rafmacalaba/datause-encoder-data huggingface dataset:
- Distrust of Institutions

- Personal economic impact

- Other

- Irrelevant

These categories were decided upon based on the _La Prensa Grafica_ survey and some preliminary exploratory analysis<sup>3</sup> . The second task is to classify the sentiment regarding the reform expressed in the tweets according to the following:

- Strongly positive

- Positive

- Neutral

- Negative

- Strongly negative

In order to label this content, the tweets were uploaded to Amazon Mechanical Turk<sup>4</sup> , a crowdsourcing platform whereby tasks may be completed by distributed teams through an online marketplace.

Mechanical Turk offers several standard template tasks including labeling of images, assigning sentiment to a piece of text, etc. However, these templates are not very flexible. In our case we wanted to have all the instructions in Spanish not to exclude non-English speakers. Also it was necessary to allow a tweet to fall into more than one category, this was not allowed in the standard template.

Therefore, we decided to create a custom task. In this case the user designs the task with questions, tick/check boxes and instructions either with a user interface or directly adding in the HTML code. The disadvantage of this is that the custom task has an extra overhead. In order to create a task with the text of all the tweets in an automated way, a simple script was created to paste the header and footer HTML code as text together with the tweet text.

Creation of the task required a title for the Human Intelligence Task (HIT), a set of instructions and a means to record the results. The instructions are available in Appendix A. A time estimate must be provided (it is recommend to be generous with this time

> 3This preliminary analysis included the use of topic models fit via Latent Dirichlet Allocation (LDA) Blei et al. [2003]. LDA is a clustering algorithm of sorts that helps discover the different “topics” contained a collection of documents. This approach was also used during the taxonomy refinement stage to discover new keywords. More details are available upon request.

> 4 `https://www.mturk.com/`

8
- 24 -

schools indicate that we can proceed to the second step in the HLM analysis (the Chi-square chart indicates that all estimated parameter variances are significantly different from zero; see Annex A).

Table 5: Parameter estimates from six HLM unconditional models, Philippines secondary, 1983 (t-statistics in parentheses)

|Independent variable|Science<br>test<br>score|Mathematics<br>test<br>score|Positive<br>attitude<br>toward<br>science|Negative<br>attitude<br>toward<br>science|Positive<br>attitude<br>toward<br>school|Negative<br>attitude<br>toward<br>school|
|---|---|---|---|---|---|---|
|Individual level effect|||||||
|Male (fixed)|0.79|0.34***|-0.10|0.23***|-0.18|0.24***|
||(10.95)|(5.89)|(5.26)|(11.96)|(8.53)|(11.65)|
|Age (fixed)|-0.01***|-0.01***|-0.00|0.00***|-0.00|0.00|
|SES (random)|(4.51)<br>0.54***|(5.43)<br>0.26***|(0.33)<br>0.12***|(3.68)<br>-0.06***|(0.26)<br>0.07***|(1.69)<br>-0.00|
||(10.63)|(6.78)|(9.67)|(4.96)|(5.52)|(0.10)|
|School<br>level<br>effects|||||||
|Mean|12.82***|11.78***|0.06|-0,47***|0.09|0.29**|
||(29.19)|(33.23)|(0.52)|(4.46)|(0.83)|(2.65)|
|Percent between-school<br>variance<br>axplained|9.75|8.02|20.0|12.5|10.0|0.0|

*** p c .001; ** p C .01

48. School Type Effects Model. In the second step, we address the major purpose of this study: to explore differences in the achievement and attitudes of students attending different types of secondary schools. Comparisons were made, therefore, between the achievement and attitudes of those attending national public, local public and private schools. Two variables, "Local public schools" and "Private Schools", were added to each of the two between-school equations for both achievement and attitudes; national public schools are the omitted category. Table 6 summarizes the results of these analyses.
each case, after the matching the covariates are balanced, and this is the expected result indeed (D'Agostino, 1998; Grier and Grier, 2021; Rosenbaum and Rubin, 1983).

