rafmacalaba/datause-encoder-data huggingface dataset:
better off than their off-grid counterparts in terms of total income and expenditure. Specifically, for households with access to electricity, per capita income is 29 and 26 percent higher, and per capita expenditure is 14 percent and 24 percent higher than those of households without electricity in 2005 and 2010, respectively. Notably, off-grid households have a higher per capita farm income in 2010. It is possible that while grid-connected households have moved from farm to non-farm activities over the years, off-grid households have maintained and expanded farm activities.

At the individual level, children in electrified households spend longer hours studying. For example, in 2010, girls in households with access to electricity studied about 2.4 hours per day, compared to 2 hours spent on study per day by girls who lived off-grid. Labor force participation among individuals was also higher in households with electricity in 2005, although the results were opposite in 2010. Finally, better empowerment outcomes are observed among women with access to electricity. For example, women are more likely to own income generation activities and have more decision-making power if they reside in households with access to electricity. While the differences are not statistically significant in 2005, they become much larger and statistically significant in 2010.

Table 4 describes mean outcomes of electrified households by average daily electricity availability based on 2005 survey data.<sup>3</sup> Results of five groups of households are compared – those facing daily outages up to 5 hours, between 6 and 10 hours, 11 to 15 hours, 16 to 20 hours, and above 21 hours or more. There is generally a positive correlation between hours of power outages and volume of kerosene consumption, and negative correlation between length of the outages and income and expenditure, study hours, labor force participation rate and women’s empowerment. Also noticeable is the fact that when outages are longer than 21 hours per day, there are almost no distinguishable differences between unelectrified and electrified households.

Table 5 reports mean outcomes of grid-connected households in 2010 based on their duration of exposure to grid electricity. Again, five groups of households are considered – those connected to electricity grid for up to a year, between 2 and 5 years, between 6 and 10 years, between 11 and 15 years, and 16 years or more. The findings reveal a positive correlation between the duration of exposure and household and individual socioeconomic outcomes. Notably, kerosene consumption goes down almost immediately after a household gains access to grid electricity. But other benefits manifest themselves only gradually over time. For example, increase in income and expenditure, and hours of study by children are only observed

> 3 The 2010 survey did not include questions on daily duration of power outages. So, all analyses related to the effects of quality of power supply are based on 2005 survey data.

7
This paper reports estimates of the labor market returns to an intervention that gave psychosocial stimulation and nutritional supplementation to growth-retarded toddlers living in poverty in Jamaica (Grantham-McGregor et al., 1991). Enrollment in the study was conditioned on stunting because it is an easily and accurately observed indicator of malnutrition that is strongly associated with poor cognitive development (Walker et al., 2007). The randomized treatment group assigned to stimulation received weekly visits for a period of two years from community health workers who actively encouraged mothers to interact and play with their children in ways designed to develop cognitive and psychosocial skills. Unlike the effects of many other early childhood interventions that fade out over time,<sup>9</sup> the Jamaican stimulation intervention proved to have large impacts on cognitive development 20 years later (Walker et al., 2011). In contrast, the nutritional intervention had no long-term impact on any outcome.

We use labor market information collected 20 years after the intervention when the participants were 22 years old. We show that stimulation increased average earnings by 42%. The magnitude of the estimated impact on earnings is put into perspective when compared to a non-stunted comparison group identified at baseline. In fact, the earnings of the treated stunted group completely caught up with the earnings of the matched nonstunted comparison group. These results provide evidence that stimulation interventions very early in life can compensate for developmental delays and thereby reduce inequality later in life.

We also examine pathways through which the intervention likely affected earnings. First, we find that the intervention increased maternal investment in children during the intervention period. Second, there are large effects on key determinants of earnings including schooling, cognitive development, and psychosocial development. Finally, we show that the treatment group was more likely to migrate to the U.S. or U.K., and thereby gained access to higher quality schools and better labor markets.

> 9See Cunha et al. (2006), Almond and Currie (2011) and Engle et al. (2011) for reviews.

2
assistance to improve productivity, and organized marketing facilities. Family income is therefore highly variable and there is little opportunity for saving. They have very few assets, including education, and are very vulnerable.

