rafmacalaba/datause-encoder-data huggingface dataset:
Evans and Lahire (2016) demonstrate that schools’ baseline characteristics are balanced grade-year and restricting attention to WSDP and control group observations where 2 grades tions with 8 observations for each of 120 schools, including 61 schools in the WSDP treat(2016), who use student level data clustered by school-year for their cross-sectional analysis. We instead focus on a balanced panel of school level observations. As Table 7 shows, this the implementation of Mallows’ deconvolution algorithm, which we use in Subsection 7.2.2.

a similar small and statistically insignificant effect, using pooled OLS on the stacked crosssections. But does this null average effect mask treatment effect heterogeneity? Of course, because they think they will benefit participants, programs may have unintended negative consequences if, for example, they disrupt effective systems. If this is the case here, what proportion of schools benefitted from the WSDP? In this subsection, we answer these questions by studying features of the distribution of the WSDP’s effects beyond the mean. Unlike average effects and quantile treatment effects, features of the distribution of impacts beyond the mean are not identified by random variation induced by an RCT alone; additional assumptions are always required. The WSDP RCT is a good candidate for satisfying these assumptions because it includes four waves of data, and treated schools are observed both before and after the implementation of the WSDP.

tion 4.4.4. Conditional on school fixed effects, the variance of treatment effects is identified so long as the treatment effect is uncorrelated with all time-varying components of the error term, and these time varying components are not themselves too correlated over time. The full distribution of treatment effects is identified under the stronger assumption that treatment effects are independent of all time varying components of the error term. While these are not directly testable assumptions, they may be plausible. They would be violated, for ex-
8 change in this index can provide a useful "yardstick" for assessing the importance of the expansion of intra-trade.<sup>6</sup> Table 3 reports intensity ratios which were computed using United Nations Series D COMTRADE statistics for Mercosur countries' trade with each other, and with NAFTA members for selected periods from the late 1970s to 1994. The index clearly documents the increased "intensity" of trade between Mercosur members. For example, Argentina's intensity index for trade with Brazil took a value of 39 in 1994 which was more than five times its corresponding level in 1979-81 - Brazil's index for trade with Argentina more than doubles over the same period. With the exception of those involving Paraguay, where the quality of trade data is a problem, the bilateral intensity index ratios for Mercosur intra-trade were markedly higher in 1994 than in any previous period. These results strongly reinforce the impression provided by the previous comparisons (Tables 1 and 2) that show a major re-orientation of exports toward regional markets has occurred.<sup>7</sup> A key question is whether these changes are along lines that are consistent with efficiency and the true comparative advantage of member countries.

> 6The intensity of trade index **(Iij)** is defined for country i's exports to country j as the share of i's exports going to j (Xij/X,) relative to the share of j's imports (M1) in world imports (Mv). That is,

# (1)

**Ii.** = (XjjXj)/(MjNlM

Brown (1947) appears to have been the first among many to use this index five decades ago. Some analyses have netted out country i's imports from global imports in the denominator of equation (1).

7In contrast to the pattem for Mercosur, no similar results occur for trade with Canada and the United States. With one exception (Brazil's exports to the United States) the index for exports to both Canada and the United States are below unity. Although Mercosur members' trade with Mexico produced index values above unity there is considerable volatility in the numbers (for example, Argentina's index was 6.0 in 1979-81 and 1.5 in 1994) and lower values were generally recorded in 1994 than in the previous periods. The key message is that Mercosur members are typically becoming much more trade dependent with each other and less so with NAFTA countries.
effect and none differed significantly from the others. If there were differing norms, one might expect some messages to work while others failed.

A third explanation, based on our own surveys, involves the fact that individuals in Peru seem to believe that active recycling through a formal program is no better for the environment than the existing informal recyclers (who scavenge recyclable items from trash bags and dumps and may be even more effective at sorting). Thus, from an environmental perspective, the formal process requires more effort and does not change the ultimate outcome. This explanation has the benefit of explaining why similar messaging was successful in the United States, where such informal recycling exists but is far less prevalent, but was not successful in Peru.

