rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Descriptive Statistics for Women Ages 15-45 With No Schooling

||Mean|Standard<br>Error|Min|Max|
|---|---|---|---|---|
|Children even born|4_._14|2_._73|0_._00|13_._00|
|Age 20-24|0_._17|0_._37|0_._00|1_._00|
|Age 25-29|0_._21|0_._41|0_._00|1_._00|
|Age 30-34|0_._20|0_._40|0_._00|1_._00|
|Age 35-39|0_._18|0_._38|0_._00|1_._00|
|Age 40-45|0_._17|0_._38|0_._00|1_._00|
|Orthodox|0_._54|0_._50|0_._00|1_._00|
|Muslim|0_._26|0_._44|0_._00|1_._00|
|**Community characteristics**|||||
|Total area (square km/100)|15_._51|10_._10|0_._00|53_._81|
|Avg. yearly rainfall (mm/100)|11_._97|4_._25|4_._46|20_._48|
|Avg. yearly rainfall squared (mm/100)<sup>2</sup>/100|1_._61|1_._05|0_._20|4_._19|
|Elevation (m/100)<br>|19_._36|4_._18|8_._65|29_._26|
|Elevation squared (m/100)<sup>2</sup>/100|3_._92|1_._62|0_._75|8_._56|
|Urban (rate)|0_._04|0_._20|0_._00|1_._00|
|Market in area (rate)|0_._35|0_._48|0_._00|1_._00|
|Road access - all year (rate)|0_._41|0_._49|0_._00|1_._00|
|Road access - dry season (rate)|0_._41|0_._49|0_._00|1_._00|
|Percent with no education in zone|79_._97|6_._40|30_._07|94_._83|
|Percent with 1-6 years of education in zone|11_._17|5_._42|2_._05|37_._45|
|Percent with 7-8 years of education in zone|2_._28|1_._27|0_._20|10_._70|
|Population / 1000|3_._08|4_._90|0_._35|96_._94|
|Access to family planning (rate)|0_._18|0_._38|0_._00|1_._00|
|Observations||1326|||

**Notes.** Estimated means and standard errors based on sample frame and weights.

# **4 Results**

We begin by examining results of our estimates for equation (1) on the placement of programs. Table 3 presents the results from the determinants of placement. The dependent variable is whether a given community was within 40 kilometers of the nearest family planning program in 1990. Most of the variables have the expected signs. Areas that have a market are statistically significantly more likely to also have access to family planning services. Furthermore, urban areas and areas with easier access, as measured by whether there is road access by car either all year or during the dry season, are more likely to have a program, although the effects are not statistically significant.

The main variables of interest are the rank variables that identify program placement. Almost all instru12
11 accidents and injury rates rise with age in the United States: work accidents are lower for older than for younger employees, but little is known about pattems in other countries.3 As yet there is litle evidence to support or to refute the view that worsening health and productivity problems among older persons explains trends toward earlier retirement in the set of developed countries. If anything, most older persons in developed nations are probably living longer and in probably as good health (if not better) as compared to previous decades.' In developing countries, by contrast, this conclusion may not hold since urbanization in many developing countries sometimes exposes people to more disease, pollution, and risk of injury than In wealthier economies.

Mandatory retirement and age discrimination: Retirement research over the last decade casts serious doubt on the view that age discrimination and mandatory retirement rules explain observed early retirement trends in developed countries.

To summarize the labor economics argument, long-term compensation contracts with upward-sloping pay profiles are often used as a means of spurring worker productivity and ensuring long tenure. Ex ante, a long-term contract specifies that an employee will leave the firm at the point when his total compensation comes to exceed his total productivity (Lazear 1979; Gustman and others 1992). From this perspective, the age of mandatory retirement plays a central and valuable role: it is the pre-agreed point at which the labor contract comes to an end, after which the employee would be overpaid relative to his value to the firm. Hence mandatory retirement is rationalized as an economically important phenomenon, rather than as evidence of employer age bias without economic rationale.

The hypothesis that mandatory retirement demarcates the end of a long term labor contract is supported in several research studies. In the U.S. for instance, individual companies (and unions) were originally permitted to set mandatory retirement ages until a recent change in federal policy outlawed the practice. After the law was changed, companies responded by developing alternative Inducements for retirement, including pension incentives and special early retimment wndowM plans. In Japan many firms retire workers on attaining a retirement age, but do not necessarily require that older workers drop out of the labor force - instead, many Japanese employers re-hire these older workers (at
Pires, T., and S. Urzua. 2011. “Longer School Days, Better Outcomes?” Manuscript, Northwestern University, Evanston, IL.

