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

Wars are detrimental to the populations and the economy of affected countries. Over and above the human cost caused by deaths and suffering during a time of conflict, survivors of conflict are often left in poor economic circumstances and mental health distress even after the conflict ends. How large are these costs? How long does it take for conflict-affected populations to recover from the mental stress of conflict? What policies are appropriate to assist mental health recovery? While considerable attention has been paid to post-war policies with regard to recovery in physical and human capital, mental health has received relatively less attention.

In this chapter, we review the nascent literature on mental health in the aftermath of conflict. We believe that mental health is an outcome that deserves greater attention from scholars and policy makers alike.<sup>4</sup> Mental health captures a dimension of individual welfare (or lack thereof) that is not perfectly correlated with alternative conventional outcome indicators such as poverty, consumption or income: for instance, Das et al. (2007, 2009) document an absence of correlation between mental health status and poverty in five developing countries. This result echoes the Easterlin paradox in the literature on happiness, which shows little correlation either within or across countries between the level of income and average happiness (Easterlin, 1974, 1995); in fact, the relationship between income and life satisfaction or happiness is a subject of considerable debate (see, among others, Deaton, 2008, and Stevenson and Wolfers, 2008). Furthermore, mental health is an important dimension of human capital. While economists have paid a great deal of attention to the effect of physical health on educational attainment and labor productivity (Maccini and Yang, 2009; Kremer and Miguel, 2004), little is known about the consequences of poor mental health on human capital or labor productivity.

In the specific context of conflict, there are likely to be severe mental health consequences in addition to consequences for physical health or economic wealth. People exposed to conflict have often suffered personal injuries, experienced the loss of friends and relatives and witnessed violent events. Such mental health distress, while a matter of concern in and of itself, might also have adverse consequences for individuals’ labor force participation and labor productivity in the post-conflict period, thereby delaying economic recovery after the conflict ends. Quantifying the effect of conflict on mental health is therefore likely to be important for designing appropriate post-conflict policies for recovery.

> 4 World Bank (2009) provides a review of the existing literature in this field.

2
# **Poverty Dynamics in India between 2004 and 2012: Insights from Longitudinal Analysis Using Synthetic Panel Data**

Hai-Anh H. Dang and Peter F. Lanjouw<sup>*</sup> World Bank

# JEL: C15, I32, O15

Key words: poverty dynamics, mobility, imputation, synthetic panel, India

> * Dang (hdang@worldbank.org) and Lanjouw (p.f.lanjouw@vu.nl) are respectively with the Poverty and Inequality Unit, Development Research Group, World Bank, and VU University, Amsterdam. This paper is a background paper for the India Poverty Assessment Report. We are grateful to Rinku Murgai, Ambar Narayan, Himanshu, Abhijit Sen, and participants at a workshop for the India Poverty Assessment Report (Washington, DC) and a seminar at Jawaharlal Nehru University (New Delhi) for helpful discussions on earlier versions. We thank Yichen Tu for very capable research assistance. We would further like to thank the South Asia Data for Goals program for financial support, and the UK Department of International Development for funding assistance through its Knowledge for Change and Strategic Research Programs.
Poland. The general conclusion is that the output drop has been far less sharp, particularly in the FSU, and this is a factor accounting for why unemployment has remained low.

**Table 1: Unemployment rates in East and Central Europe and the Former Soviet Union, 1992-95**

|**Country**|**1992**|**1993**|**1994**|**mid-1995**|
|---|---|---|---|---|
|**East & Central Europe**|||||
|Bulgaria|13.2|16.3 (21.4)|14.1(20.2)|13.0|
|Czech Republic|3.1|3.0 (4.1)|3.3 (4.0)|2.9|
|Hungary|10.7 (9.9)|12.8 (11.9)|11.3 (10.7)|10.5 (9.9)|
|Poland|12.9|14.9 (14.0)|16.4 (14.4)|15.0|
|Romania|6.2|9.2|11.0|10.3|
|Slovak Republic|11.3|12.9 (12.5)|14.6 (13.7)|13.8|
|FSU|||||
|Arnenia|0.2|5.2|6.4|5.5|
|Azerbaijan|0.2|_0.5_|0.8|0.9|
|Belarus|0.2|1.1|1.9|2.3|
|Kazakhstan|0.3|_0.5_|0.8|1.3|
|Kyrgyz Republic|0.1|0.2|0.4|1.3|
|Russia|0.4 (4.8)|1.0 (5.6)|1.7 (7.1)|2.7 (7.7)|
|Ukraine|0.3|0.4|0.4|0.4|
|Uzbekhistan|0.1|0.2|0.2|0.5|

