. There were more than 688 million children enrolled in primary schools globally in 2005, up 6 percent from 1999 alone. Worldwide average schooling level for the population was 5.0 years of schooling in 1970 attained across 103 countries (based on calculations from Barro and Lee’s (2000) database). There was a 31 percent increase by 1999, when average schooling went up 1.5 years of schooling to above 6.5 years.
8 through the mid 1980s and Is one of the most striking labor market developm nts In the last halt-century. These trends translate directly into earlie **r** etirement ages: for instance, in the United States, twenty years ago men retired at age 65 on average, and now the average retirement age is 62 (Ippolito 1986).

The general downward trend in market participation which characterized older men was also seen among older women across the OECD countries, but in a muted form. Participation rate trends of older women are more difficul **t** o summarize because many women entered the paid labor market only relatively recently, and have not accumulated enough years of service to retire with a pension plan in their 50's. Hence institutional factors no doubt provide an Incentive for women to remain on the job at older ages, which offsets the general trend to earlier retirement predominant for males. As Is evident from Table 2, women's market participation rates among those over age 65 have remained quite low across the OECD nations, and the slow change among those aged 55-64 reflects both positive and negative Influences on retirement pattems.

As a whole, the data show that fewer and fewer older persons ari. working for pay in developed nations. This trend is particularly striking in view of the fact that life expectancies In most of these countries have been rising in the last fifty years, and health problems for people in their 50s and 60s are, at the very worst, no more serious than they were fifty years ago (Baily 1987). Earlie **r** etirement is therefore increasingly the norm, despite the fact that people are now living longer in the retirement period.<sup>3</sup> Retirement Trends in Transition Economies: Tracking retirement trends is more difficult in the transition economies, which here are taken to mean Eastem Europe and the former Soviet Republics. These countries share some retirement system problems with those nations in Asia, Latin America and Africa conventionally Included in the developing country group. Time series are less readily available, surveys tend to be less comparable, and the meaning of labor force participation rates is less clear when large segments of the population are engaged in subsistence farming and related activities. Also older workers' health pattems tend to be worse in the developing nations. For example a recent survey of older workers' health status in Hungary pointed out that life expectancies have actually declined
differences estimation is less subject to biases introduced by unobserved heterogeneity across countries and measurement error is reduced as the focus is on one specific policy measure, implemented in the same way but at different times across sub-national units.<sup>26</sup> On the other hand, the events in different states, such as branch deregulation, were not independent from each other, but rather came in waves, which might bias the estimate of _β_ (Huang, 2008). Further, the concern of reverse causation can only be addressed by utilizing instrumental variables or by showing that the decision to implement the policy change across states is not correlated with future growth rates, as was done by Jayaratne and Strahan (1996).

Apart from the problem of endogeneity, serial correlation of the error terms in differences-in-differences estimations can lead to underestimation of standard errors, as shown by Bertrand, Duflo and Mullainathan (2004).<sup>27</sup> This problem increases with the number of time periods and the persistence of the dependent variable and is exacerbated by the fact that the treatment variable, e.g. branch deregulation, shows little change across states, at most one change from zero to one. Using Monte-Carlo simulation, Bertrand, Duflo and Mullainathan (2004) show that collapsing data to before and aftertreatment<sup>28</sup> or allowing for correlation within states (clustering) are solutions that resolve the problem of underestimated standard errors.

Going even more local, Huang (2008) uses county-level data from contiguous counties only separated by a state border in cases where one state deregulated at least three years earlier than the other. This helps reduce concerns of omitted variables, as one can assume a very similar structure of two contiguous counties and also helps reduce

> 26 On the other hand, focusing on one country reduces the policy relevance of its findings, as the relationship might vary across countries with different economic and institutional settings. Further, subnational variation might not be independent from each other given the higher mobility of capital and labor within than across countries.

> 27 Bertrand, Duflo and Mullainathan (2004) find overrejection of the null hypothesis using randomly assigned placebo treatments in Monte Carlo simulation

> 28 Specifically, this would imply regressing growth on state and year fixed effects and other time-varying control variables, taking the residuals and averaging them for the period before and after the treatment for each state. The estimate of the treatment can then be obtained from a regression of this two-period state panel on the treatment dummy.