|g, ; , ; u u, .<br>**Table 11.**t-test on the mean of each independent variable:PrPp_dim<br>d_1<sup>+</sup><br>as the dependent variable<sup>++</sup><br> <br>|
|---|
|Lagged control variable<br>Outcome<br>OECD<br>|
|Laggedrf_rp<br>Unmatched<br>−3.15<sup>∗∗∗</sup><br>Matched<br>−1.06<br>|
|Laggedrf_iu<br>Unmatched<br>−1.64<br>Matched<br>1.14<br>|
|Laggedrf_hdi<br>Unmatched<br>1.19<br>Matched<br>−0.18<br> <br><br>|
|t-statistics reported;*:p< 0.10,**:p< 0.05,***:p< 0.01.<br>**+ **PrPp_dim<br>d_1 = 1ifP2 > 0.5and0otherwise.<br>|
|<br><br> <br>++Recall that for MENA, we take as a treatmentP2 > 0.25as the maximum ofP2is less than0.5and<br>using this treatment for OECD as well does not change the conclusion. The corresponding balancing tests<br>for both MENA and OECD are presented in Table 12.<br><br>|
|**Table 12.**t-test on the mean of each independent variable:PrPp_dim<br>d_2<sup>+</sup>as the<br>dependent variable<sup>++</sup><br>|
|Lagged control variable<br>Outcome<br>MENA<br>OECD<br>|
|Laggedrf_rp<br>Unmatched<br>−8.36<sup>∗∗∗</sup><br>−2.20<sup>∗∗</sup><br>Matched<br>−0.56<br>0.99<br>|
|Laggedrf_iu<br>Unmatched<br>−8.70<sup>∗∗∗</sup><br>−1.08<br>Matched<br>−0.68<br>0.15<br>|
|Laggedrf_hdi<br>Unmatched<br>9.34<sup>∗∗∗</sup><br>21.09<sup>∗∗∗</sup><br><br>|
|Matched<br>0.47<br>0.78<br><br> <br>|
|**+ **PrPp_dim<br>d_2 = 1ifP2 > 0.25and0otherwise; t-statistics reported;<sup>*</sup>:p< 0.10,<sup>**</sup>:p< 0.05,<br>***:p< 0.01.<br><br>|
|<br>**Table 13.**t-test on the mean of each independent variable:PrPfpm_dim<br>d<sup>+</sup>as the<br>dependent variable<sup>++</sup><br>|
|Lagged control variable<br>Outcome<br>MENA<br>OECD<br>|
|Laggedrf_rp<br>Unmatched<br>−9.23<sup>∗∗∗</sup><br>−0.19<br>|
|<br>Matched<br>−0.37<br>−1.34<br>|
|Laggedrf_iu<br>Unmatched<br>−9.41<sup>∗∗∗</sup><br>−2.19<sup>∗∗</sup><br>|
|<br>Matched<br>−0.33<br>1.34<br>|
|Laggedrf_hdi<br>Unmatched<br>6.95<sup>∗∗∗</sup><br>8.09<sup>∗∗∗</sup><br><br>|
|Matched<br>1.50<br>1.14<br><br> <br>|
|**+ **PrPfpm_dim<br>d= 1ifP1 > 0.5and0otherwise; t-statistics reported;<sup>*</sup>:p< 0.10,<sup>**</sup>:p< 0.05,<br>***:p< 0.01.|

# **5. The role of digitalization**

How does digitalization affect Participation in MENA? Does it strengthen it? Do the effects of Provision and Protection on Participation in the region change when we control for 30
where ��� is daily water flow or sediment load for hydro station _q_ on day _d_ of the year; as in equation (1) _Tmy-1_ is the treatment indicator at the micro-watershed level; ����� is daily rainfall for day _d_ ; �� and �� are hydro station and year fixed effects; and ��� is the time-varying error component.

A key advantage of equation (2) is that amounts of sediment in downstream water flows directly measure soil erosion, the reduction of which is one of the main objectives of SLM projects. In interpreting the regression results, however, this must be weighed against the limitations imposed by the fact that data are available for only a few stations in intervention areas. Identification thus relies on variation in treatment intensity and results are most appropriately interpreted as indicating correlation rather than causality. Estimates of equation (2) suggest (table 7) that SLM activities on cultivated land, degraded land, and areas planted with new trees are statistically significantly and negatively correlated with daily water flow and sediment load.

Interestingly, in contrast to the earlier results, point estimates are rather large and statistically significantly different from zero. The estimated treatment elasticities of daily water flows at mean values of treatment and outcome indicators are -82%, -44% and -16% for SWC on cultivated land, SWC on degraded land and planting trees, respectively. Treatment indicators are also significantly negatively associated with daily sediment load with estimated elasticities of -158%, -33% and -14% for cultivated land, degraded land and planting trees, respectively. Without reading too much into these figures, they increase confidence that the quantitatively modest, though statistically significant, estimates obtained earlier for LSWI, SAVI, and NDVI are indicative of economically meaningful outcomes.