Note: **The numbers are from registrations data. Figures in brackets are LFS numbers Source: OECD and World Bank** .However, while this highlights the importance of compensating output growth from sources other than the originally dominant state or privatised firms, it does not undermine the argument that output in the state or privatised sector has declined very significantly. As such, we still have to explain the discrepancies highlighted by Table 2. This shows that compared with East and Central Europe, changes in industrial employment relative to output since 1990 have run in opposite directions. In the former, employment has commonly adjusted faster than output. By contrast in the FSU there has 3
(ATT). We control for differences in pre-return income, household head age, gender, education, and household size, as the household head was slightly older and the household size slightly larger in the treatment group (see table 1). To understand whether ) should take a linear or quadratic form, we run regressions with both the day of return ( ) and the same model using an and 2 term, in order to look for an inflection point ii ff(xx and test whether the relationship between the outcome variables and the day of return is indeed ii ii ii xx xx xx nonlinear. After testing the functional form of ) , we find that the additional quadratic terms are not significant and therefore opt for a linear form for the day of return.<sup>11</sup> ii ff(xx After controlling for a set of household and individual demographic factors, we find that the results described in section 5 still hold. Households in the treatment group were 25 percentage points more likely to own a house, 23 percentage points more likely to have been issued _tazkira_ , and 24 percentage points less likely to have spent most of the cash assistance on food than households in the control group (table 6). Differences between the nonparametric model and the linear parametric model are particularly pertinent regarding education. In section 5, we did not find any impact of cash assistance on children’s school enrollment. In contrast, the parametric model suggests that households in the treatment group were 14 percent points more likely to enroll all their children in school than households in the control group.

# **Table 5 Results of linear parametric model on effect of UNCHR cash assistance on reintegration outcomes**

|_Item_|(1)<br>_Own house_|(2)<br>_Household_<br>_member_<br>_employed_|(3)<br>_Household_<br>_has_tazkira|(4)<br>_All children in_<br>_school_|(5)<br>_Spent most of_<br>_the assistance_<br>_onfood_|
|---|---|---|---|---|---|
|Cash amount|–0.245***|0.065***|–0.226***|–0.144***|0.238***|
|(1=$150, 0=$300)|(0.021)|(0.023)|(0.022)|(0.032)|(0.027)|
|Day of return|0.000|–0.000|0.001***|–0.000*|0.000**|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Transportation per capita|0.002***|–0.001*|–0.003***|–0.001*|0.001**|
||(0.000)|(0.001)|(0.001)|(0.001)|(0.001)|
|Household per capita Income||||||
|(RS)|–0.000|0.000**|0.000**|–0.000|–0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Household size (per||||||
|Voluntary Repatriation Form)|0.004*|0.007**|0.003|0.005|–0.005|
||(0.002)|(0.003)|(0.003)|(0.005)|(0.003)|
|Household head gender|0.057***|0.079***|0.057***|0.076***|–0.086***|
|(0 = Female, 1 =Male)|(0.015)|(0.019)|(0.018)|(0.028)|(0.021)|

> 11 We conducted a joint _F_ -test of significance of the quadratic term and predicted the post-estimation margins, in order to test for an inflection point or curvature of the week of return variable. Results are available upon request.

19
where is income of _p_ -th percentile in state _s_ at time _t_ =0. Income is always expressed in real terms. In regression (1) Gis is Gini of the poor π (or the rich ρ) in state _s_ (at time 0, omitted), Xh are regional controls estimated at time=0, Ds is state-level dummy or a broader regional dummy variable, and is the error term with the conventional properties. In a simpler formulation, _one-on-n_ , the Gini variable is Gs, that is overall state Gini. But in a more complex formulation, _two-on-n_ , the regression is estimated across percentile growth rates for both Ginis (that is, for the Gini for the poor and for the rich). We run three formulations of (1): pooled regression with four US regional dummies (East, Mid-West, South, West), (2) generalized method of moments, or system GMM shown as part of our robustness checks, and (3) fixed-effects across individual states (shown in the Annex).