# **<<A>>** PARTICIPATION INTENSITY STUDY

# **<<B>>** Experimental Design

In addition to the enrollment study, we conducted a participation intensity study among individuals who had previously enrolled in the recycling program, meaning that they had recycled through PRISMA at some point in the past. The purpose of this second phase was to assess whether failure to regularly remit recyclables might be attributable to either forgetfulness or the costs of participating, including time costs or the inconvenience of storing recyclables for a week. In the participation intensity study, we randomly assigned program households to receive a bin with an informative sticker about how to recycle, a bin without an informative sticker, or no bin. The bin experiment tests the hypothesis that the inconvenience or cost of recycling hinders participation because households report that bags are often too small and inconvenient for recycling. The 20
In 1987, some groups of public sector employees were allowed to be excluded from the increase in the normal contribution. This prompted the establishment of the 'B' contribution, which has remained frozen at the 1982 contribution level. The normal contribution, now called the 'A' contribution, was further adjusted in 1996. At that time some of the above public sector workers decided to accept contribution increases, giving rise to the creation of the 'C' contribution. A similar process unfolded in relation to a further increase of contributions in 2006, leading to the creation of a new 'D' contribution. A further increase will take effect from 2009 (Table 1). At present, ATP is striving to have as many employees as possible rejoin the original 'A' contribution. Similar initiatives have been effective in the past.

**<u>Table 1: Evolution of Individual Contributions</u>**

|DKK|1982|1988-90|1996|2006|2009|
|---|---|---|---|---|---|
|'A' Contribution|1,166|2,333|2,684|2,924|3,240|
|'B' Contribution||1,166|1,166|1,166|1,166|
|'C' Contribution|||1,517|1,757|2,076|
|'D' Contribution||||1408|1,724|
|Increase||1,167|351|240|316|

Source: ATP Lack of political agreement on the basic direction of public pension policy probably explains the failure to raise contributions. The contribution rate has remained low and was even allowed to reach insignificant levels in the late 1970s and early 1980s. The lack of political agreement may have been fueled by the preference of unionized workers, especially white-collar workers, to contribute to their own pension schemes, which may have offered better benefits. The exemption of some public sector workers from contribution increases in the 1980s and 1990s lends support to this view.

Various explanations could be put forward for the failure to raise contributions between 1964 and 1982 but evaluating their empirical importance would require access to detailed historical records and would be beyond the scope of this paper. For instance, the intentional redistribution in favor of older cohorts in the early years of operation of the fund created a wedge between investment returns and bonus rates. Although ATP was among the most successful institutional investors, its investment performance was just above inflation during this period (see below). The real value of pension benefits was eroded in line with the erosion in the real value of contributions.

# **5. Benefits**

ATP benefits take the form of single life annuities, unless the accumulated balance is very small in which case it is withdrawn as a lump sum. Pension benefits are modest, reflecting the low level of contributions. They amount to 40 percent of the social pension or 8 percent of average earnings. ATP pensions are received by nearly 80 percent of all present day pensioners. More than 98 percent of all new pensioners have pension rights with the ATP. The coverage of ATP is increasing due to the greater labor market participation of younger cohorts of older women and to the inclusion of recipients of social benefits.