23
P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. Standards errors allow for clustering at the Country level. All models are estimated via three methods: ordinary least squares (OLS) model, robust regression model and instrumental variable model. In dropping outlier OLS model, top 5% samples on each dependent variable are dropped except % of Women Borrowers. Though we do not present the coefficients to conserve space, all models include all of the following controls; annual % of inflation rate (GDP deflator), annual % of GDP growth, Rule of law , Bank overhead costs / Total assets, Ease of doing business index, Bank Credit / GDP, regional dummies (Europe and Central Asia, Latin America and Asia, Sub-Saharan Africa, South Asia and East Asia and Pacific, omitting Middle East and North Africa), status dummies (commercial bank, credit union / coop, NGO, non-bank financial institution, omitting rural bank), lending methodology dummies (individual and solidarity lending, omitting village banking) and log of MFI's total asset values. The details of instrumental variable model are shown as below.

*1 1st excluded IV is Supervisory Forbearance Discretion, and 2nd is Independence of Supervisory Authority - Political respectively

*2 1st excluded IV is Supervisory Forbearance Discretion, and 2nd is Percent of 10 Biggest Banks Rated by Domestic Credit Rating Agencies respectively

- - <u>Panel E: Formation/operation of non regulated microcredit institutions (0 4)</u>

||||<br>Dep var:|<br>% of Women|<br>borrowers|||
|---|---|---|---|---|---|---|---|
|_Microscope variable_|Robust Reg<br>w/o Lending<br>methodology<br>dummy|OLS w/o<br>Lending<br>methodology<br>dummy|Robust Reg|OLS|OLS w/ the<br>same<br>observations<br>to IV|Instrumental<br>Variable 1*<sup>1</sup>|Instrumental<br>Variable 2*<sup>2</sup>|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Formation/operation of non-regulated microcredit institutions (0-4)|0.0406***|0.0337|0.0333***|0.027|0.0787***|0.1357|-0.0585|
||[0.000]|[0.142]|[0.002]|[0.222]|[0.004]|[0.411]|[0.510]|
|Observations|713|713|611|611|530|530|510|
|Number of countries|40|40|39|39|29|29|28|
|R-squared|0.507|0.366|0.588|0.438|0.473|0.459|0.429|
|Adjusted R-squared|0.495|0.351|0.575|0.420|0.454|0.439|0.407|
|F statistic for weak identification||||||1.881|1.626|
|Shea partial R-squared||||||0.069|0.081|
|Hansen J statistics||||||0.290|0.212|

32
**Table 3. Data-generating processes and inequality measures**

|**I**<br>|**nequality **|**measure**<br>|**s**||**R‐squa**|**red by o**|**bserved**|**circumstan**<br>**'**|**ce**<br>**'**|
|---|---|---|---|---|---|---|---|---|---|
|**Error**||**Total**||||||**Fathers**|**Mothers**|
|**Distribution**|**Gini**|**MLD**|**IOO**|**All**|**Female**|**Urban**|**Region**|**education**|**education**|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|
||||_Pane_<br>|_l A. Bas_<br>|_eline scen_<br>|_ario_<br>||||
|_N(0,1)_|0.081|0.003|0.978|0.979|0.260|0.183|0.564|0.120|0.199|
|_N(0,5)_|0.100|0.005|0.635|0.647|0.172|0.122|0.372|0.080|0.132|
|_N(0,7)_|0.116|0.007|0.468|0.483|0.128|0.091|0.279|0.058|0.099|
|_N(0,10)_|0.144|0.011|0.299|0.314|0.086|0.058|0.180|0.038|0.064|
|_N(0,15)_|0.196|0.020|0.156|0.170|0.045|0.031|0.099|0.019|0.034|
|_N(0,20)_|0.252|0.035|0.091|0.104|0.027|0.019|0.059|0.012|0.020|
|||_Pan_<br>|_el B. In_<br>|_equality_<br>|_Enhance_<br>|_d scenar_<br>|_io_<br>|||
|_N(0,1)_|0.165|0.014|0.326|0.354|0.094|0.067|0.206|0.045|0.074|
|_N(0,5)_|0.180|0.017|0.272|0.298|0.080|0.056|0.172|0.037|0.062|
|_N(0,7)_|0.194|0.020|0.233|0.258|0.068|0.049|0.150|0.032|0.055|
|_N(0,10)_|0.219|0.026|0.176|0.199|0.053|0.039|0.116|0.025|0.043|
|_N(0,15)_|0.274|0.043|0.109|0.130|0.034|0.024|0.075|0.016|0.027|
|_N(0,20)_|0.335|0.069|0.069|0.087|0.021|0.017|0.051|0.012|0.018|

Notes: Data generating process is presented in equation (3). Error terms listed in the first column are randomly added to equation (3) to induce variation in incomes across individuals. R‐squared is obtained from an OLS regression using income as the dependent variable and the specified circumstance as regressors. MLD is the mean log deviation, IOO is the ratio of the smoothed distribution MLD and total MLD. All estimates are based on observing the full income distribution (i.e. no truncation).

