rafmacalaba/datause-encoder-data huggingface dataset:
against pre-NREGS consumption, with and without our adjustments, for various values of α. We see from Figure 7 that the net gains from the scheme are highest for the poorest using unadjusted consumption, but the impacts are attenuated when we adjust for the disutility of the work provided by the scheme. This effect is not found for the sub-sample of un-rationed households, for whom the difference is attenuated by the lower participation rate, as noted above.

Table 6 gives the impacts of NREGS earnings on poverty for both the population as a whole and for the sub-sample of NREGS participants for the median α. We first give results using a common poverty line, set at the median of the post-NREGS distribution of (un-adjusted) consumption. (Qualitatively similar results were obtained using consumption at the 40<sup>th</sup> percentile.) We see that the poverty measures (both post-NREGS and pre-NREGS) are generally higher with our adjustment for the disutility of casual manual labor; the only exception is for the headcount index in the population as a whole. The impact estimates (post-NREGS less preNREGS) are found to be lower for all three poverty measures after adjusting for the disutility of labor.<sup>19</sup> As implied by Figure 7, poverty impacts are little affected by our adjustment for the disutility of work using the sample of un-rationed households.

These results use the median of the post-NREGS un-adjusted consumption distribution as the poverty line. One can question whether the same poverty line should be used with or without our adjustment for the disutility of work. In effect, this amounts to using the reference casual labor supply in setting the poverty line for the adjusted distribution of consumption. It will be recalled that the reference value was chosen to balance the means of the two distributions. However, this need not accord with the labor supply to casual manual work of people living at or near the median of the post-NREGS un-adjusted consumption distribution. Table 6 also provides the analogous estimates obtained when we use average labor supply of people in a neighborhood of the post-NREGS consumption median. The main results are robust to this change.

# **6. Conclusions**

Workfare schemes typically offer unpleasant work. In the case of India’s National Rural Employment Guarantee Scheme the work provided is manual labor, toiling for long hours in the

> 19 Notice also that the impact on the income-gap ratio—namely the mean distance of the poor below the line, as a percentage of the line (i.e., the ratio of the PG index to the headcount index)—is also lower with our adjustment.

14
**Figure 19: Traffic between the Small Island States and the Americas**

<!-- Start of picture text --> a) North America and Puerto Rico b) Central and South America<br>1,400 45<br>40<br>1,200<br>35<br>1,000 30<br>800 25<br>20<br>600<br>15<br>400<br>10<br>200 5<br>0<br>0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>thousands thousands<br>Estimated Seats, Estimated Seats,<br><!-- End of picture text --> _Source: Authors’ calculations based on data from Diio (2014)._ San Juan acts as a gateway to and from North America, being American Airline’s primary hub in the region. The hub and spoke system out of San Juan has shown a steady decline and has collapsed with the pulling out of America Eagle by February 2013. This hub will be virtually shut down with American Airline’s bankruptcy and related consolidation of operations. American is transferring operations to Miami and other airports. The effect of the decline of San Juan is noticeable in the traffic trends between the continental U.S. and small island states, which has gone up significantly. In other words, the increase in traffic with continental US has been driven mainly by a transfer of operations rather than a pure overall expansion of the market (Figure 20a and Figure 20b).

**Figure 20: The San Juan Effect on Small Island States’ Air Traffic Patterns**

<!-- Start of picture text --> a) Direct flights from and to Puerto Rico b) Direct flights from and to the U.S.<br> 700 1,000<br>900<br> 600<br>800<br> 500 700<br>600<br> 400<br>500<br> 300 400<br>300<br> 200<br>200<br> 100 100<br>0<br> - 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>thousadns thousands<br>Estimated Seats, Estimated Seats,<br><!-- End of picture text --> _Source: Authors’ calculations based on data from Diio (2014). Note: 2012 data not complete._ Though there currently is much discomfort in the region with the final shutting down of the San Juan hub, the decline of the hub began long before American Airline’s bankruptcy (Figure 20b). American Eagle (flying the regional traffic for American Airlines) has now liquidated its ATR 72s, the turboprop aircraft instrumental in serving the shorter runways of the region. This means that airports such as Melville Hall in Dominica and ET Joshua in St. Vincent have lost an important connection. The shutdown occurred February 2013.

49
**Figure 3a: The Evolution of Public Spending on Education, 1960–1999**

<!-- Start of picture text --> 1960 1970 1980 1990 2000<br>year<br>21<br>20.5<br>20<br>19.5<br>Mean log pub educ expend, abs val PPP<br>19<br><!-- End of picture text --> Figure 3b indicates an increase in public spending on education in per capita terms, and figure 3c illustrates the evolution of public spending on education per pupil.

