rafmacalaba/datause-encoder-data huggingface dataset:
aimed at measuring time use over a specific set of activities—in which case a full accounting of the respondent’s activity over the reference period would be needlessly cumbersome. A recent impact evaluation study in northwest Bangladesh provides an example (see Box 1).

# **Box 1. Stylized questions in an impact evaluation study of the CARE-Bangladesh Strengthening the Dairy Value Chain Project**

Although the broad aim of the CARE-Bangladesh Strengthening the Dairy Value Chain Project was to improve the participation of smallholder farmers in the dairy value chain in northwest Bangladesh, several components of the project were specifically targeted to women, who are traditionally responsible for the care and feeding of livestock, even if these are regarded as mostly owned by men (Quisumbing et al. 2013). A key component of the project encouraged women to bring milk to collection points instead of selling them to itinerant buyers, which was expected to increase time that women spent on dairy-related activities. To assess whether the increased time involved in dairying had implications for domestic work, and whether increased time burdens were shared among household members, the authors asked respondents the following questions with respect 18 livestock-related activities and 5 household activities (see Figure 1):

- Who is primarily responsible for doing it? [MEMBER ID]

- Who does it in the absence of the responsible person? [MEMBER ID]

- In the past 30 days, who usually does it? [MEMBER ID]

- In an average week in the past 30 days, how many hours total has this person spent on the activity?

Importantly, the list of activities is not exhaustive. Instead, it focuses only on the specific areas of interest in which the project hypothesized that tradeoffs exist—livestock and household activities. Total time spent in these activities was aggregated for men, women, boys, and girls. The authors found that the increased allocation of adult women’s time to dairy activities came at the expense of time in household activities, with young girls (but not boys) consequently increasing their time in domestic work.

Conventional wisdom suggests that stylized questions are the least costly of the methods considered in this paper, based on the argument that they collect much of the same data as more extensive methods (e.g., time diaries), but require far fewer questions and less effort (e.g., shorter interviews) to implement. This is clearly true in most developed country settings, where stylized questions are often used to collect a person’s working hours in labor force surveys.<sup>5</sup> It is much less obvious whether stylized

> 5 See, for example, the labor force component of the United States Current Population Survey: <u>http://www2.census.gov/programs-surveys/cps/techdocs/questionnaires/Labor%20Force.pdf.</u>

4
indicate more heterogeneity in this regard. The p-value also provides a way to test for heterogeneity by examining the null hypothesis that all studies in the meta-analysis are evaluating the same effect. A p- value lower than 0.05 would reject this hypothesis at the 5% significance level and indicate that the results of the studies are more heterogeneous.

As seen in Figure 11 (savings behavior), the I-squared was 54.5%, which is not generally considered a low number (indicating consistency) but which is lower than the I-squared terms for the other outcome variables, indicating _relatively_ more consistency among the results for savings. The other meta-analysis results (Figure 12 - retirement savings behavior, Figure 13 - loan default behavior, and Figure 14 - record-keeping behavior) reveal higher I-squared statistics than Figure 11, indicating more heterogeneity between the studies involved in their respective samples.

Meta-analysis to evaluate the impact of financial education interventions on retirement savings produced weaker results and was unable to reject the null hypothesis of no effect on retirement savings at the 95% confidence interval, but it could reject the null when the confidence interval was relaxed to 90% (p=0.077). Five studies were included in this analysis, all based on programs in the U.S., none using randomized controlled trials and only two using exclusively classroom methods. Results are shown in Figure 12.

