rafmacalaba/datause-encoder-data huggingface dataset:
studies adopting this methodology, the threshold ranges from 1.5 to 3.0 and few logical explanations on the choices of thresholds are found. The lower the threshold is the more crisis periods are identified. This analysis uses 2.0 as the threshold, since this is about an average of currency crisis studies, and then the probability to exceed 2.0 is 2.5 percent. Regarding sample periods, this analysis uses five years so that the number of sample data becomes 20 on a quarterly basis, since the minimum number of data for an analysis is 20 as a rule of thumb.

As shows in Fig. 1, the degree of crisis is measured by the combination of the length of crisis periods and depths. The depth of a crisis in period (t) is the standardized score of period (t) when exceeding threshold (2.0). As such, the total degree of a crisis episode is measured by the following equation (2), and two sets of conditions to identify a crisis episode is further explained in section 2.4.

where a _it_ is a crisis depth of ( _m)_ different type of crises ( _i_ >2.0) at the period of _t_ and _n_ is the length of a crisis episode. Equation (2) shows that the degree of a crisis episode is determined by the length, the depth and the number of crisis types involved.

# **2.3 Multidimensional Aspects of Crises**

One of the most important characteristics of recent crises is multidimensionality in which several types of crises occur simultaneously and/or one type of crises affects other crisis types within a short period. Although most crisis studies focus on five types of crises such as banking, balance of payments, currency, debt and financial crises, few studies handle all fives types of crises together and existing studies miss other important types of crises. This article includes growth rate crises, and banking crises is divided into liquidity type banking crises and solvency type banking crises. Accordingly this article deals with seven types of crises. A change in growth rates affects other crisis types and therefore growth rate crises are treated equally to other types of crises, although some studies (for example, Gupta, Mishra and Sahay (2001), and Aziz et al (2000, p.23), Hutchison and Neuberger (2002)) use growth rates to measure the costs of other types of crises. The literature review shows existing crisis studies are ambiguous about conceptual and operational definitions of crises. Thereby the definitions should be clarified. Table 2 shows the summary result of the clarification.

7
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should have much larger behavioral effects than subsequent amounts. Designing studies with multiple reward amounts (RESPECT used $20, $10, $0) will be particularly important for testing such hypotheses.

In addition to the above discussed pathways for behavioral change in response to the incentives, it is also useful to consider potential long-term effects of time-limited incentives. Two competing hypotheses are of particular interest:

# **_Learning_**

For behaviors which individuals may not have tried until encouraged to by the incentives (e.g., use of condoms), it is possible that the incentives will induce learning (and reinforcement) that could result in permanent positive behavior changes even after withdrawal of the incentives.

# **_Reduced Intrinsic Motivation_**

Alternatively, psychologists have emphasized the potentially pernicious effects of extrinsic monetary incentives in destroying the intrinsic desire to engage in positive behaviors. Cameron et al. (2001) reviews the literature on the possible destruction of intrinsic incentives and concludes that while this might occur for some high-interest tasks, in general incentives do not have pervasive adverse effects.

# **RESPECT Study: Design Considerations and Lessons Learned**

In this section we highlight major considerations in designing a sexual behavior incentive study, providing rationale for choices made in designing RESPECT, so as assist others in designing similar studies in the future. Table 3 provides 2009 baseline summary statistics on key variables discussed below. Further details of the RESPECT study are described elsewhere (de Walque et al., 2012).

# **_Ethical Considerations_**

Ethical concerns permeated all aspects of project planning, as referred to in many of the subsequent sections. The use of incentives in the sexual arena raises many 14
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# **Table A8: Household coping strategies**