21
# **Non-technical Abstract**

_This paper attempts to explain the determinants of the propensity to armed struggle and the probability of participation by individuals in the Niger Delta region of Nigeria using primary (micro) data. While grievance appears to be pervasive among individuals and communities in the region and can be systematically explained, neither the grievance level nor its commonly-cited causal factors appear to be strong enough to create a disposition towards armed rebellion. Rather, factors that reduce the opportunity cost and risk of participation or increase the perceived benefits appear to be more important. The study identifies three of these factors that are amenable to policy makers’ control as income level, educational attainment, and government presence._

# **Technical Abstract**

This paper attempts to explain the determinants of the propensity to armed struggle and the probability of participation by individuals in the Niger Delta region of Nigeria using primary data from a sample of 1,337 individuals drawn from 18 communities in the region. Nineteen variables are used to capture factors that reflect motives and opportunity for rebellion and a logit regression model is used to estimate the propensity to armed struggle in the population.

About 36 percent of the sampled population revealed a willingness to take up arms, which translates to a potential rebel army size of about 24 percent of the male population in the states covered. While grievance appears pervasive and is systematically explained by the data, neither the grievance level nor its commonly-cited causal factors, appear to be strong enough to create a disposition towards armed rebellion. Rather, factors that reduce the opportunity cost and risk of participation or increase the perceived benefits appear to be more important. Three communitylevel and five individual-level characteristics are particularly found to increase conflict risk in the region. Oil-availability, longer distance from state capital and absence of government presence makes a community a haven for would-be rebels. The propensity to armed struggle also increases in lower income level, lower educational attainment, lack of asset or asset-immobility, if an individual is unconstrained by a marital bond, or is from the dominant ethnic group. Three of these statistically significant variables are amenable to policy-makers’ control.

2
# _Table 1: Triple difference_

||(1)<br>|(2)<br>ln(FDI)<br>|(3)<br>|
|---|---|---|---|
||Trade War|Covid-19|Invasion of Ukraine|
|**I(t>Event) x I(US) x ...**||||
|... x I(Same country)|0.583*|0.781**|0.795***|
||(0.337)|(0.306)|(0.281)|
|... x I(Border)|0.233|0.804**|0.643**|
||(0.417)|(0.336)|(0.300)|
|... x ln(distance)|0.236**|0.034|0.078|
||(0.117)|(0.090)|(0.077)|
|... x I(NATO)|-0.067|-0.371*|-0.485***|
||(0.218)|(0.200)|(0.165)|
|... x I(Common language)|-0.099|-0.192**|-0.186**|
||(0.116)|(0.089)|(0.081)|
|**I(t>Event) x ...**||||
|... x I(Same country)|-0.516*|-0.490*|-0.486**|
||(0.311)|(0.256)|(0.237)|
|... x I(Border)|-0.251|-0.238|-0.257|
||(0.233)|(0.183)|(0.164)|
|... x ln(distance)|-0.311***|-0.105|-0.116*|
||(0.086)|(0.070)|(0.060)|
|... x I(Common language)|0.141|0.143*|0.089|
||(0.110)|(0.084)|(0.075)|
|Observations|20,088|27,540|34,992|
|R-squared|0.865|0.863|0.862|

Note: The regression includes source country-time, host country-time, and source country-host country fixed effects. The sample covers bilateral US and EU investment. In column (1), the sample covers the period from January 2015 to February 2020 (Trade War); in column (2), from January 2015 to January 2022 (COVID19); and in column (3), from January 2015 to December 2023 (Invasion of Ukraine). Robust standard errors, clustered at the source-time level, are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1

# **4. Robustness analysis**

This section presents a series of robustness checks. First, to handle the zero investment flows, we use the Poisson pseudo maximum likelihood (PPML) estimator. Second, to exclude that our results are driven by phantom investment in small countries, we estimate the baseline regressions on the sample of countries with a population greater than 1 million.

14