rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: Studies description

||Bangladesh<br>(1)|Burkina Faso<br>(2)|Gha<br>Year 1<br>(3)|na<br>Year 2<br>(4)|India<br>(5)|Kenya<br>(6)|Mali<br>(7)|
|---|---|---|---|---|---|---|---|
|Citation|Hill et al.(2019)|Stoeffer et al.<br>(2022)|Karlan et al.<br>(2014)|Ibid.|Cole et al.<br>(2017)|Bulte et al.<br>(2019)|Elabed and<br>Carter(2014)|
|Published|Yes|Yes|Yes|Ibid.|Yes|Yes|No|
|Data publicly available<sup>a</sup>|No<sup>b</sup>|No|No|Ibid.|Yes|No|No|
|Start year|2013|2013|2009|2010|2009|2016|2011|
|Experimental design|Price<br>experiment<sup>c</sup>|Price<br>experiment|Free provision|Price<br>experiment|Free provision|Free provision<sup>d</sup>|Price<br>experiment|
|Randomization level|Cluster|Cluster|Household|Cluster,<br>household|Household|Household|Cluster|
|Additional treatment|None|None|Cash grants|Ibid.|Fertilizer<br>coupons|None|None|
|Eligibility criteria|Membership to<br>GUK<sup>e</sup>|Membership to<br>a farmer group<br>operating with<br>Softex (cotton<br>purchasing<br>company)|Maize<br>cultivation,<br>less than 15<br>acres|Ibid.|Land<br>ownership|Membership to<br>a farmer group|Membership to<br>a cotton<br>cooperative|
|Sampling frame|Villages of the<br>Bogra region|Farmer groups<br>of the Houndé<br>region|Villages in<br>Northern<br>Ghana<br>included in<br>the GLSS5+<br>survey|Expansion to<br>villages<br>within 30<br>kilometers of<br>rain gauges|Villages in two<br>districts in<br>Andhra<br>Pradesh,<br>Mahbubnagar<br>and Anantapur|Farmer groups<br>of the Meru<br>region|Cooperatives of<br>the Bougouni<br>region|
|Baseline survey|Yes|Yes|Yes|Ibid.|Yes|Yes|No|
|Sample size|2,300|1,015|385<sup>f</sup>|1406<sup>f</sup>|1,479|780|981|
|Number of clusters|120|80|60|72|45|40|87|

Source: Authors’ elaboration.

> a As of August 2020 (when we registered our study and pre-analysis plan).

> b The published article contains an online appendix with supplementary data. However, the data set only includes the variables analyzed in the original study.

> c Some of the subsidies took the form of rebates.

d Free provision was conditional on purchasing improved seeds.

> e GUK: Gram Unnayan Karma – local NGO providing a range of services to households in Bogra region, including microfinance, non-formal primary education, primary healthcare, and women’s empowerment activities.

> f We exclude households receiving only cash grants.

33
33

# Notes:

- 1/ Explicit deposit insurance scheme introduced since previous release of the deposit insurance database in 2004.

- 2/ Covered by the deposit insurance scheme of the United States (FDIC).

3/ Insurance product tailored to small retail depositors provided to private banks by a state-run insurance company. Several large banks, including Kanbawza and Co-operative Bank, have participated as of 2011.

4/ New Zealand introduced an opt-in retail deposit guarantee scheme in October 2008 and closed it in December 2010. Deposits held in New Zealand branches of Australian branches were covered under the Australian deposit insurance scheme from 2008 - 2010, but current legislation will limit coverage to Australian dollar-denominated deposits only.

- 5/ Bolivia has a bank resolution fund with funding provided by member banks, but no explicit deposit insurance.

6/ The Dominican Republic has no deposit insurance for commercial banks, but there is a scheme (established in 1962) insuring the savings and term deposits in savings and loan associations. In the past, the Central Bank has guaranteed deposits at Bancomercio (1996) and Baninter (2003) when these large banks failed.

7/ In 2009, Cameroon, Central African Rep., Chad, Congo (Rep), Equatorial Guinea, and Gabon, which share a regional central bank, established the Fonds de Garantie des Depots en Afrique Centrale (FOGADAC), a regional deposit insurance scheme that became operational in 2011.