Pischke, J. 2007. “The Impact of Length of the School Year on Student Performance and Earnings: Evidence from the German Short School Years.” _Economic Journal_ 117 (523): 1216–42.

Powell, C. A., S. P. Walker, S. M. Chang, and S. M. Grantham-McGregor. 1998. “Nutrition and Education: A Randomized Trial of the Effects of Breakfast in Rural Primary School Children.” _American Journal for Clinical Nutrition_ 68: 873–79.

Rice, J. K., R. G. Croninger, and C. F. Roellke. 2002. “The Effect of Block Scheduling High School Mathematics Courses on Student Achievement and Teachers’ Use of Time: Implications for Educational Productivity.” _Economics of Education Review_ 21: 599– 607.

Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. _Biometrika_ , _70_ (1), 41-55.

Shephard, R. J., H. Lavallee, M. Voile, R. LaBarre, and C. Beaucage. 1994. “Academic Skills and Required Physical Education: The Trois Rivieres Experience.” _CAHPER Journal Research Supplement_ 1: 1–12.

United Nations Development Program (2013). Atlas Brazil 2013.

Valenzuela, J. P. 2005. “Partial Evaluation of a Big Reform in the Chilean Education System: From a Half Day to a Full Day Schooling.” Doctoral dissertation, University of Michigan.

Xerxenevsky, L. L. 2012. “Programa Mais Educação: avaliação do impacto da educaçãp integral no desempenho de alunos no Rio Grande do Sul.” Doctoral dissertation, Pontifícia Universidade Católica do Rio Grande do Sul.

30
variables method to overcome potential selection issues in index-based livestock insurance (IBLI) uptake. We exploit the randomization of incentives to purchase IBLI, newly introduced in southern Ethiopia by a commercial underwriter in August 2012. The novelty of the product obviates the potential confounding of past, unobserved experience with IBLI on buyers’ reported SWB. Second, three-round panel data enables us to control for time-invariant household unobservable characteristics that might affect both SWB and IBLI uptake. Third, no indemnity payouts occurred during this period.<sup>4</sup> Without indemnity payments, we exploit the considerable intertemporal variation in households’ IBLI uptake to isolate the causal effect of IBLI on SWB. We use coverage active during a survey round to capture _ex ante_ welfare effects and coverage that had lapsed by the time of the survey to capture _ex post_ impacts. These data offer an unprecedented opportunity to estimate the SWB effects of insurance that arise purely from _ex ante_ risk reduction and to disentangle them from _ex post_ buyer’s remorse effects.

We find that current IBLI coverage improves SWB. Lapsed IBLI contracts that did not pay indemnities have a negative effect on SWB, consistent with the buyer’s remorse hypothesis. Although both effects are statistically significant, the welfare gains of current coverage significantly exceed the adverse buyer’s remorse effects. Our results are robust to a range of alternative estimators, corrections to address concerns on the measurement of SWB, variable definitions, model specifications and variations in the relevant panel sub-samples analyzed. Further, we show that the estimated SWB gains from insurance are downwardly biased if one omits control for lapsed insurance coverage that generates buyer’s remorse.

The implication is that, despite premiums set above actuarially fair rates, IBLI improves buyers’ SWB even over a period when pastoralists in southern Ethiopia lose money on the policy. The _ex ante_ peace of mind effect dominates any _ex post_ buyer’s remorse. In other words, even an insurance policy that does not pay out still improves people’s perceptions of their well-being.

The remainder of the paper is organized as follows. The next section presents the study setting and discusses IBLI and its contract design. Section 3 discusses the sampling and experimental design. Section 4 reports summary statistics of the data. Section 5 introduces our estimation strategy. Section 6 details our vignette correction strategy, following best current practice in the SWB literature. Section 7 reports our main results. Section 8 presents

> 4The first IBLI indemnity payments – on 509 contracts yielding total payments of ETB 526,000 (approximately $ 26,225) – occurred in October-November 2014, after the period covered by our data.