The differing characteristics of the smallholders, sharecroppers, and wageworkers suggest that a poverty reduction strategy needs to provide multiple paths out of poverty tailored to the heterogeneous cross-section of poor rural households. This will involve at least a five-pronged approach aimed at: (i) small farm sector intensification, (ii) improved employment opportunities in dynamic commercial agriculture, (iii) growth of the rural nonfarm sector, (iv) migration of the young, and (v) provision of safety nets for those “trapped” in poverty. The recommended measures include improving human capital endowments, reforming the land, labor, and financial markets, enhancing research and extension, improving the supply of public goods and services, pricing and trade policies, and transfer programs.

In order to increase land productivity and labor-intensive farming, it is necessary to facilitate the movement toward farming medium-sized land holdings, in part via facilitating land rentals and sharecropping arrangements. This can be done by providing more secure titles to land and by the revision of the land legislation so as to secure longer-term tenancy arrangements, resolution of disputes regarding interpretation, and enforcement of land rental arrangements. The impact of such a program would be greatly enhanced by simultaneous adjustments of the labor code and of the land tax system. Labor laws have had an antisharecropping bias. In this context, the experience with the Rural Leasing Exchange in the Triângulo Mineiro in Brazil contains useful lessons that could be worth considering for Mexico.

Extreme rural poverty is not distributed equally across population groups. The incidence of poverty is much larger among indigenous peoples that are at the bottom of the income distribution.<sup>6</sup> According to 2000 Census, 44 percent of indigenous groups are in the bottom 20 percent of the overall distribution of income, and 80 percent in the bottom 50 percent (Hall et al 2004). Hence, indigenous peoples account for about a fifth of the extreme poor that is, over twice their population share. Moreover, indigenous groups typically suffer higher levels of deprivation in terms of access to public services.

> 6 The ENIGH does not include a question on ethnicity.

22
# Table 1 (cont)

|CHARACTERISTICS|With<br>Training|Without<br>Training|
|---|---|---|
|Activity<br>|||
|Wage worker private sector|0.25*|0.09|
|Wage worker public sector|0.12*|0.05|
|Self-employed|0.21|0.24|
|Unpaid worker|0.11|0.12|
|Out of the labor force|0.30*|0.49|
|In the Labor Force a/|0.56*|0.44|
|Job specific|||
|experience (years)|3.8*|6.7|
||(4.9)|(8.4)|
|Usual weekly hours|||
|worked|34.7*|37.1|
||(19.1)|(21.8)|
|Months worked|||
|last 12 months|8.4|9.0|
|All Paid Workers b/|||
|Monthly earnings|756*|683|
|(intis June 1986)|(1,141)|(940)|
|Real hourly wage rate|5.87*|4.53|
|(intis June 1986)|(10.5)|(5.6)|
|Private Sector Worker|||
|Monthly earnings|692|718|
|(intis June 1986)|(587)|(746)|
|Real hourly wage rate|4.93|4.61|
|(intis June 1986)|(8.57)|(5.66)|
|Nonwage Sector Worker|||
|Monthly earnings|643*|595|
|(intis June 1986)|(1,587)|(1,009)|
|Real hourly wage rate|6.38*|4.02|
|(intis June 1986)|(13.60)|(5.75)|

Note: a/ Means include only women who reported positive hours of work during the 7 days or 12 months prior to the interview, regardless of whether they were paid.

- b/ Means include only those in a/ who reported payments in cash and/or in kind.

- Standard deviations in parentheses.

- Differences between means are statistically significant at 10 percent level or better.