# **5. Conclusion and Policy Implications**

This study demonstrates that freely available imagery, when combined with information on the location, type, and timing of SWC efforts, offers a new way to assess the impact of these interventions at a scale and resolution superior to what was available in the past, at a fraction of the cost. Applying this methodology to the TBIWRD points towards significant impacts, the magnitude of which was most pronounced for soil water content and which are robust to the choice of differently sized treatment groups. Ground measurement of water flow and sediment content at certain hydrological stations provides independent support to this finding, suggesting that project activities were associated with large reductions in sediment load. Valuing these in economic terms would be an important area for future research that would allow strengthening the economic rationale for such efforts by comparing benefits to the cost of specific interventions.

The fact that we obtain highly significant results, despite limitations due to the high level of aggregation of treatment variables by space, time, and activity type, highlights the potential of remotely sensed data to complement traditional data sources to improve understanding of climate change impacts and ways to 14
of the SNDP reports on plans to expand access to both primary health care (such as the Maternal, Newborn and Child Health and primary Mobile Hospital services) and specialized services (such as promoting outreach programs from tertiary hospitals to districts or the provision of mobile referral hospital services) and creation of additional health posts, district hospitals and expansion and improvement of existing hospitals and health centers.

The first tier of care is provided at the local level by district hospitals, health centers and health posts. The second tier consists of the larger provincial general hospitals. These hospitals both provide care directly and receive referrals from first-tier institutions, treating patients in need of more sophisticated care. The third tier consists of national and specialized hospitals, the highest-level referral hospitals in Zambia, which focus on patients with rare conditions or in need of complex treatments. Cases that cannot be effectively treated at second-tier hospitals are referred to third-tier hospitals. At each level a specified basic health-service package is publicly funded and available to the public at no charge. Importantly, more specialized services at the higher levels of the healthcare system may also be free if they are accessed by referral from a lower-level institution.

In an attempt to address unstable public-health funding arising from broader fiscal and macroeconomic volatility, Zambia introduced a system of user fees in 1993, though certain exemptions for specific services and specific age groups were allowed. In 2006 these fees were abolished for all primary health services in rural areas. In November 2011 user fees for primary healthcare were also eliminated in urban areas. According to Masiye et al (2008), the removal of user fees in rural areas was seen as a tool for bridging the rural-urban income divide and improving healthcare equity. However, its impact on the quality and accessibility of health care remains unclear, even after additional efforts were undertaken to increase health funding and provide for a more efficient distribution of drugs and other medical supplies.

In order to address public health priorities in a cost-effective and equitable manner, a substantial share of resources should be allocated to the lowest level of the healthcare system, i.e. first-tier institutions. However, in practice just over a third of health expenditures are allocated to primary healthcare (see Table 4). Expenditure data show that total public health spending is estimated at 2 percent of GDP, well below the expenditure share of many other countries in Sub-Saharan Africa.<sup>9</sup> Between 2006 and 2009,

> 9 Shares of public health care spending over GDP in the region are 9.3% in Sierra Leone, 3.9% in Namibia, 3.8% in Chad, 3.6% in Malawi, 3.5% in Niger, 3.4% in South Africa, 3.2% in Senegal. Nigeria and Ethiopia, at 2.1%, have similar shares than Zambia. Kenya, at 1.4%, has a lower rate.  See World Bank, Data Indicators, 2012b.

11
13 control for differences in rainfall patterns during the time of harvest and general geographic characteristics. The last component εit is a random error term that follows an iid normal law centered in zero.

As the PHL variable is censored between 0 and 1, equation (1) is estimated using the Tobit type I estimator (Wooldridge, 2002). The empirical application is to 2008-09 survey of Tanzania.<sup>22</sup> PHL declines as the storage depletion rate increases (Table A2 column 1), though it is not statistically significant and no longer holds when storage and crop technologies are controlled for, suggesting that the use of improved storage technologies and depletion are negatively correlated, i.e. the faster the depletion and the shorter the storage period, the lower the use of improved storage techniques. This is consistent with the observation by Stathers, Lambell and Mvumi (2013) that households usually only treat grains that are to be stored for more than 3 months, as insect populations usually do not reach damaging intensities until 4 months storage.