**Figure 3b: The Evolution of Per Capita Public Expenditure on Education, 1970–1999**

<!-- Start of picture text --> 1970 1980 1990 2000<br>year<br>Education, 1970–1999<br>1970 1980 1990 2000<br>year<br>4.9<br>4.8<br>4.7<br>4.6<br>Log Per Capita Cost of Schooling 4.5<br>4.4<br>6.8<br>6.7<br>6.6<br>6.5<br>Log Per Pupil Cost of Schooling 6.4<br>6.3<br><!-- End of picture text -->

**Figure 3c: The Evolution of Per Pupil (Primary and Secondary) Public Expenditure on Education, 1970–1999**

6
# **Contents**

# **Page**

|Introduction......................................................................................................................... 3|
|---|
|1<br>Background................................................................................................................. 4|
|1.1 The Danish Pension System ....................................................................................... 4|
|1.2 The Danish Retiree ..................................................................................................... 5|
|1.3 The Danish Pension Institutions ................................................................................. 6|
|2<br>Income Statements and Balance Sheet Composition.................................................. 7|
|3<br>Regulatory framework .............................................................................................. 11|
|4<br>The perfect storm...................................................................................................... 12|
|5<br>The Growing Use of Derivatives.............................................................................. 13|
|6<br>The Financial Impact of Derivatives ........................................................................ 16|
|7<br>Case Studies.............................................................................................................. 23|
|7.1 PFA: a coerced strategy............................................................................................ 23|
|7.2 ATP: a proactive strategy ......................................................................................... 24|
|7.3 Magistrenes Pensionskasse (MP): a passive strategy ............................................... 26|
|7.4 Lessons from the case studies................................................................................... 26|
|8<br>Conclusions............................................................................................................... 27|
|Bibliography ..................................................................................................................... 29|

2
7 <mark>those provinces which are closer to Tanzania generally had higher levels of international displacement than other provinces.</mark> Figure 2 – Main locations of Burundian refugees (% in each country) <!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>DRC Rwanda Tanzania<br>Note: Source (UNHCR 2021b)<br>%<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> After the signing of the peace agreement, Tanzania closed the camps for the 1993 conflict refugees, ended official protection for this group and pushed them to return home. By 2009, the large majority of refugees from the 1993 conflict had returned to Burundi, along with about 50,000 of the 1972 refugees. This period of return lasted for a few years until late 2015 when there was a new spike of displacement following an announcement from the President of Burundi that he was running for a third term in office, what some considered a violation of the peace agreements. As we explain in the next section this occurred after the end of data collection for this paper, therefore in our analysis we focus on the return period. Please see Section 6.2 for a discussion of this new episode of displacement in Burundi and the applicability of the findings of this paper to such episode.

Ruiz and Vargas-Silva (2021) suggest that by 2011 the economic conditions of stayees in areas of Burundi with below and above the median number of returnees were not very different.<sup>2</sup> There was no statistically significant difference in terms of livestock, subjective economic indicators, land access, and food insecurity. However, some important gaps started to appear by 2015.

The recovery of the “promised” agricultural land was particularly problematic. Many returnees found their land occupied by others or sold by family members who had returned to Burundi earlier (Hovil 2009; van Leeuwen 2010). In other cases, land abandoned by refugees was allocated to other households by the government (Fransen and Kuschminder 2012). The main challenge with the peace agreement conditions was that in practice there was insufficient land available to give returnees a landholding equivalent to their pre-exile land (van Leeuwen 2010). Many of the solutions to land disputes involved land-sharing 2 In particular, see Table 2 of that paper.
The snowball survey shows even more differences. Females are 13 percentage points more likely to be return migrants in the snowball survey, compared to no difference from males in the stratified survey. The snowball survey gives strong education selectivity, significant at the 1 percent level – individuals with postgraduate education are less likely to be return migrants, and with undergraduate more likely to be return migrants than individuals with high school or less. Sansei (third generation), are surprisingly less likely to be return migrants than the small number of fourth generation in the sample. The snowball survey method therefore gives very different results than the stratified survey or intercept point survey to basic questions of interest such as “are return migrants more likely to be men or women?”, or “are return migrants more or less educated than nonmigrants”.