- 14 -
<!-- Start of picture text --> Box 3. Evolution of the Yield Curve<br>Yield Curve as of January 1997 Yield Curve as of March 1997 Yield Curve as of June 1997<br>634 632 650<br>6 30 6i<br>N 626~~~~~~~~~~~~~~~~~~~6<br>3 ~ ~ ~ ~ ~ ~ ~~ ~~~~~~~~~~~~~~~~~ 3<br>* 625 I~~~~~~~~~~~~~~~~2<br>20-<br>623<br>2 6 6 'O 14 6 ~~~~~~~20 4 0 1 1 406 6 10 2 1<br>134N410I 1 26 8 0 1 1<br>M.hbtlA M66y<br>Short-terim interest rtes expected to fall Short-term interest rates expected tofall Short-term interest rates expected tofall<br>Yield Curve as of September 1997 Yield Curve as of December 1997 Yield Curve as of January 1998<br>a61s 74. 72M<br>-~~~~~~~~~~~~~~~~~~ ~~~~~~~~68<br>- ~~~~~~~~72.<br>hor . r ir r 6.6<br>X70 64<br>60. ,.~~~~~~~~~~~~~0<br>/ 60<br>6 2 a l 12 14 66 _ __ _ _ _ _ _66 Ss-__ _ _ _ _ _ __ _ _ _ _ _<br>2 4 6U 6 10 2 4 6 a 10 12 14 2 6 8 10 12 14<br>M"iN M~Ky Mday<br>Short-term interest rates expected to rise Short-term interest rates expected to rise Short-term interest rates expected to rise<br>Yield Curve as (if March 1998 Yield Curve as of June 1998 Yield Curve as of September 1998<br>a53 / 5.5<br>6.6 6 6.0~~~~~~~~~<br>64 ~~~~~~~~~~~~a5,<br>62. /7D<br>2 4 6 86 10 12 14 2 .4 6 6 110 12 14 2 6 6 86 2b 12 14<br>Short-term interest rates expected to rise Short-term interest rates expected to rise Short-term interest rates expected to rise<br>Yield Curve as of December 1998<br>64. ~ ~ ~ ~ ~ ~ ~ ~ 1<br>5 35<br>536<br>Short-term interest rates expected to rise<br>14<br><!-- End of picture text -->
informed and whether it applies conditional on being informed are sensitive to the expected benefits, that is, the probability of being accepted conditional on application multiplied by the utility benefit that one receives if enrolled in the program. This estimation is useful because it allows us to analyze the impact of changing program benefits on the different stages of participation.

Our results indicate that the marginal impact of monetary benefits on participation outcomes is small. This finding has important policy implications because it suggests that increasing benefits will not significantly increase participation. This conclusion is not unreasonable in the context of _Oportunidades_ because monetary benefits for participants are already quite high. These results also suggest that policy should focus on addressing barriers to participation that stem from other determinants of applying and acquiring information.

A key policy issue involves disentangling the effect on outcomes of the different stages in the participation process. On the basis of our model, which follows the approach developed in Heckman and Smith (2004), we present a statistical decomposition of the impact of different variables at the different stages of the participation process. We find that the information stage is the major source of variation in participation, accounting for more than half of the impact of almost every variable that we consider. The second main source of variation in participation is enrollment, accounting for more than a quarter of the impact of most variables, with application a distant third source. Policies directed at increasing participation in _Oportunidades_ should therefore focus more on these stages of the participation process. More generally, these findings reinforce the need for studies of program participation outcomes to take a disaggregated view of the participation process and, accordingly, to collect data to facilitate such an analysis (Heckman and Smith 2004). A general lesson arising from our work is that the enrollment stage and especially the information stage may be as important as the application stage in explaining participation outcomes in social programs. Because the outcomes of 4
stigmatized social groups, for example, is affected by their teachers’ opinions and expectations about their academic abilities (Frier 1993; Rist 2000). In experiments conducted by Hoff and Pandey (2008), lower-caste Indian students performed significantly worse in mental puzzles in situations where they are first reminded of their social status by the announcement of their social identity in the form of their name—a clear give-away of caste—and the presence of higher-caste individuals in the test group. In educational environments, the negativity and lack of self-confidence that arises from this process of internalization affects student learning. Other impacts that have been documented in experiments have been rejection of their social group (Brown and Pinel 1993), a discounting and withdrawing from the task or domain in which the discrimination takes place, (Steele, Spencer and Aronson 2002) and selfblame (Steele and Aronson 1999).

Recent research on poverty and the feelings of shame among the poor also highlights the way in which broader societal perceptions of those impacted by poverty have a profound psychological impact on the poor. The impacts of these perceptions include feelings of inadequacy that limit their agency, and experiences of shame associated with poverty which stop some poor people from accessing services.<sup>17</sup> Brain research using neuro-imaging techniques has found that the brain bases of social pain associated with exclusion are similar to those of physical pain.<sup>18</sup> In other words, being socially excluded is a painful experience. Little wonder then that people seek to avoid feeling excluded, for example, by avoiding efforts to join a group or an occupation if they think they might be excluded.