# Figure 12

|Papers testing|retirem|ent sav|ings behavior after|interventio|n|
|---|---|---|---|---|---|
||||||**%**|
|Authors|Date<br>Country|Intensity|Channel|**ES (90% CI)**|**Weight**|
|Bernheim & Garret|2003<br>USA|Varied|7|**0.12 (0.07, 0.18)**|**22.23**|
|Muller|2003<br>USA|Varied|1|**0.09 (-0.06, 0.24)**|**12.98**|
|Lusardi and Mitchell - ALP article|2007<br>USA|Seminars|NA|**0.23 (0.13, 0.33)**|**17.60**|
|Hung Yoong|2010<br>USA|Varied|2|**0.06 (0.01, 0.11)**|**22.98**|
|Beshears, Choi, Laibson, Madrian, Milkman|2011<br>USA|One letter sent|5|**-0.04 (-0.07, -0.01)**|**24.21**|
|Overall (I-squared = 87.7%, p = 0.000)||||**0.08 (0.01, 0.16)**|**100.00**|
|NOTE: Weights are from random effects analysis||||||
||||**0**<br>-.327|.327||

Care should be taken with the interpretation of these results – the sample diversity and small sample size in both the meta-analysis of general savings and retirement savings require that the results are seen as indicative rather than definitive in terms of the impact of financial education on savings. Still, they 21
2

**Table** 1. Drug Imports Per Capiia in Eighlt Afkan Countries, 1982-84

|Country|Per Capita<br>Drug Imports<br>USS|
|---|---|
|Senegal|7.40|
|<br>Zambia|6.50|
|Swaziland|4.80|
|Etopia|1.47|
|<br>Burkiba hso|1.36|
|Rwads|Q93|
|Uanda|Q93|
|Guione|0.86|

- uojustea; inacrrent terms **(795Z-4).**

SOW= **C_ambi ad** Ftu, **19lS** t pOl ests vai greatly from one county to another as we lThese fpre do, however, e an idea of the relative order of magnitude required to provde bask treatments using essential dmgs Whether a given amount of drugs i *sufficient depends on coverage of health servics, and on how many annual contas with the halth services for some form of treatment are thought to be 'adequate.

Data on per capita drug consumption may also overstate the availability of drugs in rural areas, s drugs tend to *stick' at central levels and in hospitals. Unless specific measures have been taken to ensure regular availabity of drugs in rural health centen, as in Zambia, Kenya, Uganda, Zimbabwe, Tanzania and a number of other countries, it is likely that virtually no drugs are reaching the lower level health facilities. In Tanzania Table 2 Average Costs per Treatment Episode at Various Levels in Several African Countries (USS)

|Countly|Year|Village|Dispensary|health<br>Center|i<br>Hospital<br>OPD|
|---|---|---|---|---|---|
|Kenya|1984||U.Z9|U.2U||
|<br>Kenya|1985||||_0.50_|
|<br>Sudan|1985|Q15|032|032|0.59|
|Bwundi|1986||0.37|037||
|Gambia|1967|0.12|0.24|_035_|0.50|
|Guln-Blsaau|1987|||031||
|Ugda (a)|1988||0.29|0.29|0.29|

(a) Represents total number of outpatient treatments, without designation -o **level.**

**_Som_** WHO **DMa**
**Table 3: Parameter estimates for aggregate metals**

||**Base**|**[1]**|**[2]**|**[3]**|**[4]**|**[5]**|
|---|---|---|---|---|---|---|
|**_Parameter esti_**|**_mates_**||||||
||5.01***|5.24***|4.78***|2.45***|4.96***|4.42***|
|y�|(0.30)|(0.29)|(0.33)|(0.57)|(0.32)|(0.32)|
|�|‐0.22***|‐0.24***|‐0.22***|‐0.09***|‐0.22***|‐0.18***|
|y�|(0.02)|(0.02)|(0.02)|(0.03)|(0.02)|(0.02)|
||‐0.26***|‐0.28***|‐0.25***|‐0.12***|‐0.27***||
|p�|(0.03)|(0.03)|(0.04)|(0.03)|(0.03)|―|
|_Inv. share_|―|‐0.14**<br>(0.07)|―|―|―|―|
|_Urbanization_|―|―|0.39**<br>(0.18)|―|―|―|
|_Pop. density_|―|―|―|‐0.24**<br>(0.14)|―|―|
|_Trend_|―|―|―|―|0.00<br>(0.00)|―|
|_Nom. price_|―|―|―|―|―|‐0.18***<br>(0.04)|
|_RER_|―|―|―|―|―|0.01**<br>(0.00)|
||‐0.19***|‐0.20***|‐0.21***|‐0.29***|‐0.19***|‐0.19***|
|ρ|(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|
|**_Key statistics_**|||||||
|Hausman test|2.26|6.98|5.06|0.88|7.07|4.96|
|_p_‐value|0.52|0.14|0.28|0.93|0.13|0.29|
|Log‐likelihood|1,853|1,880|1,835|1,857|1,853|1,887|
|Observations|2,165|2,137|2,113|2,001|2,165|2,165|
|Countries|43|43|42|43|43|43|