8/ The Sri Lanka Deposit Insurance Scheme (SLDIS) became effective on January 1, 2012, although member banks and finance companies participating in this scheme already started contributing on a mandatory basis starting on October 1, 2010.

- 9/ Taiwan (ROC) has deposit insurance but is not an IMF member.

10/ A deposit guarantee fund (Fonds de Garantie des Dépôts) exists on the basis of the deposit guarantee law of 2008 but has not become operational yet as of end-2013. Sources: World Bank Survey, IADI, Laeven and Valencia (2012), FSB (2010, 2012), IMF staff reports, and national deposit insurance agencies.
# **5 Summary**

Given the dominance of the service sector in most economies, it makes sense that the applied input-output and CGE literature has moved away from an exclusive focus on goods. Much of this literature is focused on cross-section comparisons, benchmarking of openness and shifts in policy regimes, but also recently quantifying the behind the border linkages between services and goods. In this paper, we have taken advantage of recent data from the GTAP database (benchmarked to 2007) and have combined this with earlier releases. As a result, we have a dataset, with a common classification scheme, national input-output linkages, and cross-border linkages through trade for 29 countries and/or regions, and for 24 sectors. These data cover 1992, 1995, 1997, 2001, 2004, and 2007. They offer a unique opportunity to examine the value added linkages between services and goods, and the importance of services, through these linkages, for the total cost structure of traded goods and services. This panel approach offers the opportunity to extend recent work on the impact of service sector openness and cost structures on shifts in the pattern of production and trade, similar to the work of Inklaar, Timmer and van Ark (2008) and Triplett and Bosworth (2004), but with a ”beyond OECD” focus and with the opportunity to explore cross border linkages as a factor in recent productivity gains in services. We have done some basic data exploration here, but in our view there is great potential in refining and extending these data, and organizing the historic GTAP data series for panel-based analysis of value added trade, and within and cross-country linkages.

26
# **The Lives and Livelihoods of Syrian Refugees in the Middle East: Evidence from the 2015-16 Surveys of Syrian Refugees and Host Communities in Jordan, Lebanon, and Kurdistan, Iraq**

Nandini Krishnan<sup>1</sup> , Flavio Russo Riva<sup>2</sup> , Dhiraj Sharma<sup>1</sup> and Tara Vishwanath<sup>1 3</sup> JEL Classification: F22, O15 Keywords: Syrian refugees; Jordan; Lebanon; Iraq; Livelihoods

> 1 Poverty and Equity Global Practice, World Bank.

> 2 Fundação Getulio Vargas

> 3 Supported by Monica Biradavolu, Kevin Carey, Thomas Ginn, Jacob Goldston, Matthew Groh, Meghan Lucas, Daniel Masterson, Anna Newby, and Nethra Palaniswamy. The findings, interpretations and conclusions expressed herein are those of the authors and do not necessarily reflect the view of the World Bank Group, its Board of Directors or the governments they represent.
Due to budgetary limitations we omitted recording gram sabhas in Andhra Pradesh in round 1. In the other three states we randomly selected 4 blocks from Karnataka, 5 blocks from Kerala, and 6 blocks from Tamil Nadu, resulting in a total gram sabha sample of 38 villages. In round 2 we expanded the sample to include the state of Andhra Pradesh where we visited 18 villages in 6 blocks. In the other three states, in addition to the villages where we recorded gram sabhas in 2003 we sampled 10 more blocks resulting in an total sample of 131 gram sabhas in 97 villages. Out of these 131 visited gram sabhas, in 4 instances the village leaders did not allow the proceedings to be taped.

To explore the relationship between individual preferences and the topics discussed during the gram sabha we link the household data to the meeting transcript from the same village. In the villages where both rounds of meetings were recorded, each household is counted twice. Hence, our analysis is based on the subset of 2404 households located in villages where gram sabhas were recorded.