Consistently, the use of improved storage technologies reduces PHL—with the use of modern storage technologies having a higher PHL effect than the use of traditional storage technologies) (Table A2, Tobits 1 and 2). The use of poor storage is associated with an increase in PHL, as is the use of crop protection technologies.<sup>23</sup> Nonetheless, while indicative and consistent with the literature, the effects are all imprecisely estimated and further investigation is needed.

When it comes to marketing versus auto-consumption, losses appear higher when a larger share of the maize harvest is marketed, a loss which is reinforced the higher the number of sales hinting that the self-reported PHL estimates may include some of the losses incurred during

> 22 The Tanzania 2008-09 survey round is chosen because PHL was higher than in 2010-11. The TZ 2008-2009 survey has also been used to provide regional aggregates of the seasonal variables required to update the APHLIS PHL profiles. For TZ this led to 21 different data points (21 different PHL regions considered in APHLIS, compared with 4 regional breakdowns in Uganda, and 3 in Malawi). Regional APHLIS PHL estimates were adjusted with updated estimates of storage duration, share of marketed quantities during the first three months after harvest, regional production, and occurrence of rainfall at harvest, derived from the LSMS-ISA data. This induced larger cross-regional PHL heterogeneity, though mainly in the losses at harvest, during storage and marketing.

> 23 Locally-available crop protection chemicals are often of poor quality (fake, expired) and the instructions (dosage, timing) for their use inadequately applied (Stathers, Morris, Riwa, and Mvumi, 2004). They may also be especially used by more PHL-prone farmers (or promoted by development projects in more PHL-prone regions), with their PHL reducing effects not sufficient to offset higher PHL for other (unobserved) reasons among these farmers.
Figure 7: Wage effects of immigration, 2000 – 2010 (%), by age-education group <!-- Start of picture text --> NLD<br>USA IRL DEU FRA ITA ESP GRC DNK SWE NOR FIN CZE HUN POL JPN TUR<br>PRT<br>BEL<br>AUT<br>SVK<br>GBR<br>CAN<br>NZL<br>CHE<br>LUX<br>AUS<br>Young, LS Old, LS Young, HS Old, HS<br>6%<br>4%<br>2%<br>0%<br>−2%<br>−4%<br>−6%<br><!-- End of picture text --> <!-- Start of picture text --> The figure plots equilibrium wage responses to immigration<br><!-- End of picture text --> Figure 8: Wage effects of emigration, 2000 – 2010 (%), by age-education group <!-- Start of picture text --> USA JPN TUR<br>CAN AUS DEU AUT FRA BEL NLD ITA ESP GRC DNK SWE NOR CZE<br>GBR CHE LUX FIN HUN POL<br>NZL<br>SVK<br>PRT<br>IRL<br>Young, LS Old, LS Young, HS Old, HS<br>The figure plots equilibrium wage responses to emigration<br>6%<br>4%<br>2%<br>0%<br>−2%<br>−4%<br>−6%<br><!-- End of picture text --> 37
who find that children randomly assigned to participate in a preschool program exhibited greater inequality aversion than children assigned to a control group.

We also leverage the daycare experiment to study the origins of gender differences in social preferences. Men and women tend to behave differently when it comes to other-regarding preferences (e.g., Croson and Gneezy 2009). Are there biological explanations for why women tend to be more prosocial than men? Or do boys and girls develop different social preferences because they are raised differently from very early on (Beal 1994; Eagly 1997; Witt 1997)? The daycare experiment represented an exogenous change in the environment of boys and girls. Evidence of differential effects on boys and girls would provide support for the latter hypothesis.

The intent-to-treat results show that daycare attendance increased girls’ aversion to disadvantageous inequality but had no effect on the aversion of boys. In the two trials of the sharing task mentioned before, treatment girls were 9.5 percentage points more likely to choose the equitable option than control girls (p-value of 0.001). In contrast, one cannot reject that treatment and control boys were equally likely to make such choices (p-value of 0.482). Even if we adjust for 16 hypothesis tests (8 outcomes × 2 genders), the increase in the aversion of treatment girls to disadvantageous inequality remains statistically significant (p-value of 0.003).