**Notes** : The dependent variable is the logarithm of aggregate metal consumption. Three (***), two (**), and one (*) asterisks denote significance of parameter estimates at 1, 5, and 10 percent level, respectively. Stand‐ ard errors in parentheses. “―” indicates that the corresponding variable was not included in the model.

_— 20 —_
an annual rate of 1.9 percent over this period of time, the bottom decile experienced an annual growth of 6.2 percent. These growth rates along the GIC allowed changes in income concentration (Figure 2b) where households up to the 86th percentile increased their share in the total wealth (defined by the total per capita income) by non-negligible reductions of the households in the top decile of the income distribution, thus reducing the Gini coefficient, an inequality measure that is particularly sensitive to inequalities in the extremes of the distribution.<sup>9</sup> However, these patterns changed substantially between 2010 and 2012, when both the regional growth incidence curve and the income concentration flattened.

<!-- Start of picture text --> Figure 2a. Growth incidence curve of household Figure 2b. Change in concentration of<br>per capita income, Latin America household per capita income in Latin America<br>2003-10 and 2010-12 10 2003-10 and 2010-12 11<br>10 0.1<br>8 0<br>6 -0.1<br>4 -0.2<br>2 -0.3<br>0 -0.4<br>0 10 20 30 40 50 60 70 80 90 100 5 15 25 35 45 55 65 75 85 95<br>Per capita income percentiles Per capita income percentiles<br>GIC 2003-10<br>2003-10 2010-12<br>GIC 2010-12<br>Annualized growt rate<br>Average anual change (pp)<br><!-- End of picture text --> Source: Authors’ calculation from SEDLAC (CEDLAS and the World Bank). Note: The regional Gini coefficient is computed based on pooled country-specific data for 17 countries. The methodology to pool these datasets is explained in footnote 1 and the years used for each country in the weighted average are detailed in Table A2 in the annex.

The deceleration in inequality reduction in the region between 2010 and 2012 is captured by other inequality measures that weight the disparities between households along the income distribution differently than the Gini coefficient. Similar declining trends in income inequality in Latin America for the period 2003-2010 and stagnation (even reversal) for the period 2010-2012 are captured by the Theil index of inequality (Figure 3). Between 2003 and 2010, the Theil index dropped by 8.7 points from 63.1 to 54.4 (and 14.3 points from 1995 to 2010). While the Theil continued to fall between 2010 and 2011, the following year it rose for the first time in the last decade, from 54.4 to 54.8. Signs of

> 9 See Hey and Lambert (1980).

> 10 Figure 2a presents the annualized growth rate of per capita income for every percentile of the per capita income distribution. The figure excludes the first percentile since results are usually sensible to the presence of zero incomes.

> 11 Figure 2b was computed for 20 quantiles of the income distribution, from the lowest 5 percent of households on the left to the highest 5 percent on the right, each group was labeled with the maximum percentile that each income quantile includes, thus the figure labeled as 5<sup>th</sup> percentile includes information from the percentile 1<sup>st</sup> to the percentile 5<sup>th</sup> , while the results of the percentile 100<sup>th</sup> includes information from the percentile 96<sup>th</sup> to percentile 100<sup>th</sup> .