4
Tenure Security Premium in Informal Housing Markets: A Spatial Hedonic Analysis

# Shohei Nakamura<sup>_∗_</sup>

**Keywords** : slums, property rights, spatial econometrics, hedonic model, India **JEL Classification** : O18, R21, R22

> _∗_ World Bank, 1818 H Street, NW, Washington, D.C. 20433, United States. Phone: +1-202-473-4200. E-mail: snakamura2@worldbank.org. I would like to thank the Pollman Fellowship, which allowed me to stay at Harvard Graduate School of Design while working on this paper. Richard Peiser, Kirti Joshi, and participants in a workshop at the World Bank gave me very useful comments on the earlier draft of this paper.
To retrieve the effect on wages from the change in prices induced by trade liberalization, I utilize the results on the effects of trade liberalization on prices of section 4.1 and the effect on prices on wages of section 4.2.

In this setup the effect on wages is simply given by: where _dwr_ ( _s_ , _u_ ) represents the percentage change in the wage in region _r_ of skilled (s) or unskilled (u) individuals, β _grs_ is the price-wage elasticities for skilled and unskilled wages for good _g_ in region _r_ , and _dwgr_ is the percentage change in price.

Table B.2 summarizes the estimated effect of trade liberalization to the labor income of skilled and unskilled individuals in the five regions. Skilled wages have increased in four of the five regions, while unskilled wages increased in three of the five regions. Wages in the border, northern and central regions have increased for both skilled and unskilled workers, while in the Mexico City area and in the southern states unskilled wages have declined.

More specifically, skilled wages have increased by 3.5, 5.3 and 2.9 percent in the border, northern and central regions respectively while unskilled wages have increased by 0.6, 4.4 and 1.1 percent. In the case of the Mexico City area, skilled wages increased about 4.4 percent as a result of trade liberalization while unskilled wages decreased by about 3.8 percent. In the southern regions, mostly dominated by rural agriculture, trade liberalization did not have any effect on skilled wages. Unskilled wages have decreased by about 2.8 percent, mostly driven down by the lower prices of agricultural products. In all regions skilled wages have increased more than unskilled wages.

44
reported having constructed a toilet in the last two years in treatment communities versus 13 percent in control communities. This difference is statistically significant ( _p_ =0.07) _._ To reconcile the finding that the percentage of households with access to improved sanitation did not increase in treatment villages relative to control villages with the finding that more toilets were built in treatment villages, Table 9 examines toilet building more closely. It shows that 275 (14.4 percent) of the 1,908 panel households reported constructing a toilet in the last two years, with 151 (16 percent) of these toilets built in treatment communities, and 124 (13 percent) built in control communities. Not all construction resulted in a shift from “unimproved” to “improved” sanitation—102 of these toilets were built by households who already had access to improved sanitation. Only 180 of the households that built toilets improved their sanitation as a result. The number of households that built a toilet and consequently improved their sanitation is only very slightly higher in treatment communities than in control communities (9.7 versus 9.2 percent) and not statistically significant ( _p_ =0.73). Note, however, that health may improve as a result of toilets being built by households that already had access to “improved sanitation.” For example, many of the households that had improved sanitation at baseline and had constructed a toilet by endline had upgraded to a porcelain latrine, which may be easier to keep clean and so more hygienic. Similarly, health benefits may accrue to households that don’t have access to “improved” sanitation but have nevertheless gone from defecating in the open to using a pit toilet (without a concrete slab).

The endline survey also asked respondents when the toilet was built. Figure 4 reports the percentage of households that built a toilet prior to the endline survey, which was conducted between November 2010 and February 2011. Overall, a high percentage of households reported that they built their toilet between 12 and 24 months prior to the endline survey—almost 40 percent of the treated households reported that their toilet facility was built 24 months prior to the endline survey. The figure is slightly smaller for households in the control group (32 percent).

16
Figure 1 goes about here.

In the next section, I review the existing evidence on the impact of conflict on education and family formation, with an emphasis on male-female differences, before turning to the presentation of the Nepalese context in Section 3, data and estimation methods in Section 4, and estimation results in Section 5. Section 6 concludes.