||**I**|**ncreasin**|**g incom**|**e**|**C**|**utting ex**|**penditu**|**re**|**Relying**|**on help**|**from re**|**latives**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**2014**|**2015**|**2016**|**2017**|**2014**|**2015**|**2016**|**2017**|**2014**|**2015**|**2016**|**2017**|
|Average|2.81|4.21|3.18|3.88|15.25|25.63|29.15|28.04|1.29|1.64|1.66|1.42|
||||Poor|and vuln|erable p|opulation|||||||
|Vulnerable|4.04|5.48|4.18|5.16|13.43|24.69|26.38|24.00|2.10|1.31|2.89|2.22|
|Official poverty|1.79|6.54|1.36|6.14|18.98|25.21|25.37|24.12|1.94|1.83|3.90|2.65|
|Moderatepoverty|2.97|5.37|3.71|4.32|16.76|25.71|27.02|26.91|1.88|1.48|2.16|1.87|
|||||Place o|f residen|ce|||||||
|Minsk|2.81|3.50|3.94|3.45|15.58|27.72|30.96|31.14|1.08|3.29|1.23|0.99|
|Large cities|1.88|4.86|2.94|3.17|17.81|29.41|31.31|30.03|1.35|1.09|1.50|1.46|
|Small cities|3.77|4.36|2.63|5.75|15.00|24.38|34.17|28.49|2.01|1.92|3.65|2.39|
|Rural area|3.21|3.66|3.35|4.04|11.93|19.36|21.26|22.53|0.78|1.05|0.88|1.06|
||||||Age||||||||
|Child|3.61|5.41|3.99|4.79|14.24|26.56|28.21|26.41|2.12|1.83|2.37|2.09|
|Working age|3.24|4.77|3.58|4.20|14.84|26.55|29.63|26.89|1.26|1.70|1.76|1.29|
|<br>Retiree|1.33|2.25|1.86|2.73|16.85|23.15|28.87|31.10|0.73|1.38|0.97|1.21|
|||Empl|oyment|status fo|r workin|g age po|pulation||||||
|Employed|3.06|4.30|3.33|4.13|14.10|25.48|29.45|26.41|0.88|1.35|1.25|0.96|
|Partly employed|3.76|4.83|7.09|7.15|17.54|30.65|28.77|28.50|1.41|1.58|2.08|0.88|
|Non‐employed|3.15|5.23|3.30|4.07|16.62|26.90|27.59|27.35|2.20|2.65|2.88|2.21|
|||Edu|cation l|evel for|working|age popu|lation||||||
|High education|3.61|4.28|4.30|4.11|15.23|27.52|27.63|27.12|1.44|1.81|1.20|1.00|
|Secondary specialized|2.90|4.82|2.80|4.12|14.77|25.70|30.99|29.20|1.46|1.16|1.73|1.18|
|Other|3.30|4.85|3.75|4.34|14.82|26.25|29.86|24.74|0.99|1.49|2.18|1.59|
||||T|ype of h|ousehold|(HH)|||||||
|HH without children|2.29|3.25|2.70|2.98|16.45|25.36|29.52|29.12|0.65|1.49|1.20|0.94|
|Retiree HH|1.19|1.55|1.18|1.63|15.51|22.80|27.87|31.58|1.02|1.58|1.32|1.55|
|HH with children|3.32|5.17|3.67|4.78|14.06|25.91|28.77|26.97|1.93|1.79|2.12|1.90|
|1 child|2.87|4.62|3.30|4.74|14.23|25.78|30.82|27.79|1.70|2.00|2.06|1.79|
|2 children|3.99|5.87|3.75|4.59|13.16|26.89|25.64|25.95|2.23|1.84|2.34|2.42|
|3 and more children|3.58|6.60|6.21|5.83|16.21|22.99|25.67|24.69|2.23|0.00|1.62|0.60|
|singleparent|2.57|6.50|4.13|5.46|15.59|29.87|28.86|29.83|3.49|3.03|3.71|4.54|

Source: Authors’ estimates based on HSS data.

35
**Table A2: Descriptive Statistics on Importer-Broker Relationships**

||**By i**|**mporter**|**By im**<br>**sem**|**porter-**<br>**ester**|
|---|---|---|---|---|
|**Number of brokers**|N|%|N|%|
|1|2,319|63.36|8,077|83.41|
|2|765|20.90|1,200|12.39|
|3|322|8.80|284|2.93|
|4|139|3.80|92|0.95|
|5|57|1.56|20|0.21|
|6|29|0.79|8|0.08|
|7|11|0.30|1|0.01|
|8|8|0.22|1|0.01|
|9|5|0.14|||
|10|5|0.14|||
|Total|3,660|100.00|9,683|100.00|

_Notes:_ The table shows the distribution of the number of brokers each importer works with over the period. The sample covers the period January 1, 2015 to November 17, 3018.