Note that we could in principle push this all the way to an _n-on-n_ approach, where one could look at how inequality at each percentile of income distribution affects growth at every percentile of the income distribution. But pushing disaggregation too far has its costs: we would leave out the between-percentile inequality which is, when the partitions are fine, by far the most important part of total inequality. Empirically, inequality within each percentile (except for the very bottom and the very top)<sup>16</sup> is very small, and it could be hard to argue that it may affect growth at its own percentile or, for that matter, any other.

Therefore the disaggregation level has to be reasonably wide to include enough inequality and also to provide a credible causal narrative whereby inequality around one part of the distribution may affect growth at that or another part of the distribution. We do this by separating total inequality into inequality among the bottom of the income distribution (bottom 40%) and inequality among the top 40% of the population and then allowing bottom and top inequality to have different effects on growth of bottom and top incomes.

> 16 Based on some dozen household surveys from various parts of the world, we find that the values of percentile-level Ginis are less than 0.02. It is only among the bottom and top 1%  or top 2% that Ginis are higher.

10
WPS3460

**Schooling and Labor Market Impacts of a Natural Policy Experiment**

Harry Anthony Patrinos<sup>*</sup> The World Bank <u>hpatrinos@worldbank.org</u> Chris Sakellariou<sup>∗</sup> School of Humanities and Social Sciences Nanyang Technological University, Singapore <u>acsake@ntu.edu.sg</u>

**Abstract** : We use a nationally representative household survey to estimate returns to schooling in Venezuela from instrumental variables based on a supply-side intervention in the education market. These estimates apply to a subgroup of liquidity-constrained individuals, in the spirit of the local average treatment effect (LATE) literature. Returns to schooling estimates that apply to a subgroup of individuals affected by the policy intervention may be more interesting from a policy perspective than the return to the “average” individual. We use an instrument based on the 1980 education reform (the Organic Law of Education), which provided for nine years of compulsory basic education. Alternative estimates using father’s education as an instrument are also obtained in an attempt to derive high and low estimates of returns to schooling in Venezuela. The estimates are consistent with recent findings suggesting that the effect of education, at least for certain subgroups affected by policy intervention, is as large as or larger than what is suggested by ordinary least squares estimates.

JEL Classification: J24, J31 Key words: Endogenous education, education reform, treatment effects, Venezuela

# World Bank Policy Research Working Paper XXXX, Month 2004

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ ∗ The views expressed here are those of the authors and should not be attributed to their respective employers. The authors received useful comments from George Psacharopoulos and Emiliana Vegas.
Figure 8: Effects of Removing the Pension System <!-- Start of picture text --> Earnings = 100% Average Earnings=50% Average<br>0 Discount rate (-) 6<br>-0.05 0.00 0.05 0.10 0.15 0.20 0.25 Cost formal work (-)Prob. working in retirement (-)<br>-1 4<br>-2 2<br>Discount rate (+)<br>-3<br>0<br>-4 -0.40 -0.30 -0.20 -0.10 0.00 0.10 0.20 0.30 0.40<br>-2<br>-5<br>Discount rate (+) Cost formal work (+)<br>Prob. working in retirement (+) -4<br>-6<br>-7 -6<br>-8 -8<br>-9 -10<br>Change in Contribution Density (% of Time) Change in Contribution Density (% of Time)<br>Change in Retirement Age (# of Years) Change in Retirement Age (#of Years)<br><!-- End of picture text --> _Source:_ Simulation model.

Finally, in the largest group, there are individuals for whom the pension system provides incentives to delay retirement (because of eligibility for the minimum pension) but who do not have high contribution densities (for instance given a high probability of work during retirement or a high disutility of formal sector work) and thus do not reduce them (otherwise they would not meet the eligibility conditions). When the pension system is removed these individuals appear within the ellipse of the chart: they reduce retirement ages but leave contribution densities more or less unchanged.

Regarding the unemployment insurance system there are two effects that one could expect. For some workers removing UI would provide more incentives to self-insure – which implies spending more time in the formal sector and saving more. For others, eliminating UI implies loosing implicit subsidies that are financed entirely by the employer and this reduces incentives to enroll in the social security. For high income workers the first effect dominates – there is only one case where the contribution density falls which is associated with a high disutility of formal employment. When loosing UI these workers contribute more and then can also afford to retire earlier. For low income workers in half of the cases the same phenomenon is observed and contribution densities increase. In the other half of the cases, however, contribution densities decrease. These cases are characterized by a high disutility of formal work, a high discount rate, and a high probability of work during retirement. And indeed, as discussed in the previous section, the first two parameters tend to have higher values among low income workers. For these individuals eliminating UI reduces the incentives they have to engage in formal sector work which is _naturally_ costly.