To get a sense of the magnitude of these effects, we estimate the willingness to pay to avoid disadvantageous inequality, separately by gender. When they were behind, treatment girls were willing to pay twice as much as control girls – 0.26 tokens vs 0.13 tokens – to reduce the other child’s payoff by 1 token. There is no effect of daycare attendance on the willingness to pay of boys (control and treatment boys were willing to pay about 0.18 and 0.19 tokens respectively to reduce the other child’s payoff by 1 token).

As no pre-analysis plan was registered for this project, there is a legitimate concern about whether the effect of daycare attendance on the aversion of girls to disadvantageous inequality is spurious. Cappelen et al. (2020) provides an opportunity to investigate whether these differential effects by gender hold in a different sample. While these results were not included in the published paper, in their publicly available code Cappelen et al. (2020) break their results by gender. We reproduce these results in the paper for the purposes of comparison. They show that the effects of preschool attendance on inequality aversion that Cappelen et al. (2020) document are mostly driven by girls—which is consistent with our findings in the experiment in Rio. Despite the relatively small size of the sample, preschool attendance led to a statistically significant increase 4
<!-- Start of picture text --> 2 x 10° Stock ofNatural Gas: Model Baseline<br>NN<br>8 oN<br>_ ee<br>0.5<br>0 2020 2040 2060 2080 2100<br>2 | 7 Year<br><!-- End of picture text -->

<!-- Start of picture text --> 5 Stock of Coal: Model Baseline<br>7X 10<br>6p —<br>———<br>—~ee<br>5 ne——ae<br>ot<br>g5<br>3<br>2<br>1<br>0<br>2020 2040 2060 2080 2100<br>Year<br><!-- End of picture text -->
2014).<sup>23</sup> We matched TFDD’s river basin codes to the GRDC river basin codes to associate treaties with our observations at the river basin level.

We interact the presence of the treaty with the presence of a downstream country to see if the presence of agreements reduces free riding. The estimated equation is equation (2): in which _Mk_ is a binary variable indicating whether an international water management treaty is in effect in the GRDC basin in which the area is located and the interaction term is _CijMk._ We cannot identify a separate effect of the treaty on dam counts because any such effect is absorbed by the river basin dummy, _uk_ .

Columns 3 and 4 of Table 7 report estimates of equation (2). In column 3, all dams are used. In column 4, the dams are restricted to the dams with large reservoirs. The coefficients of interest in these equations are not statistically significant. The coefficient on the treaty interaction has an unexpected positive sign: rather than reducing the tendency to find more dams in areas upstream of borders, the point estimate would suggest an increase in dams upstream of other countries in the presence of a treaty.

One possible explanation of the counterintuitive point estimates in columns 3 and 4 is that water management treaties are endogenous. They may be more likely to emerge in watersheds that are valuable to more than one country (especially where resources are scarce); dams may also be more likely in such watersheds. Alternatively, institutions may arise specifically to support dam construction,

> 23 See: http://www.transboundarywaters.orst.edu.

17
_6_ **_PoUsh FLsc Criss_** Flgure **1** <!-- Start of picture text --> Profitability in the Enterprise Sector 1969-91<br>Profit/sales Tr, producer prices, irn %<br>40 -- --<br>5o \<br>20- - - - - - - - - - -<br>10<br>1969.01 02 03 04 1990.01 02 03 04 1991.01 02 C3 04<br>H istor i ca I cost_ ;Cash f I ow<br><!-- End of picture text -->

**The** relationships between CCA profit, HCA profit, and cash flow are summarized in the following box.

Unfortunately, we have no direct data on real accumulation of inventories; this must be estimatud from nominal inventory data. I do this by deflating nominal inventory data with a weighted moving average of the industrial price index. A fuller description of the ..iethod used to calculate real inventory accumulation is giveu in the Appendix.

The key assumption in the process is the estimate of the average life in invent^ry - the time between the purchase of materials and the sale of goods - in the entire Polish enterprise sector. The problem is that inflation was extremely rapid at a crucial period - late 1989 and early 1990. The estimates of real inventory accumulation for 1989 and 1990 are therefore rather sensitive to the choice of the length of the moving average.

Estimates of the average life in inventory for the entire enterprise sectot, and the corresponding weighted price deflators, are derived in the Appendix. Adjustments and allowances are made for: (1) the Polish definition of sales ("sales of own production"), which understaies **_Ui'i_** volu,; v; .vaW turnover by