# 3.2 Methodology

We measure the extent to which a villager’s preferences are matched by the topics. To this end, we construct two individual level variables, a match dummy (MD) and a match intensity (MI). Let Tg = f(tkg)g the set of topics<sup>11</sup> mentioned at the meeting in village g, with each topic tkg being occupying a fraction fkg of the discussion. Let an individual i living in village of g have topic ti as her ...rst priority. Then the match dummy is de...ned as:

> 11 Note that all Tg are subsets of the universe of topics U = {water, roads, electricity, housing, health, education, employment, agricultural, liquor}.

12
17 Section 5: ESTIMATION OF THE INFLATION MODEL To empirically verify the presumptions on the link between inflation and relative price variability built on the theory as well as visual inspection of graphs and tables, I chose the model which appears in the 1996 paper by Coorey, Mecagni and Offerdal.<sup>2 0</sup> It is a simple static model for a two-sector economy (tradables and non-tradables). The economy is small and open and therefore takes world tradable prices as given. Additionally, money market is assumed to clear at all times. Using this basic set of assumptions authors derive seven structural equations describing market for nontradable goods, money market equilibrium, real income determination and inflation. The resultant model takes the following form<sup>21</sup> : _7c = YJ + Y2m + r3w + r4 (77VT - OT) +yv5 V,+ seasonals_ and takes account of:

- nominal money growth - _m_

- nominal wage growth - _w_

- real exchange rate based on relative inflation in tradable and non-tradable sector _7NT - 1r_

- i relative price variability - _V,_

- i quarterly seasonality - _seasonals_

The variables entering the model are based on the data from _Monthly Statistical Bulletins_ and _Bulletins on Monthly Price Changes_ of the _Polish Statistical Office_ (GUS) and _Monthly Bulletins_ of the _National Bank of Poland_ as well as _International Monetary Fund's International Financial Statistics._ They are defined as follows: _1)_ f _- CPI index;_ percentage changes of end-of-quarter value of the index

> 20 Coorey, Macagni and Offerdal [1996]

> 21For details see Coorey, Macagni and Offerdal [1996], pp. 73-78
between 21.2 and 21.5 (Desai et al. 2010). Therefore, women in our sample would have commenced childbearing, leading to a higher average household size than of the whole sample. This is borne out in table A2. In addition, the share of households with at least one male is higher than the share of households with at least one female, irrespective of age group in all survey rounds. This is because single migrants to urban areas tend to be men and women usually migrate after marriage. Table 2 shows only 16 percent of women (aged 25-55) with young children were in the labor market in 2011, compared to 23.6 percent of women without young children. The difference in LFPR of the two sets of women increased from 4.7 percentage points in 1983 to 7.5 percentage points in 2011.<sup>8,9</sup>

**Table 2. Labor Force Participation Rates for Married Women with and Without Children under Age 6 in Urban Areas, 1983–2011 (percent)**

||**1983**|**1993**|**2004**|**2007**|**2009**|**2011**|
|---|---|---|---|---|---|---|
|Female with no children under|||||||
|age 6|24.4|24.4|25.8|21.7|22.8|23.6|
|Female with at least one child|||||||
|underage 6|19.7|18.7|20.2|15.9|14.6|16.1|
|_difference_|_4.7_|_5.7_|_5.6_|_5.8_|_8.2_|_7.5_|

Source: Authors’ estimates from the NSS for urban women aged 25-55, who are wives of household head.

We expect a lower likelihood of employment for younger women, those belonging to nonSC/ST/OBC households and Muslim women. SC/ST women have been generally disadvantaged and are among the poorest; therefore, they are more likely to work compared to non-SC/ST women. They also tend to have weaker norms of male-female segregation (Das and Desai, 2003; Das 2006). Religion, however, plays a complex role and its negative effects on employment are stronger for Muslim women, due to a variety of reasons, of which lack of opportunities may be one (see Das 2005). We also add the husband’s education and employment type as a proxy for household income and socio-economic status.<sup>10</sup> Further, we account for household structure to bring in the presence of potential caregivers. In doing this, we include older females and males by age group, assuming that their presence in

> 8 The difference in LFPR between women with at least one young child and those without young children is statistically significant in all years.

> 9 We estimate the LFPR for women with children aged 6–7 and find that it is around 2–3 percentage points higher than the LFPR of women with children under age 6 in the period 2004–2011, whereas in the years before the difference was negligible.