26
2013; Kunreuther et al., 2013).<sup>2</sup> We introduce a Weather Insurance Savings Account (WISA), which combines features of a savings account with rainfall index insurance by indexing the return on savings to rainfall realizations. To fix ideas, denote the insurance share of the WISA by _γ ∈_ [0, 1]. Then _γ_ = 0 implies pure savings, with a standard (non-rainfall-dependent) interest rate on savings, and _γ_ = 1 implies pure insurance. Intermediate values of _γ_ would provide returns equal to a linearly weighted average of the insurance payout and the regular interest earnings.<sup>3</sup> We an insurance model to demand for WISAs as a adapt intertemporal study function of _γ_ . The most important prediction from the model is that under the standard regularity conditions, demand as a function of _γ_ cannot have a local minimim. Practically, this means that demand for a mixture of savings and insurance (0 _< γ <_ 1) must be higher than demand for pure insurance ( _γ_ = 1), pure savings = ( _γ_ 0), or both. The model does not allow demand for a mixture of saving and insurance to be below demand of both pure products. We then present a laboratory experiment that tests this key prediction. As the experiment uses real products offered to real farmers, it also serves as a pilot for the WISA concept. Preferences for WISAs with a range of _γ_ ’s were elicited using a Becker-DeGroot-Marschak (BDM) incentive compatible mechanism. We also measured risk and time preferences. Contrary to the model’s central prediction, our within-subject design reveals many participants value both pure savings and pure insurance more highly than any interior mixture of the two. This preference for pure products is stronger for those with higher risk aversion.

We then evaluate three candidate alternatives to the standard model in an attempt to account for participants’ puzzling preferences for both pure products over interior mixtures of them. First, farmers may value mixtures less because they are harder to understand than pure products. Experimentally, we manipulated whether

> 2Bundling rainfall insurance with loans has previously been attempted. The Weather Based Crop Insurance Scheme (WBCIS) of The Agriculture Insurance Company of India (AICI) saw substantial rainfall index insurance coverage when it was a compulsory add-on to agricultural loans. Similarly, the NGO Microensure provides weather insurance exclusively tied to loans. In experiments by Giné and Yang (2009), requiring insurance as a loan add-on decreased demand for the loan.

> 3If the nominal interest rate on normal savings accounts is _i_ , then nominal losses are possible in a WISA when _γ >_ 1+ _<u>i</u> i_<sup>.</sup>

3
non-oil and gas exports decreasing at relatively high rates (China for the case of Kyrgyz Republic, Russia for the case of Moldova, and USA for Tajikistan and Turkmenistan).<sup>12</sup> Figure 16a: Growth orientation of markets (RP).

Figure 16b: Growth orientation of markets (RR).

<!-- Start of picture text --> ARM: Growth Orientation of Markets AZE: Growth Orientation of Markets<br>Top 10 Markets Top 10 Markets<br>USA GEO GEO<br>IRN RUS<br>ESP DEUITA IRN TURUKR RUS<br>BEL<br>NLD<br>CHN<br>DEU<br>ITA<br>ISR USA<br>-60 -40 -20 0 20 -20 -10 0 10 20<br>WLD: CAGR of Exports 05-10(%) WLD: CAGR of Exports 05-10(%)<br>BLR: Growth Orientation of Markets GEO: Growth Orientation of Markets<br>Top 10 Markets Top 10 Markets<br>BGR<br>BRA<br>CAN UKR<br>UKRPOL<br>LVADEU LTU DEU AZETUR ARM<br>CHN<br>ITA ITA<br>FRA<br>USA<br>RUS<br>-20 -10 0 10 20 30<br>WLD: CAGR of Exports 05-10(%) -40 -20 0 20 40<br>WLD: CAGR of Exports 05-10(%)<br>KGZ: Growth Orientation of Markets KAZ: Growth Orientation of Markets<br>Top 10 Markets Top 10 Markets<br>RUS<br>IRNTUR TUR CHN<br>DEU USA<br>JPN<br>UKR IDN<br>CHN ITA<br>KOR<br>DEU<br>USA<br>UKR<br>RUS<br>CAN<br>IRN<br>-60 -40 -20 0 20 -10 0 10 20<br>WLD: CAGR of Exports 05-10(%) WLD: CAGR of Exports 05-10(%)<br>20 20<br>0 10<br>-20 0<br>-10<br>-40<br>ARM: CAGR of Exports 05-10(%) AZE: CAGR of Exports 05-10(%)<br>-20<br>-60<br>30 40<br>20<br>20<br>10<br>0<br>0<br>-10 -20<br>BLR: CAGR of Exports 05-10(%) GEO: CAGR of Exports 05-10(%)<br>-20<br>-40<br>20<br>20<br>0<br>10<br>-20<br>0<br>-40<br>KGZ: CAGR of Exports 05-10(%) KAZ: CAGR of Exports 05-10(%)<br>-60 -10<br><!-- End of picture text --> 12 Some of this was driven by the trade collapse of 2008-2009.