61
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## AN 3. **BASIC** EARNNGS **FUNCTONS**

# Colombia 1980

|||Ge|nder|Sec|tor|
|---|---|---|---|---|---|
|Variable|Entire<br>Sample|Males|Females|Private|Public|
|Years of Schooling (S)|0.186|0.186|0.173|0.167|0.140|
|Experience (EX)|0.066|0.081|0.042|0.060|0.060|
|Experience-squared(EX<sup>2 </sup>)|-0.0008|-0.0010|-0.0005|-0.0008|-0.0009|
|Log Hours (H) Worked|0.584|0.395|0.577|0.690|0.434|
|Constant Term|4.020|4.668|4.299|4.000|4.665|
|**1R2**|0.34|0.33|0.35|0.35|0.10|
|N|11,373|7,017|4,356|8,136|3,237|
|Means||||||
|Eamings (Y)|8,401|10,124|5,626|10,374|3,442|
|S|6.9|7.0|6.8|7.7|5.0|
|EX|20|21|18|21|17|
|H|48|50|46|47|51|
|Var (Ln Y)|1.479|1.610|1.169|1.295|1.225|

Source: _Encuesta Nacional de Hogares, CEPAL._ Notes: The eamings variable (Y) is in pesos per month. H is the weekly hours worked.

All coefficients are statistically significant at the _5%_ level or better.
PHN 1991 Sector Review v 2i. Third, the full potential of PHN policies and Interventions to contribute to institution building and poverty alleviation can be realized only when policies are grounded in a macroeconomic and multisectoral policy framework oriented towards growth with poverty reduction, accompanted by a sound strategy for building institutions and the capacity to manage and implement policy. This means that countly operations divisions have a critical role in assisting cey national decisionmakers to understand and internalize poverty alleviation and institution-building objectives and to plan and prioritize sector interventions and allocate resources accordingly.

22. Fourth, more creative use of Bank instruments and resources should be sought through: (1) a review and assessment of the optimal use of lending instruments for PHN sector interventions; (2) increased innovation in the identification and financing of local expertise; and (3) increased efforts to encourage the exchange of experience and ideas among developing countries, over and above the notable efforts of the World Bank's Economic Development Insititute (EDI).
**<u>Table 4: Wage Bill Growth and Positive/Negative Output Gaps</u>**

|Dependent Variable:<br>|Wage_Bill_<br>|Growthit(Gro<br>|wth Rate of R<br>|eal Wage Bill,<br>|%)<br>|
|---|---|---|---|---|---|
|Model|OLS<br>(1)|FE<br>(2)|D-GMM-1<br>(3)|D-GMM-2<br>(4)|D-GMM-3<br>(5)|
|Wage_Bill_Growthit-1|0.218***|0.028|0.135|0.125|0.172|
||(0.069)|(0.074)|(0.081)|(0.080)|(0.075)|
|IMF_Programit|-1.484|-2.525*|-2.882|-3.016|-13.029|
||(1.151)|(1.419)|(2.167)|(2.512)|(14.530)|
|Revenue_GDPit|-0.265**|0.220|0.379|0.521|0.129|
||(0.119)|(0.248)|(0.657)|(0.363)|(0.283)|
|Positive_Output_Gapit|1.152***|1.159**|1.621**|1.383*|0.714|
||(0.347)|(0.429)|(0.616)|(0.710)|(0.898)|
|Negative_Output_Gapit|1.452***|1.677***|1.254**|1.643***|-0.160|
||(0.487)|(0.525)|(0.575)|(0.551)|(1.620)|
|Electionsit|3.083*|3.153|3.252*|2.748*|0.830|
||(1.768)|(2.005)|(1.658)|(1.406)|(0.823)|
|Year Effects|Yes|Yes|Yes|Yes|Yes|
|Internal Instruments|No|No|Yes|Yes|Yes|
|# of Instruments|--|--|23|23|22|
|AR(1) Test p-val.|--|--|0.002|0.009|0.094|
|AR(2) Test p-val.|--|--|0.225|0.289|0.747|
|Hansen J Test p-val.|--|--|0.720|0.720|0.462|
|Sample Period|2000-2011|2000-2011|2000-2011|2000-2011|2000-2011|
|Countries|26|26|26|26|19|
|Observations|236|236|210|210|171|

Notes: The unit of observation is a country-year from the sample described in the Data Appendix. Columns (1) and (2) report standard errors clustered at the country level. Column (3) reports one-step difference GMM with clustered standard errors, while column (4) reports two-step GMM whose standard errors have had the Windmeijer correction. Column (5) reports only Western European countries. The internal instrument is the second lag of Wage_Bill_Growthit, and internal instruments are collapsed. ***, **, * indicate statistical significance at the 1%, 5%, 10% levels, respectively.