> 17See University of Oxford’s global research project on Shame and Poverty, http://www.ox.ac.uk/media/news_stories/2010/poverty_and_shame.html

> 18 Research subjects were scanned while playing a virtual ball-tossing game in which they were ultimately excluded. Paralleling results from physical pain studies, two parts of the brain called the anterior cingulate cortex and the right ventral prefrontal cortex were active during exclusion and correlated with self-reported distress (Eisenberger et al, 2003).

25
_5_ _Ways Out of Poverty - Diffising_<sup>_Best Practices and Creating Capabilities_</sup>

# **III.** BASIC WAYS TO PROMOTE<sup>PRO-POORGROWTH</sup>

**Alternatives to market economies.**<sup>So what can be donefor poor people to benefit from</sup> wealth creation? Today there<sup>are feweralternativeapproachestoeconomicsystems than</sup> at any time since Karl Marx wrote<sup>the Communist Manifesto in1848.Somestill hopefor</sup> non-market solutions, but they<sup>arestruggling,as exemplified in a recentspeech by Joao</sup> Amaral, a reformist member of<sup>the Portuguese CommunistParty: "Weare notsocial</sup> democrats or market-friendly<sup>socialists.Communismmeans replacing capitalismwith</sup> something better, even if we<sup>do not yet knowexactly what that will be."'Maybeat some</sup> future date a completely different<sup>economicsystem willemerge.For now we have to</sup> make some kind of system making<sup>use of markets.</sup>

**Redistribution of income.** The<sup>question becomeswhat pro-poor policiesmean in market</sup> economies. Some focus on redistributing<sup>incomes.For example,a guaranteed basic</sup> income for all was recently proposed<sup>by the BasicIncomeEuropean Network."2The</sup> massive redistribution required<sup>for sucha plan on a global scale would requirethat</sup> economic growth is adequate<sup>to fundit.Inaddition,it requireswillingnessof rich</sup> countries to fund the plan. Yet,<sup>the official development aid of all nations amounts toless</sup> than $0.15 for each person living<sup>on less than$1a day, and most of that has to be repaid.</sup> Compare that with the Republic<sup>of Korea, whichtook about10 yearsto generatemore</sup> income for its citizens from<sup>the manufacturing sector alonethan allof the world's</sup> development aid together (figure<sup>2).</sup>

**Figure 2: Growth achieves more**<sup>**thanredistribution**</sup>

<!-- Start of picture text --> 250.0<br>9 200.0<br>B 1500<br>1000<br>50.0<br>o 00-<br>1960 1965 1970 1975 1980 1985 1990 1995 2000<br>-Korea, M anufacturing Value Added<br>-Official Net Resource Flows to Dev Countrles<br>-Total Grants to Dev Countnes, Excl Tech Coop<br><!-- End of picture text --> Source: World Bank WDI Database.

**Creating productive employment.**<sup>Even doubling the world's official aid and</sup> transforming all loans into grants<sup>would notachievesustained economicgrowth in</sup> countries and areas where poor<sup>peoplelive. The hope for poor people lies in productivity</sup>

**t'** Wise, 2002. **12** Williams, 2002.
16

# IV. **Methodology and Results**

Methodology. We use a panel data approach to analyze capital flows to Latin America and Asia. This approach is employed for several reasons. First, the panel data estimation method is among the most efficient techniques to analyze the impact of a common set of global factors -ross a diverse group of countries. Second, this structure acknowledges that each country can have its own characteristics (country-specific effects), which can be correlated or uncorrelated with some or all the explanatory variables. Third, it is an appropriate method to alleviate the effects of omitted (missing and/or unobserved) time-invariant variables that are correlated with the explanatory variables. Fourth, the panel data technique solves or reduces some econometric problems by increasing the data points (increasing the degrees of freedom) and decreasing the collinearity among explanatory variables.