6
again enumerating the number of clients, not accounts – from the European Association of Cooperative Banks (EACB). For a few countries which do not report numbers of accounts or clients in the Financial Access Survey, WOCCU, or EACB but which report value of accounts, we conduct a simple regression based prediction of numbers of accounts from per capita income and the value of accounts in mutually owned institutions relative to total adult population. Table 9 contains the regression table which has an R<sup>2</sup> of 0.8 with 22 observations. The parameter for log of income is -0.93 and the parameter for log of deposits per adult population equals 0.97, both significant at 1%. While it might seem somewhat surprising that the coefficient on income should be negative, this likely reflects the fact that, conditional on the amount of deposits relative to the total population, the higher income the country, the fewer individuals (and thus fewer accounts) these deposits represent. As we have a large number of small countries for which no data is available, we can also make assumptions about the distribution of the cooperative penetration variable for the missing values to improve our estimate. Specifically, in the preferred estimate, we assume that the countries with missing values (no reported values, no WOCCU or EACB data, and no predicted value from the regression) have an average cooperative deposit penetration which is close to the 25<sup>th</sup> percentile of the distribution of reported values. This would be a reasonable approximation, for instance, if many of the countries which did not report numbers had smaller cooperative sectors than the ones who did. As table 10 shows, the bulk of the accounts we count (92%) come from actual reported data including our survey data, data reported to WOCCU or EACB or from direct communication with regulators (CGAP numbers).

[ _High estimate_ ] The “high estimate” is the same as the preferred estimate with the exception that we assume the average of the missing values are closer the 50<sup>th</sup> percentile of the observed distribution.

[ _Low estimate_ ] Here we assume that the missing values are zero.

# **_4.1.3 Government savings banks_**

that compared to countries for which we have survey responses, WOCCU will overstate the number of accounts in regulated MOFIs.

18
-----. 2005b. "Mobility, Creativity, and Technology Development." Presented at

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Page, Scott E. 2007. _The Difference: How the Power of Diversity Creates Better Groups,_ _Firms, Schools, and Societies_ . Princeton, New Jersey: Princeton University Press. Phelps, Edmund S. February 2007. "Entrepreneurial Culture: Why European Economies Lag Behind the U.S." _The Wall Street Journal_ Opinion Journal.

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Wieser, Robert. 2005. "Research and Development Productivity and Spillovers: Empirical Evidence at the Firm Level." _Journal of Economic Surveys_ 19 593-596. Yusuf, Shahid, M. Anjum Altaf, Barry Eichengreen, Sudarshan Gooptu, Kaoru Nabeshima, Charles Kenny, Dwight H. Perkins, and Marc Shotten 2003.

_Innovative East Asia: The Future of Growth_ . New York: Oxford University Press.

17
3.6. Extension 2: Cumulative Multipliers with Flexible Dynamics. In this section, I estimate cumulative multipliers _Ch_ over the rst few quarters following each transfer shock, which allow for dynamics beyond the rst quarter (Figure 3 in black circles, with 95 percent condence interval (CI) bars). I use the benchmark specication with state and time FEs, and so _C_ 0 ( _h_ = 0) represents the impact multipliers in the second row of Table 1.<sup>33</sup> The speci- cations estimated are presented in Section 2.3.1. As permanent transfers are likely to have longer-lasting eects than one-o transfers, I estimate over a longer horizon (as do others in the literature). For permanent transfers, I follow Romer and Romer (2016) and estimate over a horizon of a year ( _h_ +1 = 4 quarters), and for one-o transfers I follow Parker et al. (2013) and estimate over a horizon of half a year ( _h_ + 1 = 2 quarters).<sup>34</sup> For permanent transfers (Figure 3, Panel A), the cumulative multipliers are roughly constant at about 1.5, and are similar to impact multipliers presented in Table 1, Panel A (within the 95 percent condence interval). While the multiplier rises to _C_ 3 = 1 _._ 7 after a year, this is less than 1/2 of a standard error from 1.5. For temporary transfer shocks and labor income (Panel B(ii)), _C_ 1 = 0 _._ 26 is very similar to the impact multiplier estimate of 0.31 (and insignicantly dierent). For temporary transfers and GDP (Panel B(iii)), the point estimate _C_ 1 = 0 _._ 8 is larger than the impact multiplier estimate of 0.4, but they are insignicantly dierent. In part this is because _C_ 1 is imprecisely estimated: the standard errors triple in width, even leading to a loss of statistical signicance (from zero).<sup>35</sup> nds that households saved large Alaska permanent fund dividends (PFD), but tended to spend small tax refunds. But Kueng (2018) nds that the PFD are spent, with an MPC of 0.25, mostly by high-income households.