# 2. Literature Review

2.1. Education As put by Blattman and Miguel (2010) in a recent survey of causes and consequences of civil wars, “The leading question is not whether wars harm human capital stocks, but rather in what ways, for whom and how persistently” (p.64). The evidence is still scant, however, and there is no emerging consensus. A number of cross-country analyses suggest that political instability has large negative effects on growth (see Blattman and Miguel (2010) for a review), but results from better-controlled studies using within-country variation in the intensity of conflict are more mixed. Miguel and Roland (2006) find that, 25 years down the line, living standards and levels of human capital had converged between regions of Vietnam differentially affected by bombings during the Vietnam War after instrumenting the number of bombings with distance to the 17<sup>th</sup> parallel north latitude. Other within-country, macro-studies have investigated the impact of bombing on city-level population growth, with varied results as to the duration of the war effect. Davis and Weinstein (2002) found that, in Japan, the destruction only had a shortterm effect on population growth. Findings in Brakman et al. (2004) indicate that most but not all of the effect of WWII bombings in Germany was temporary in Western Germany, but that it was persistent in the long run in Eastern Germany.

At the microeconomic level, results from an emerging (difference-in-difference) literature on the impact of war-related destruction or civil conflict on educational attainment suggest that the effect of conflict is highly heterogeneous, and depends on conflict characteristics, age and gender. Although results vary from one context to the other, overall the main losers appear to be girls in post-primary education, while the evidence of an adverse impact for primary education is generally non-existent (or even sometimes suggests a counterintuitive positive effect). AkbulutYuksel (2009) finds that German individuals of school age during World War II who lived in cities more heavily targeted by allied bombings had significantly less education than cohorts born later, compared to individuals of the same age exposed to less bombings. In Guatemala, where the worst period of the Guatemalan civil war (1979-1984) saw over 25,000 deaths (to be compared with a current Guatemalan population of 13 Million), Chamarbagwala and Morán 5
deviations of mean (real) income and inequality from the national level. This decomposition allows us to uncover the dominant factor between these two.

Our results for some important members of the class of additively decomposable poverty measures defined by equation (2.1) are presented in table 2.5 (a&b for 2001 and 2007 respectively). There are six regions (the two major cities, and the coastal, western, southern and south-western provinces of where poverty is generally below the national average in both 2001 and 2007. Poverty is above the national average for the other six regions. The overall pattern that emerges from these results is that, except for the western, southern and south-western provinces, the real income (scale) effect dominates (in magnitude) the inequality effect in 9 regions. Thus regions (among these 9) with lower poverty rates than the national average tend to have average real income higher than the national average. Similarly, average real income tends to be lower than the national average for those regions (out of 9) with higher poverty rates than the national average. Poverty levels in the West, South and South-West tend to be lower than the national average due to lower inequality.

The above results suggest that regional disparity in Cameroon is mostly due to differences in average real income, an indication of significant between-group inequality. The results of similar analysis applied to rural-urban differences for 1996, 2001 and 2007 are presented in table A5-A7 in the appendix. These results confirm the urban bias noted earlier to the extent that urban poverty is consistently below the national average while rural poverty is consistently above. A close look at the Shapley contributions reveals that rural poverty would be much higher than the national average if rural inequality were not lower than the national average. For instance in 2007, the incidence of rural poverty would have been about 21 percentage points higher than the national average if rural inequality had been at the level of overall inequality. The observed difference stood at 15 points because the inequality effect was -6 percentage points.

15
**Table 16. Explaining cross-country variation in efficiency, Multiple Inputs and Multiple Outputs - Heterogeneous Slopes**