We investigate both gross and net flows. For reasons indicated above, we only discuss gross bond flows and net equity flows here. The model that we estimate is of the following type: Yit = a; + **X3t 0** + **u,t** (1) where i=1,2,...,N is a country index, t=1,2,...,Tis a time index, Yit is the explained variable (either net equity or gross bond flows), a; is the country specific constant which can be either fixed or random, Xi, is a set of explanatory variables, ,B is a vector of slope parameters, and **uit** is the usual random disturbance. Model (1) states that the slope of each explanatory variable is the same across all the countries and the differences among the countries are captured by the latent variable a;. The model can be rewritten as follows:

**Yit** = a; + **x'A** + z, y + **Uit**

where x,, is a set of country-specific factors (e.g., credit rating, secondary market price, price earnings ratio, and black market premium) and z, is a set of time-invariant variables which capture global factors (e.g., US interest rates and US industrial activity).

The appropriate estimation technique for model (1) or (2) crucially depends, among other factors, on the nature of the latent variable a,. If a, is fixed, i.e. correlated with some of the explanatory variables, and the model is over identified, then the two-stage least squares method (2SLS) a la Hausman-Taylor (1981) provides efficient estimates of a ad y. If _a1_ is fixed and the model is just identified, then 2SLS and within-estimator are equally efficient. If _a 1_ is random, that is uncorrelated with all the explanatory variables, then the generalized least square estimator, a weighted average of between-group and within-group estimators, is the appropriate technique.

Thus, prior to using any particular estimation technique, we test for the hypothesis that Ho: E **(ac I xt,** z;) = 0 against **HI:** E (a1 **I** x,,, z;) **;# 0.** The relevant x<sup>2</sup> test statistic (see Hausman and Taylor (1981, pp. i382-1383) is:
_5_ than currently being used by the firm t. In effect, we ask: What is the maximum value of Z,, a common scale by which all outputs of firms t can be increased by this synthetic firm? While not necessarily optimal, Z,= I (i.e., no expansion) is always feasible, because Aj-=O for j $t and Aj= I for j = t provides a feasible solution. When Z,*, the optimal value of (1), exceeds unity, the synthetic firm, representing the weighted behavior of a "reference set" of firms (those for which J4>0), could achieve at least the same output levels as firm t, using no more inputs than firm t. The measured efficiency of firm t is then defined as h*= _/IZ*._ If Z1*> 1, then h,* **<** 1 and firm t is technically inefficient relative to other firms in the sample. If Z,*= 1, then h,*= 1 and firm t is technically efficient, but again only in a relative sense. This linear program must be solved for each of the n firms in the sample, and it can be formally shown that only efficient firms (h*= 1) will enter into the "reference set" of any particular inefficient firm.

Unlike the previous DEA studies of education, we allow for variable returns to scale. Banker, Charnes and Cooper (1984) have added a constraint in their DEA program, where the sum of the weights (A.) for a reference set of firms is equal to one. This corresponds to the more general case of variable returns to scale and provides for richer and more diverse production behavior at segments of the production surface [e.g., see Seiford and Thrall (1990).] Figure 1. The **VRS** Requrement <!-- Start of picture text --> Isreiece A//X w c f<br><!-- End of picture text --> Consider the simplest case in which each firm uses one input (X) to produce a single output
# **Losing the Gains of the Past: The Welfare and Distributional Impacts of the Twin Crises in Iraq 2014**<sup>**1**</sup>

Nandini Krishnan Sergio Olivieri<sup>2</sup> _Keywords: oil, internal displacement, micro‐simulation, ex‐ante, crisis, Iraq_ _JEL: D39, E27, O15_

> 1 This paper has been benefited from discussions with Dean Jolliffe, Kevin Carey, Tara Vishwanath, Eric Le Borgne and seminar participants at the World Bank, workshops and conversations with senior technical counterparts from Government of Iraq and Kurdistan Region in Baghdad and Erbil, Iraq, May, 2015; and Beirut, Lebanon, June 2015. We are grateful to Andrea Germiniasi who provided outstanding research assistance on the validation section.

> 2 Nandini Krishnan: The World Bank Group, 1818 H St, NW Washington, DC 20433; nkrishnan@worldbank.org Sergio Olivieri: The World Bank Group, 1818 H St, NW Washington, DC 20433; solivieri@worldbank.org