> 10 The NSS surveys do not collect earnings information for self-employed workers (who constitute around half of employed persons) making it impossible to infer household income.

10
- 12 -

Table 1. **Female** Education, Family **Planning** Sevices, and Total FertlUty Rate

|Indepandent Variables|_________||Dprnden|Variable, TF|R. 198S||||
|---|---|---|---|---|---|---|---|---|
||(1)<br>Witbut hioat<br>Sec.Sob. Enr<br>1970|ion.<br>ollmmts<br>|(2)<br>With**interci**<br>See. Sob. Enr<br>1970|**on**<br>ollment<br>|(3)<br>Witdout inte<br>Sec. Sob.Er<br>19|mdon<br>olumet<br>75|(4)<br>With Intacti<br>Sec. ScbEnr<br>1975|on<br>ollmnso|
|________________|CoefficienT|<br>twdo|Coefficient|t.rato|Coofficient|:at-ido|Coeflicint|t.ratio|
|Co| t__|7.322|15.801"e0|7.199|14.35***|7.137|14.453*0|6.77|12.453*"0|
|F|emale<br>Seconday<br>Gross Eaollmet, 1970|.0.187|*3.723*|.0.177|-3.4S0_|||||
|Fenmie Secoadary<br>GrosaEnrollnent squard|0.002|2.1660|0.002|2.3250||_|||
|Male Secondary<br>GrossEnrollmn, 1970|0.038|0.996|0.029|0.709|||||
||MaleSecondaq<br>Grou Enamentsquared|0.0004|0.756|0.001|0.914||,|||
|Femule Secondacy<br>osEnrollent,197S|||||-0.116|-3.64400|0.100|-3.079*0|
|FemaleSeconday<br>Gross Enrolmnsquared|||||0.001|1.496|0.001|1.939*|
|Male Secondey<br>Gross<br>Eanollm,<br>1975|||||0.027|1.08|0.010|0.272|
|MaleSecondary<br>**Grou**Enaollment||||_|0.003|0.775|0.001|_1.39S_|
|GDP per capita, 1985|40.0001|*1.361|4.0002|-1.490|4.0001|4.653|4.0001|4.726|
|Faemilyplaig<br>servces,<br>19S2|<br>4.021|-3.900w*|4.014|-1.251|4.024|-4.6030'|4.010|4.695|
|Fem. Sec.saroil_en and<br>FP service|||4.0004|4.723|||4.001|-1.853*|
|Urbanpopulation<br>u<br>Softo<br>populadon, 1985|40.010|-1.040|4.100|4.871|4.009|4.963|4.006|4.613|
|Population per physiia,_1985_|-0.000004|4.576|4.000004|.0.497|4.000001|4.101|0.000001|0.07|
|AfricaRegional Dumy|0.229|0.741|0.282|0.835|0.302|0.931|0.515|1392|
|AsiRegional Dummy|49|**-**.<br>1.249|0.460|-1.125|4.561|-1.361|4.401|4.965|
|LACRegional Dummy|0.879|2.244"|0821|2.1470-|033|1.19S|0.2S2|0.886|
|R'|.8|2|.8|2|.8|3|.|84|
|a|6|4|6|4|6|5|6|5|

Absolute value of t-tios, wheo, *, and * indicate aspficanc lvels of 10,5, and I perent respectively.
for the region, as many African countries are in early stages of development. Cities in Africa are urbanizing fast, trying to lift the population out of poverty. Managing pollution and improving the business environment could help their firms improve their performance and will certainly help African cities become more competitive.

# References

- Adhvaryu, Achyuta, Namrata Kala, and Anant Nyshadham. 2014. "Management and shocks to worker productivity: evidence from air pollution exposure in an Indian garment factory." _Unpublished Working Paper, University of Michigan._

Begg, Iain. 1999. "Cities and Competitiveness." _Urban Studies_ 36 (5-6): 795-809.

Birdsall, Nancy, and David Wheeler. 1993. "Trade Policy and INdustrial Pollution in Latin America: where are the pollution havens?" _Journal of Environament & Development_ 2 (1): 137-149. Bishop, Russell. 2017. "Confronting Climate Change: Africa’s leadership on an increasingly urgent issue." In _Foresight Africa: Top priorities for the continent in 2017_ . Brookings Institution.