|Independent<br>Variable|EDU2-2|EDU2-2n|EDU3-2|EDU3-2n|EDU3-3|EDU3-3bl|HEA2-2|HEA3-2|HEA3-2m|HEA3-3|
|---|---|---|---|---|---|---|---|---|---|---|
|WAGE|.00051|-.00140|.00005|.00494|-.00018|-.00045|-.00063|-.00065|-.00093|-.00092|
|GOVEXP|-.00323**|.00501|-.00385**|.00520|-.00256**|-.00459|.00122***|.00063|-.00070|-.00064|
|PUBTOT|--|--|--|--|--|--|-.00180*|-.00145**|-.00149***|-.00141***|
|GDPPC|-8.6e-6|.00002|1.7e-6|.00003|-1.8e-6|-2.1e-6|-.00001**|-.00001|-.00003**|-.00003**|
|URBAN|.00137**|.00079|.00166**|.00096|.00134*|.00064|-.00246*|-.00167***|-.00160|-.00159|
|AIDS|-.04139|-.06211|-.04744|-.20362*|.00646|.04633|-.06289*|-.04001|-.07217|-.07025|
|GINI|-.14418|-.18676|.07096|-.02601|-.07474|-.20029|-.32844*|-.45695**|-.29885|-.30857|
|WAGG|-8.3e-7**|-1.2e-6|-6.4e-7***|-1.9e-6|-2.0e-7|-7.9e-7|7.8e-7*|7.2e-7|6.0e-7|6.0e-7|
|GOVG|-6.3e-8|-2.6e-6***|3.5e-7|-1.2e-6|3.0e-7|3.5e-7|-5.98e-7*|-4.9e-7|2.7e-8|1.4e-8|
|GINIG|.00003|.00012|-.00003|.00005|-.00002|.00003|.00005*|.00005***|.00006|.00006***|
|CONS|1.0515*|.89986*|1.0021*|.84756*|1.0464|1.1257*|1.1494*|1.1457*|1.1512*|1.1495*|
|# of Obs|79|36|72|34|72|66|101|101|101|101|
|(# ofCountrs)|(50)|(21)|(45)|(20)|(45)|(41)|(58)|(58)|(58)|(58)|
|Wald Chi2(6)|41.93|18.57|31.15|18.71|23.89|13.22|600.70|37.22|25.33|24.74|
|(Prob>Chi2)|(.00)|(.03)|(.00)|(.22)|(.00)|(.15)|(.00)|(.00)|(.00)|(.01)|

# Note:

1. * 0.01 significance level, ** 0.05 significance level, *** 0.10 significance level, and insignificant otherwise

2. EDU2-2: Inputs: orthogonalized public spending on education per capita, teachers per pupil Outputs: gross primary and secondary enrollments

- EDU2-2n: same inputs as EDU2-2, outputs: net primary and secondary enrollment EDU3-2: literacy of adult is added to EDU2-2 as input

- EDU3-2n: literacy of adult is added to EDU2-2n as input EDU3-3: literacy of youth is added to EDU3-2 as output EDU3-3bl: same inputs as in EDU3-2,

outputs: average years of school, first level complete, and second level complete (Barro-Lee education indicators) HEA2-2: Inputs: orthogonalized public spending on health per capita, literacy of adult

- Outputs: life expectancy at birth, and immunization DPT

HEA3-2: orthogonalized private spending on health per capita is added to HEA2-2 as input HEA3-2m: Immunization Measles is in place of DPT in HEA3-2 as output HEA3-3: Immunization Measles is added to HEA3-2 as output 36
(Figure 20). The cross-country differences are important and interesting because they help show the comparative value that these officials place on different kinds of education.

The gaps between envisioned and actual returns also differ from one country to another. In Indonesia, Vietnam and Mongolia, expected secondary premiums are not far from available published global data. In Lao PDR, secondary premiums are half as large as expected by officials, while they are underestimated significantly in the Philippines. At the tertiary level, education premiums are vastly overestimated by officials in all EAP countries. Actual returns at the tertiary level range from 20 percent in Lao PDR to just under 60 percent in the Philippines. Yet, expected returns are at least twice as large compared with the actual data for 2014 in all countries. This overvaluing of tertiary education may lead to disproportionate investments in tertiary education relative to primary education or other sectors.

_Figure 20: Estimated versus Actual Returns to Education by Sector._ <!-- Start of picture text --> 180%<br>160%<br>140%<br>120%<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Secondary vs. primary Tertiary vs. secondary<br>Expected Actual<br>Indonesia Lao PDR Mongolia Philippines Vietnam Indonesia Lao PDR Mongolia Philippines Vietnam<br><!-- End of picture text --> Source: CGD–World Bank 2022 survey for envisioned premiums and Patrinos and Montenegro (2014) for a compilation of the actual premiums.

# 6.6 Ministry of finance officials overestimate the returns to tertiary education.

Officials of ministries of finance overestimate university premiums and underestimate returns to TVET and secondary by a larger factor than officials of ministries of education. On average, among EAP countries, expected university premiums are 52 percent larger for surveyed finance officials than for education officials (Figure 21). Conversely, finance officials’ estimates of premiums are 32 percent lower for secondary (versus primary) than those of education officials. Unrealistic expectations for university returns may lead policy makers to overinvest in higher education and not invest enough in primary, TVET or secondary levels.

24