# **_H3: Impact of Wage Bill Spending on Fiscal Position_**

The third hypothesis is that wage bill expansions are associated with a deterioration of the overall fiscal position. To test this latter hypothesis, we regress the government balance as percentage of GDP as the dependent variable against the size of the wage bill (% of GDP) and a number of control variables. The estimated equation is defined below and the error term is specified as in (2): 12
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3.28 When governments engage in a crop diversification effort, they are not directing their attention primarily to plantation owners and producers of tree crops but to the millions of small farmers whose land is devoted mostly to production of non-rice field crops. Uplt i areas encompass a wide range of land types. Marginal rice land with lower clay content is the most readily adapted to growing non-rice crops. Soils of lesser fertility are often a feature of hilly terrains, and eroded or stony soils limit the range of crops that can grow without major investment in soil modification. Dry, sandy limestone soils are often best suited to root crops such as cassava or to corn or sorghum, which are moderately drought tolerant. Farmers of such land are generally poor, and families are dependent on the production of food staples for their own consumption. They cannot plant tree crops such as clove, banana, or coconut, on their small holdings, which might raise their cash income, because they cannot forgo several years of crop production until the trees come into bearing. Farmers in other areas have highly acidic soils that require the addition of agricultural lime to increase crop yields. Problems of soil fertility have surfaced in newly settled agricultural areas that were part of transmigration projects, notably the resettlement of Javanese farmers on Indonesia's islands of Kalimantan and Sulawesi.17 As forests were cleared, the fragile soils that once supported rain forest have been depleted by crop cultivation. And at the extreme end oZ the range of land and soil types, slash-and-burn (shifting) agriculture is still a feature of some parts of Asia, and like other areas of former rain forest, the soil quickly loses its fertility after several crop seasons.

3.29 This diversity of land types means that government policies intended to promote crop diversification will not meet with an equal response from all farmers. The ability to respond is very much affected by the agronomic environment of their land. Some farm households cannot respond so flexibly to changes in commodity prices as others who can assume the risks of a new crop, can afford the cost of inputs, and can go into the markets and purchase food for part of their own consumption. Farmers may have land of only moderate productivity, but they do look for opportunities 17/ See The Economist, December 19, 1987, p.36.
product classi...cation fraud whereby exporters misdeclare the country or misclassify products in their customs declaration forms. Finally, the model shows that the relationship between trade discrepancies and tari¤s depends on the e¤ect of tari¤s on the detection rate of the importing countries.

The empirical section of the paper heeds the message of Goldberg and Pavcnik (2016): “Measure (trade barriers) before you estimate (trade impacts)! ”We ...rst led the NTM data collection e¤orts of the UNCTAD and the World Bank which allow us to generate an NTM database of 50 importing countries at the detailed 6-digit Harmonized System (HS) level with bilateral variation across 96 trading partners.<sup>11</sup> We then rely on product level quantitybased gravity regressions to estimate the bilateral AVE of NTMs at the HS 6-digit level. Our estimated AVEs are reasonable and in line with previous studies, with a sample mean of 4.7 percent when NTMs are present. Using the estimated AVEs, this paper then assesses whether the presence of Border NTMs contributes to large trade discrepancies as shown in the theoretical model. The results show that exporting countries that face higher AVEs have larger trade discrepancies, consistent with the hypothesis of country origin fraud in order to gain more favorable market access conditions. Similarly, the regression results also demonstrate that the products that have higher AVEs are those that have larger trade discrepancies, consistent with the hypothesis of product classi...cation fraud with the purpose of reducing the costs of compliance with Border NTMs. These results are consistent with the theoretical model and are robust to simultaneity and measurement errors in AVEs, which we address with the use of instrumental variables. Overall, this paper ...nds that a 10 percentage point increase in the AVEs could lead to a 15 percent increase in trade discrepancies, while a comparable increase in tari¤s leads to only an 8 percent increase in trade discrepancies. These ...ndings suggest that a signi...cant part of trade discrepancies is due to trade fraud associated

> 11This is done by working with local consultants in identifying and codifying countries’laws and regulations that may a¤ect international trade. Some of these laws and regulations target speci...c products or countries.