- Boltho, Andrea. 1996. "The Assessment: International Competitiveness." _Oxford Review of Economic Policy_ 12 (3): 1-16.

- Chang, Tom, Joshua Graff-Zivin, Tal Gross, and Matthew Neidell. 2016. "The effect of pollution on worker productivity: evidence from call-center workers in China 2016." _National Bureau of Economic Research_ (No. w22328).

- Chang, Tom, Joshua S. Graff-Zivin, Tal Gross, and Matthew J. Neidell. 2014. "Particulate pollution and the productivity of pear packers." _National Bureau of Economic Research_ (No. w19944. ).

- Clarke, George, Yue Li, and Lixin Colin Xu. 2016. "Business Environment, Economic Agglomeration, and Job Creation around the World." _Applied Economics_ 48 (33): 30883103.

- Clarke, George, Yue Li, and Lixin Colin Xu. 2017. "Business Environment, Economic Agglomeration, and Job Creation around the World." _Applied Economics_ 48 (33): 30883103.

25
||**India**|**Pakistan**|**Bangladesh**|**Sri Lanka**|**Nepal**|
|---|---|---|---|---|---|
|Share of Total Exports Representing Top 50<br>Products|62.5|99.0|98.0|90.0|80.0|
|Share of Competing Products in Total<br>Exports*|32.1|82.7|89.8|77.1|73.9|
|Share of Competing Products with RCA ›1|29.7|82.3|89.6|75.7|73.8|

Notes: *The number products exported at SITC 4-digit level matching the exports of at least one regional trading partner, as a share of total exports.

Source: WITS and SIMA databases, World Bank.

When comparison is made between those products in which RCA > 1, the conclusion above remain unchanged. More than 89 percent of Bangladesh’s globally competitive exports coincide with those of at least one regional partner. The extent of competition among Nepal, Pakistan, and Sri Lanka is marginally lower than that of Bangladesh. This characteristic in export profiles underscores the extent of complexity involved in creating an environment that encourages regional competition for those products with global efficiency. Only India offers any noteworthy share of efficient exports that are dissimilar to others in the region, and therefore offers the prospects for significantly expanding regional trade.

The lack of complementarity and high degree of competition in export structures imply daunting prospects for expanding regional trade in South Asia. It seems that general liberalization efforts, which helped countries to increase efficiency, have evolved among a narrow range of products largely dominated by textiles and apparel. Ironically, these are precisely the products for which the countries have demonstrated a reluctance to totally dismantle existing trade barriers. For instance, competition between Bangladesh, Pakistan and Sri Lanka seems to emerge primarily in textiles and apparel exports, which occupy the major share in each of their comparative advantages.<sup>35</sup> With the exception of its FTA agreement with Sri Lanka, through which India provides market access through quotas, India nevertheless remains vehemently opposed to liberalization of textiles and apparel under any of its regional agreements.

> 35 Note, however, that product differentiation can provide for complementarities even in textile and apparel., paving the way for mutually beneficial trade. This can only be established at a further disaggregation, for example at HS 6-digit level, which is beyond the scope of the current study.

41
Financial Regulation and Government Revenue Table A2: Regulation and Compliance: Monthly Variation (Million Real Birr)

|Variable||NBE|Bills (Real Bi|rr)||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Lending _×_ Policy|0.270***|0.224***|0.232***|0.232***|0.231***|
||(0.00845)|(0.0112)|(0.0103)|(0.0104)|(0.0104)|
|Policy|15.59|28.54*|-19.98|-63.86**|128.8**|
||(15.18)|(16.44)|(24.67)|(28.90)|(64.06)|
|Lending|-0.0158***|0.250***|0.142***|0.139***|0.142***|
||(0.00407)|(0.0352)|(0.0405)|(0.0432)|(0.0425)|
|Bank FE||Yes|Yes|Yes|Yes|
|Year FE|||Yes|Yes||
|Month FE||||Yes||
|Non-Param. Month FE|||||Yes|
|Observations|538|538|538|538|538|
|Number of banks|14|14|14|14|14|
|Adjusted _R_<sup>2</sup>|0.890|0.957|0.961|0.961|0.960|
|Mean Dep. Var.|501.9|501.9|501.9|501.9|501.9|
|SD Dep. Var.|616.6|616.6|616.6|616.6|616.6|