5
AccessBank started to normalize. Some first spillovers of the economic crisis already occurred during the summer of 2008 when oil prices and exports started to decline. However, the sharp contraction of exports happened in the fourth quarter of 2008 and the first quarter of 2009 with exports to the neighboring CIS countries exhibiting a more sustained decline than total exports (see Appendix 1 for details). At the same time, households’ income streams were affected by the effects of this global crisis as remittances fell by around 20% in 2009 compared to 2008 (World Bank (2011)). Together with the beginning slump of the retail car market and decreasing apartment sales reported in November 2008<sup>12</sup> this implies that the demand for products, non-subsistence goods (and services) in particular, declined from the fourth quarter of 2008 onwards. Finally, bank credit to households contracted by around 2% in 2009 compared to a rapid expansion during the years before (Economist Intelligence Unit (2010)). Turning to the bank’s marketing efforts during this time period, in contrast to the period of tight refinancing, the availability of business loans was explicitly and actively marketed in 2009 to show AccessBank’s ability and willingness to be a reliable partner also during the crisis (AccessBank (2009)).

Taking all this together, the effect of the real economy shock on the number of loan applications is ambiguous. On the one hand, loan applications could increase at AccessBank because of the liquidity problems and the reduced lending at other banks. On the other hand, loan applications could decrease because business borrowers saw their sales decline and thus, for instance, postponed investment. However, in contrast to the first event, we expect approval rates to decrease because of the worsening economic situation. Screening of borrowers should have become more important to identify the less risky as borrower quality in general started to deteriorate. We expect to find a differential effect for agro, micro and SME borrowers. SME loans are likely to be affected most because SME firms often produce non-subsistence goods or services or may even be suppliers to the oil industry and are therefore more likely to be hit by the economic slowdown and to see their credit quality deteriorate.

Our empirical analysis will focus on these two distinct crisis events with the pre-crisis period comprising the years 2006 and 2007.

# **3.5 Determinants of credit approval**

> 12 See Economist Intelligence Unit (2009a) and http://www.eurasianet.org/departments/insightb/articles/eav120108a.shtml.

13
- 1.2. The SUST-RUS database contains data on air pollution for 32 industries of 7 Federal Districts of the Russian Federation in year 2006. The industry list ( _s_ ) is presented in Table A2, while set of regions ( _r_ ) (Federal Districts) is presented in Table A3.

- 1.3. Total emissions of non-CO2 pollutants

- (p∈NCO2, where NCO2 = {PM, SO2, CO, NOx, CnHm, VOC}) by industries at national level (NATEMITs,p) are presented in Table A4. These are data for year 2006, by industry and type of pollutant from industrial stationary and mobile sources in thousands of tons. The main source of these data is Ministry of Natural Resources and Environment of the Russian Federation (2007).

- 1.4. Aggregate regional emissions by the type of non-CO2 pollutants (REGEMITr,p) are presented in Table A5.

- 1.5. Non-CO2 pollution is assumed to be process-based and varies with output. Values of industrial output in the SUST-RUS database (Ys,r) by Federal Districts of the RF, measured in millions of rubles of 2006, are presented in Table A6.  Sources of values of industrial output in the SUST-RUS database are discussed in SUST-RUS (2012c, 3.1.5 Regional output statistics): “The origin of the regional output data is the Rosstat online database,” available at: (http://www.gks.ru/dbscripts/Cbsd/DBInet.cgi).

- 1.6. The authors calculate an emission intensity index of non-CO2 pollutants (p∈NCO2) at the regional level (Xp,s,r) for the SUST-RUS pollution data. It is estimated according to an output-weighted least-squares procedure (Equation 1), subject to total regional emissions (Equation 2) and national sectoral emissions (Equation 3). The procedure ensures that emissions by pollutant, sector, and region are close to average national sectoral emissions intensity index (X<sup>�</sup> p,s), deviating from the mean for more polluted regions proportionally with regional output (Ys,r).

# **Equation 1**

# **Equation 2**

# **Equation 3**

- 1.7. The average national sectoral emissions intensity index (X<sup>�</sup> p,s) for the SUST-RUS data is presented in Table A7, measured in thousand tons of pollutant per trillion rubles of output, in nominal prices of 2006.