4
# **3.  Conceptual Framework**

## **3.1 Economic Feasibility of Water Transfers**

23. In order for economic welfare to improve from a reallocation of water among use sectors, the reallocation from sector _i_ to sector _j_ would need to yield incremental gains to sector _j_ in excess of the forgone benefits in sector _i_ .<sup>8</sup> Building on Young (1986; 2005) who, in turn, had built on earlier writing of Howe and Easter (1971), the economic feasibility of the water transfer can then be tested by developing measurements for two conditions as described below.

24. **Conditions for Economic Feasibility.** The _first condition_ is that the direct economic benefits<sup>9</sup> and secondary economic benefits<sup>10</sup> in the receiving sector, net of transaction and physical conveyance costs, need to be greater than the foregone direct and secondary economic benefits in the sector where the water is presently used.<sup>11</sup> where: DBi = direct benefit (value) to receiving sector _j_ ; SBi = secondary benefit to receiving sector(s) _j_ , if any; FDBi = foregone direct benefit (value foregone) in source sector _i_ ; FSBi = foregone secondary benefit in source sector _i_ , if any; TPC = transaction and planning costs (for information, contracting and enforcement of transfer agreement plus project design costs); and CC = (physical) conveyance costs, including possible storage costs.

25. A _second condition_ is the costs of the transfer must less than those which would be incurred for the best (least-cost) alternative source of water supply for the receiving sector.

where:

> 8 This would also hold for transfers from lower- to higher-valued agricultural uses.

> 9 Direct economic benefits are those accruing to the actual users of water.

> 10 Secondary economic benefits accrue due to market links to suppliers of inputs and processors of outputs.

> 11 An important point about the notation used in equations 1a and 1b is that, for simplification, each of the expressions represents the _present discounted value_ of the concept.  Thus, each expression assumes a known discount rate, known production, water transfer and consumption technologies, known product and input prices and a time horizon appropriate to the problem situation.

8
The sample is therefore nationally representative for grade 3 and 4 teachers in public Frenchspeaking schools in Guinea.

Out of this sample, 140 schools were randomly assigned to each of the two treatment arms —recognition or in-kind performance rewards— and 140 schools were randomly assigned to the control. Random assignment was carried out publicly in the context of a program launching workshop, after the baseline data collection. Names of schools were put in bowls (per strata) and picked (blindly) by young children to be assigned to one of the groups. The public nature of the randomization process aimed at maximizing trust in the transparent and impartial nature of the allocation process.

# **2.3 Data**

Data were collected on schools, teachers, and students using the following survey instruments: (i) a questionnaire administered to the school’s principal, (ii) a teacher questionnaire administered to targeted teachers (grade 3 and 4), (iii) a Math test and French test (with different parallel booklets) administered to students in the targeted teachers’ classrooms, and (iv) an inspection bulletin administered to targeted teachers in the context of two lessons, one in French and one in Math. Table A.4 presents a more detailed description of the various instruments.

All grade 2 and 3 students in the sampled schools were assessed at baseline (May 2012), and all students in grades 2 to 4 were assessed at midline (May 2013). Because of budget constraints, a random sample of up to 25 students were assessed from each class in grades 3 and 4 at endline (May 2014). The surveys reached a varying number of schools across the different rounds (Table A.5). For example, we have student assessment data for 408, 417, and 391 schools for baseline, midline, and endline rounds of data collection, respectively. It is unclear the source of the variation in number of schools; however, it is likely that, at least for the endline, the outbreak of Ebola might have been a contributing factor.

Most data collection activities were carried out using the existing government systems (no external survey firm was used) and took place simultaneously in each region of the country. Student and teacher tests, as well as principal, teacher and student questionnaires were administered by decentralized government staff selected on a competitive basis and trained as enumerators and supervisors. Quality of the data collected through the above mechanisms was validated through spot check visits from field coordination teams.

The data collection effort was set up as a panel of schools, not of teachers or students. Some teachers —those still teaching in the surveyed grade— appeared in multiple rounds of the data collection; other teachers were new and others left the grade of the school. As such, 7