> 33For _h >_ 0 , I drop the top and bottom 1% observations of<sup>�</sup><sup>_h_</sup> _j_ =0<sup>(</sup><sup>_Yi,t_+</sup><sup>_j −Yi,t−_1)</sup><sup>_/Yi,t−_1for</sup> permanent transfers using the projection method. Due to mean reversion these outliers are not well captured dropping extreme quarterly growth rates.

> 34Longer horizons introduce other confounding variables, like the 2009 scal stimulus.

> 35Of the 0.4 increase in the GDP multiplier from _C_ 0 to _C_ 1 in Figure 3, about 2/3 is due to a larger impact multiplier in a specication that includes a lagged transfer variable. However, the larger estimates of _C_ 1 are not robust, they only occur in the benchmark specication and not the parsimonious specication (see Appendix Table 7, Panel C). Additionally, lagged transfers have little eect on the impact multiplier in the parsimonious specication (Table 1 row C3 and Appendix Table 7, Panel C).

28
employment outcomes among these young women. Self-efficacy correlates with experience (age, previous trainings) and an environment where girls enjoy family and social support, feel connected and have educated parents and other successful role models to look up to.

Likewise, the negative effects of teachers’ stereotypes on girls’ math performance and education paths in Italy cited earlier are at least partially driven by lower self-confidence in math ability (Carlana 2019). This is also the case in France, where the lower self-esteem of poorer students about their current academic ability compared to their richer classmates explains half of the gap in educational aspirations among equally-achieving classmates, compared with the quarter explained by their awareness about options (Guyon and Huillery 2021).

# **6. Conclusion**

Building on influential theoretical foundations, the last decade has seen a flurry of empirical studies in economics concerned with the influence of aspirations —people’s goals for their future— on individual behaviors and development outcomes. While it is still growing, the evidence confirms that aspirations are important correlates of economic outcomes and that researchers and policymakers should account for them when designing policies. Accounting for aspirations can enhance our understanding of complex human behaviors and micro and macro phenomena.

Empirical studies confirm what the foundational theory laid out: aspirations can be powerful motivators and higher aspirations can lead to better outcomes. Experimental studies finding a simultaneous increase in aspirations and outcomes (educational, labor market, and others) identify aspirations as a mechanism behind improved outcomes, while longitudinal studies show that aspirations correlate with better subsequent outcomes.

However, higher aspirations do not automatically translate into better outcomes; rather when they are not matched with opportunities they can result in despair and unleash dangerous frustrations that ultimately lead to worse outcomes at the individual and aggregate level.

Moreover, the social and circumstantial factors shaping them can reinforce persisting inequality. A range of experimental studies has shown that interventions led to higher aspirations and motivation towards better outcomes. However, the social environment often discourages the development of high aspirations for the poorest and underprivileged. Individuals disconnected from multiple networks and opportunities or pressured by constraining social norms and stereotypes might develop low aspirations and miss potential beneficial opportunities. Thus, for example, immigrant and poor students have on average lower educational aspirations than their wealthier peers of similar level of abilities.

25
tertiary education is relatively high. In 2023, 18.6 percent of people aged 25 to 34 had a tertiary education, compared to 11.5 percent of people aged 35 to 54, 9.1 percent of people aged 55 to 64, and 5.4 percent of people aged 65 and older (see Figure 5). It should be noted that there is a relatively low share of people aged 15 to 24 with a tertiary education because they are likely still in the process of obtaining formal education.