_Note:_ This table reports OLS estimates. The unit of observation is bank level. Robust standard errors are in parentheses. NBE Bills is the amount of bills issued by the NBE in million birr, and it is a continuous variable. Its mean and standard deviation are reported in the final two rows. The quantity of NBE Bills is regressed on “Lending _×_ Policy”, which is the interaction between Private Lending (in million birr) and a continuous variable, and the Policy dummy, taking unit value from April 2011 onward. We also report the variable Lending and Policy per se. The null hypothesis is that this coefficient is 0.27, as prescribed by the NBE directive, and we cannot reject this. In column (1) we exploit all variation, and in column (2) only within-bank by introducing bank fixed effects. In columns (3) and (4) we also add year and month FE, respectively. In column (5) we remove parametric month and year FE and replace these with non-parametric effects for each month of each year (non-param. month FE). The adjusted _R_<sup>2</sup> of these regressions is also reported. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

23
<!-- Start of picture text --> Percentage change of GDP with baseline<br>6r<br>1 ---- - - .<br>Tra a ct oExp. 1.2<br>Exp. 1.3<br>Fiur 4. Chnei D nExp ermn 1.1-1.<br><!-- End of picture text --> <!-- Start of picture text --> 0 . 0 - - - - -<br><!-- End of picture text --> <!-- Start of picture text -->
- - - - - - - - - - - - - - - - - -<br><!-- End of picture text -->

Figure 4. Chanlge in GDP in experiment 1.1-1.3 L__---- _L_ _______-____ __-030%...

0.20 Transitional cost of phasing out pillar 2 - Simulaion **result from Exp. 2.1** 0~~~~~~~~~~~~~~~~~~~~~~~~~~~1 <!-- Start of picture text --> 10 Bn. Yuan<br>0.70% r 45<br>U.b0 '-- -- ' 40<br>0.50% - -------<br>- - - Ratio to GD)P 30.<br>040% 0 t } Injectedfiscalfunds i/ 25<br>0.300/ *,1 20<br>%~~~~~~~~~~~~~~~~~~~~~~~~ 15<br>0.20%<br>, u ~~~~~~~~~~~~~~~~~~~~~~~10<br>0.10% 5<br>0.00% 0<br><!-- End of picture text --> Figure 5. Transition cost of phasing out notional individual accounts 37
many of the non-income MDGs are unlikely to be achieved by broad groups of countries; in fact, only a handful of low-income countries have met or are likely to meet the MDGs for under-five mortality, access to safe drinking water, and access to sanitation (Figure 7(c) and (d)). These countries need development policies that address the structural bottlenecks for successfully accelerating economic growth and moving toward achieving the MDGs.

**Figure 7(a) – (d):  Progress Towards Meeting the MDGs**

<!-- Start of picture text --> (a) Population living on less than $1.25 a day, % (b) Number of Countries by Progress Status for MDG 1<br>70 50<br>45<br>60<br>40<br>35<br>50<br>30<br>40 25<br>20<br>30<br>15<br>X<br>20 SSA Target 10<br>5<br>Total<br>10 0<br>SSA On Target Close to Being On Far Behind the<br>0<br>Target Target<br>1990 2005 2015<br>(c) Number of LIC s that have achieved the goal (d) Number of LIC s that are on track to achieve the goal*<br>10<br>6<br>9<br>8 5<br>7<br>4<br>6<br>5 3<br>4 2<br>3<br>2 1<br>1 0<br>0<br>Under-five mortality Access to safe Access to sanitation<br>Under-five Access to safe Access to drinking water<br>mortality drinking water sanitation<br><!-- End of picture text --> Source: _Global Monitoring Report_ 2011. Figures c) and (d) compiled based on countries listed in Table 1.1.

*The low-income countries ―on track‖ are additional to the countries that have achieved the goal, so for example, the number of countries that have either achieved the under-five mortality goal OR are on track is 6 (1 has achieved and 5 are on track).

12