# **6. Comparison of the Costs of the Different Methods**

Upon conclusion of the survey efforts, we asked _Sensus_ to provide us with their updated cost breakdown for the cost of carrying out the survey using each survey method.<sup>12</sup> The stratified survey and snowball survey has a 36-page questionnaire with just over 1000 variables, taking just over one hour to complete, compared to the 3 page intercept questionnaire with 60-70 variables, taking an average of 7 minutes to complete. The per household costs here include interviewer time and travel costs, but not an additional 14 percent for taxes and the administration fee for the survey firm.

_Sensus_ estimated that the listing exercise cost US$2 per dwelling listed, and the follow-up household interviews of households identified as Nikkei in the listing cost US$80 each. Combining the listing and surveying, the total variable cost per household interviewed in the stratified survey was US$212.<sup>13</sup> The snowball survey was estimated to cost US$100 each. Since households in the snowball survey are less geographically clustered than those identified through the listing exercise, the cost of administering the questionnaire was higher than the stratified survey (although the listing phase was not required). The

> 12 _Sensus_ was awarded the contract for this project through competitive bidding based on the quality of the proposal. Since this was a new, experimental survey, with some adjustments made along the way, the expost costs per survey were slightly more than Sensus had initially anticipated.

> 13 Recall that many households had to be listed to identify one Nikkei household to survey.

35

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26 this would not be something we could easily decompose into a contribution due to the schooling shock and a contribution due to the employment shock. The reason is that income is based on an exponential function of human capital, which is accumulated linearly: thus, the shocks are multiplicative. As a result, we instead compare deviations in the difference between log income from the steady state value.

Results are reported in Figure 9. The relative log-income of women does not deviate much over the entire period of the shock (see the patterns for _Overall_ ), with the exception of the direct impact of the shock itself, which is more severe for women. In the HICs and the MICs, relative income declines by 2 _._ 5 percent, slowly returning to zero after 10 years, whereas in the LICs it is a decrease of 1 _._ 7 percent. There turns out to be no significant long-run residual impact after the 10 years of the shock itself are over.

However, if we only apply the _schooling shock_ we see that there is almost no change in relative income. This indicates that almost all of the entire differential impact of the pandemic shock is due to the employment/experience shock. In addition, even though there are differences in the returns to schooling across gender, as well as differences in the quantity of schooling, these differences appear to be too small to have any significant impact on the losses from COVID-19. Indeed, the returns to education for women are within 1.7 percentage points of the returns for men (Table 3), so that a given period of disrupted schooling is similar as a share of the human capital stocks by gender.

We repeat the experiments assuming the same returns for men and women finding an almost identical figure to Figure 9 (not shown). This indicates that the differential impact on women is simply due to the _direct_ impact of the employment shock on women. Differences in returns to experience, while significant, are then too small to yield a difference in the impact of the shock. Indeed, the returns to experience for women are within 1.5 percentage points of the returns for men (Table 3).

Finally, we repeat the experiment assuming the same schooling distribution for women as for men. The results for the HIC and the MIC remain essentially identical. Interestingly, the income gap between men and women in the LIC peaks at 1 _._ 4 instead of 1 _._ 7 percent (not shown). This is not a large difference, but it does indicate that women’s relative lack of schooling in LICs is a factor that leaves them vulnerable to shocks. This is possibly due to women having less college than men, so that having relatively worse affected low-skilled jobs leaves them more exposed to the employment
# **4 PTAs and the gravity equation**

The previous section has argued that the incidence of low and zero MFN duty lines in the PTA members’ tariff schedules may effectively limit the direct impact of PTAs on trade to a (potentially small) subset of trade flows in which the PTA preferences actually matter. While it is possible that PTA membership and more liberal agreement provisions affect flows in high and low MFN duty lines alike due to various kinds of spillover effects, determination of whether that is the case should be a result of an empirical investigation rather than an _a priori_ assumption. In this section, we will build a gravity model of bilateral trade and apply it to both total and “preferential” trade between countries. We will use trade in all tariff lines and trade in tariff lines with a MFN duty above 3 percent as two alternative dependent variables, and, using the same set of independent variables, compare coefficient estimates across the two specifications. The expectation is that while coefficients on the standard control variables such as distance and similarity in trading partners’ GDP are likely to be the same across the two models, the coefficients on the PTA membership variable will differ significantly (and the coefficient value will be higher when only “preferential” trade is used as the dependent variable). Furthermore, by using the PTA database of section 3.1, our model will have the added advantage of estimating the average global effect of PTAs on trade directly, without having to scale up individual agreement estimates.