**Figure 4: Share of population by level of education, 2016-2023 (%)**

<!-- Start of picture text --> 100<br>9.4 9.5 10.1 10.0 10.2 10.5 10.2 10.7<br>80<br>60 49.3 49.7 50.4 50.9 52.2 52.7 52.1 53.5<br>40<br>20 41.3 40.8 39.6 39.1 37.6 36.9 37.8 35.9<br>0<br>2016 2017 2018 2019 2020 2021 2022 2023<br>Primary Secondary Tertiary<br>Share of population (%)<br><!-- End of picture text --> Source: Author’s calculations using Sakernas data

**Figure 6: Share of employment by sector, 2016-2023 (%)**

<!-- Start of picture text --> 100<br>80<br>46.7 48.1 47.9 49.1 48.7 49.3 49.2 49.6<br>60<br>40 21.4 22.3 23.1 23.3 21.6 22.3 22.2 22.2<br>20<br>31.9 29.7 29.1 27.7 29.8 28.3 28.6 28.2<br>0<br>2016 2017 2018 2019 2020 2021 2022 2023<br>Agriculture Industry Services<br>Share of employment (%)<br><!-- End of picture text --> Source: Author’s calculations using Sakernas data

**Figure 5: Share of population by age group and level of education, 2023 (%)**

<!-- Start of picture text --> 100<br>4.5 11.5 9.1 5.4<br>18.6<br>17.5<br>80<br>28.2<br>49.8<br>60<br>82.7<br>61.2<br>40 77.1<br>62.7<br>20 38.7<br>20.2<br>12.9<br>0<br>15-24 25-34 35-54 55-64 65+<br>Primary Secondary Tertiary<br>Source: Author’s calculations using Sakernas data<br>Figure 7: Share of employment by sector and<br>age group, 2023 (%)<br>100<br>31.0<br>80 41.3<br>49.8<br>55.4 55.9<br>60 12.1<br>16.6<br>40 23.5<br>24.0 25.0<br>56.9<br>20 42.1<br>26.8<br>20.6 19.1<br>0<br>15-24 25-34 35-54 55-64 65+<br>Agriculture Industry Services<br>Share of population (%)<br>Share of employment (%)<br><!-- End of picture text -->

**Figure 7: Share of employment by sector and age group, 2023 (%)**

Source: Author’s calculations using Sakernas data

# **Employment in the services sector has gradually increased over time, and the sector currently**

**employs almost half of all workers.** Between 2016 and 2023, employment in the services sector increased from 46.7 to 49.6 percent, as shown in Figure 6. In the same period, the share of employment in the agriculture sector decreased from 31.9 to 28.2 percent. The share of employment in the industrial sector was somewhat stagnant in the period. Figure 7 shows that there is an age dimension to employment by sector. Older people are more likely to be employed in the agriculture sector, while younger people are more likely to be employed in the services sector. The increasing

5
<!-- Start of picture text --> Rural manufacturing employment in India<br>a<br>QnSi ico}<br>f=c<br>=SN<br>=<br>S<br>tia<br>&<br>1990 1995 ee 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Rural manufacturing employment in India<br>lO<br>Xs<br>Bee TTT TTS<br>oh e--------e--7 ooS<br>s ~AY<br>Ew<br>ae<br>=<br>S<br>Se<br>uw<br>wo<br>°<br>1990 1995 2000 2005 2010<br>car<br>——*— Formal sector —-—#-—- Informal sector<br><!-- End of picture text --> <!-- Start of picture text --> Rural manufacturing employment in India<br>g<br>Zw<br>2s—f=<br>r= S<br>[=<br>2ey — a To|<br>i? ire) Je ——- oe _-e--77<br>oe ee<br>1990 1995 2000 2005 2010<br>Year<br>“@--~ Formal tradables — ®— - Formal non-tradables<br>—-—#-—- Informal tradables ——*—— Informal non-tradables<br><!-- End of picture text -->

<!-- Start of picture text --> Rural manufacturing firms in India<br>[<2]<br>7)<br>5 Oo<br>E —<br>c eo<br>ot See<br>e cee<br>= weet<br>1990 1995 2000 2005 2010<br>Year<br>“-@-- Formal tradables — ®— - Formal non-tradables<br>—-—#—- Informal tradables ——*—— Informal non-tradables<br><!-- End of picture text -->