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- 23 -

benefits of program participation outweigh the costs within 3 months of starting employment. For all other males except those with no prior work experience, the benefits outweigh the costs within one year. Males with no prior work experience, on the other hand, spend the longest time in job search after training (8 months as compared to the trainee mean of 4.4 months) and benefit relatively less from training in terms of monthly earnings (128 thousand pesos as compared to the average benefit of 152 thousand pesos). For this group, the costs of training are offset only after 17 months of higher earnings upon finding employment. For women, we find that program benefits from earlier employment offset the costs for those with prior work experience and for those over 25 years. Although we do not find significant wage effects, for these women the program pays for itself. For other women, the costs exceed the benefits.

In short, the PROBECAT retraining program appears to be cost-effective for improving the economic conditions of most groups of unemployed males, except for those with no prior work experience. It also appears to be costeffective in reducing search time for certain groups of unemployed females, namely older, more experienced female workers.

# Policy ImRlications

This evaluation suggests that the unemployed constitute a heterogenous group and that eligibility criteria for program participation are important determinants of the cost-effectiveness of the program. In the specific case of Mexico, the analysis suggests that the selection criteria should be modified to target those demographic groups likely to benefit most from the program, namely unemployed workers with prior work experience, slightly older workers (aged over 25), and those with 6 to 12 years of schooling. For certain groups--the very young, new entrants into the labor force, and those with low levels of schooling attainment--it may be more important to provide other kinds of training, introduce apprenticeship programs to give new entrants some work experience, or facilitate return to school.

More broadly, our study demonstrates that the results can be very sensitive to the way in which training effects are measured. We identified two principal sources of bias that many manpower training evaluations are likely to encounter: biases arising from (a) sample selectivity and (b) from 13/ Some of these conclusions have already been incorporated into the design or the new PROBECAT program under the Mexico Labor Market and Productivity Enhancement Project (1992).
# `Kalimantan`

||`(1.75)`|`(1.75)`|`(2.06)*`|`(1.87)`|
|---|---|---|---|---|
|`South Sulawesi`|`1.034`|`0.992`|`0.526`|`0.451`|
||`(3.36)**`|`(3.22)**`|`(1.99)*`|`(1.70)`|
|`Non-farm dummy`|`-0.641`||`-0.480`||
||`(6.28)**`||`(4.84)**`||
|`Mining_Quarry`||`-1.660`||`0.160`|
|||`(2.91)**`||`(0.42)`|
|`Manufacturing`||`-0.392`||`-0.345`|
|||`(2.63)**`||`(2.28)*`|
|`Electricity`||`-0.774`||`-1.431`|
|||`(1.51)`||`(1.40)`|
|`Construction`||`-0.416`||`0.061`|
|||`(2.00)*`||`(0.32)`|
|`Wholesale`||`-0.740`||`-0.779`|
|||`(5.50)**`||`(5.92)**`|
|`Transport`||`-0.785`||`0.112`|
|||`(3.94)**`||`(0.59)`|
|`Finance`||`-0.840`|||
|||`(1.20)`|||
|`Social_services`||`-0.723`||`-0.805`|
|||`(3.55)**`||`(5.27)**`|
|`Constant`|`-1.020`|`-1.085`|`0.201`|`0.330`|
||`(1.09)`|`(1.17)`|`(0.20)`|`(0.33)`|
|`Observations`|`5227`|`5227`|`5010`|`4980`|

`Robust z statistics in parentheses` * significant at 5%; ** significant at 1% 32