- 1.8. The resulting regional emissions intensity indices of non-CO2 emissions (p∈ NCO2) at the regional level (Xp,s,r) for the SUST-RUS sectoral classification are presented in Table A9 - Table A14.

- 1.9. The model’s (“the WTO model’s”) sectoral and regional lists differ from those of SUST-RUS. The industry list ( _g_ ) of the WTO model is presented in Table A15,

while set of regions ( _reg_ ) of the WTO model is presented in 86
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at least as great as _<u>u</u>_ and provides a quality level that equalizes the each party’s material payoffs. Therefore, if at least one of the parties is of the fair type, the compensation has to reflect equal material payoffs for the parties. Additionally, for both types to cooperate in the long-term relationship, the gains from cooperation need to be greater than the gains from deviation. In this case, the DICCs are the same as _DICCs_<sup>_m_and</sup><sup>_DICC_</sup> _b_<sup>_m_.</sup> If either party is of a fair type the buyer solves the following maximization problem: If _P_ ( _q_ ) =<sup>_V_</sup><sup><u>(</u></sup><sup>_<u>q</u>_</sup><sup><u>)+</u></sup> 2<sup>_c_</sup><sup><u>(</u></sup><sup>_<u>q</u>_</sup><sup><u>)</u></sup> , then<sup>_V_</sup><sup><u>(</u></sup><sup>_<u>q</u>_</sup><sup><u>)+</u></sup> 2<sup>_c_</sup><sup><u>(</u></sup><sup>_<u>q</u>_</sup><sup><u>)</u></sup> _−c_ ( _q_ ) _≥_ _<u>u</u>_ needs to be true for the seller to participate. This implies that<sup>_V_</sup><sup><u>(</u></sup><sup>_<u>q</u>_</sup><sup><u>)</u></sup> 2<sup>_−c_</sup><sup><u>(</u></sup><sup>_<u>q</u>_</sup><sup><u>)</u></sup> _≥_ _<u>u</u>_ needs to be true. In other words, the total surplus needs to be high enough so that _V_ ( _q_ ) _− c_ ( _q_ ) _≥_ <u>2</u> _<u>u</u>_ <u>.</u> Now, solving the _DICCs_<sup>_m_for</sup><sup>_p_,wegetthesamelowerboundforthebasepaymentas</sup> the case with self-interested parties. By substituting _p_ =<sup>_V_</sup><sup><u>(</u></sup><sup>_<u>q</u>_</sup><sup><u>)+</u></sup> 2<sup>_c_</sup><sup><u>(</u></sup><sup>_<u>q</u>_</sup><sup><u>)</u></sup> _− b_ ( _q_ ), the structure of the total compensation of the optimal stationary REDD contract is derived and it is given in the next Proposition.

**Proposition 9.** _The optimal REDD contract, ⟨p_<sup>_∗_</sup> _, b_<sup>_∗_</sup> ( _q_<sup>_∗_</sup> ) _⟩, when the buyer and / or the seller are fair-type, implements forest conservation,_ _<u>q,</u> if δ is high enough, S_ ( _q_ ) _≥_ <u>2</u> _<u>u,S</u>_ ( _q_ ) _≥_ <u>2</u> _<u>π,</u> the IRCs_<sup>_m,IRC_</sup> _b_<sup>_m,DICC_</sup> _s_<sup>_m,DICC_</sup> _b_<sup>_mand(35)aresatisfied.Thecontractisgivenby:_</sup>

When the total surplus is such that<sup>_S_</sup><sup><u>(</u></sup> 2<sup>_<u>q</u>_</sup><sup><u>)</u></sup> _≥_ _<u>u</u>_ <u>,</u> the total payment in the presence of fair parties is higher than the total payment in the presence of purely selfish parties. By using this total payment, the buyer and the seller share equally the surplus. Recalling the assumptions about the cost of forest conservation, _c_ ( _<u>q</u>_ <u>) = 0, the base payment included in the</u> 25
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29