We will also use the “preferential” gravity model to assess the potential impact of product exlcusions and long liberalization phase-in schedules on trade flows between PTA members. In order to do this, we will add explicit controls for both provisions to our gravity model and simulate the effect of removing exclusions and phase-in periods using the estimated coefficients. Finally, we will investigate alternative specifications of the PTA variable, such as including separate dummies for agreements that have and have not been notified to the WTO, distinguishing between PTAs with two or more members, adding explicit controls for North-North, North-South, and South-South PTAs, and testing whether income similarity has a significant effect on preferential trade.

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**Table 1**

MEANS AND STANDARD DEVIATIONS OF VARIABLES BY HOUSEHOLDS IN PERU: 1985-86

|**Variables**|**Mean**|**Standard**<br>Deviations|
|---|---|---|
|_Department-Level_<br>_RMS Variables:_|||
|Share of Industry in Dept.GDP<br>|0.36|0.14|
|Share of Agriculture in Dept.GDP|0.18|0.13|
|Share of Services etc.in Dept.GDP|0.39|0.13|
|Share of Govt. Services in Dept.GDP<br>|0.07|0.03|
|Degree of Urbanization|66.99|25.49|
|_Household-Level RHS Variables:_|||
|Predicted Household Income|787.26|385.13|
|Place of Residence (Dummy: 1-Rural, 0-Urban)<br>|0.43|0.49|
|Household Size|6.48|2.75|
|Total Landholding (Acres)|9.21|76.01|
|_Individual-Level_<br>_RHS Variables:_|||
|Father's Education Level<br>|1.48|1.76|
|Mother's Education Level|0.48|1.68|
|Father's Occupation|4.28|1.67|
|Mother's Occupation|2.98|1.63|
|_Individual-Level_<br>_LHS Variables:_|||
|Female: Education in Years|4.59|4.10|
|Male<br>: Education in Years<br>|5.49|4.13|
|Female: Highest Level of Education Completed|1.94|1.69|
|Male<br>: Highest Level of Education Completed|2.43|1.50|
|Female: Age in Years<br> <br>|26.70|16.04|
|Male<br>: Age in Years|26.12|16.13|
|Female Schooling Shortfall: 6-15 years|2.76|1.92|
|Male Schooling Shortfall: 6-15 years<br>|2.57|1.73|
|Female Schooling Shortfall: 16-25 years|7.89|4.35|
|Male Schooling Shortfall: 16-25 years|7.34|3.75|
|Female Schooling Shortfall: 26-35 years|18.76|5.84|
|Male Schooling Shortfall: 26-35 years|16.89|5.28|
|Female Schooling Shortfall: 36-45 years|31.01|5.67|
|Male Schooling Shortfall: 36-45 years<br>|29.02|5.76|
|Female Schooling Shortfall: 46-65 years|46.33|7.03|
|Male Schooling Shortfall: 46-65 years|44.54|7.13|

28
# **1. Introduction**

The United Nations’ Sustainable Development Goal 4 proposes that by 2030 “all girls and boys [should] complete free, equitable, and quality primary and secondary education” (United Nations 2015). This ambitious goal sets a triple objective for educational policies: improving free access to, equity in, and quality of learning. It is unlikely that one single intervention is enough to make headway on these objectives simultaneously. For example, policies that are successful in increasing access to school may not necessarily foster quality education, and interventions that are effective among girls, may not be equally effective for boys. However, while education programs implemented by governments or NGOs often involve multiple inputs and programmatic objectives, we know very little about their effectiveness. The vast majority of education evaluations study programs that focus on only one objective or educational input – for example, only 5 percent of the studies in McEwan’s 2015 meta-analysis of primary school evaluations measure the impact of a “combination” treatment.

In contrast, this paper presents the results from an evaluation of a multifaceted educational program designed and implemented by an India-based nongovernmental organization (NGO) in rural Rajasthan, India. The program had three primary objectives: to enroll marginalized girls, improve student learning, and support school management. The program involved multiple interventions: door-to-door campaigns to enroll drop-out or never-enrolled girls, volunteers trained to teach activity-based and playful learning among students grouped by ability, strengthening school management committees, and working with members of the community to promote girls’ education. The multiple inputs were designed to complement and support the objectives of the program. By grouping students according to ability, the volunteer-led teaching activities could help teachers cater to students of varying abilities and mitigate potential harmful effects from the enrollment drive. Similarly, the program’s community engagement and sensitization to the 2