rafmacalaba/datause-encoder-data huggingface dataset:
The JDY has yielded impressive results: near the end of 2016, about 250 million accounts have been opened under the policy, with more than 78 percent of these accounts issued by public sector banks.

We find that 21 percent of adults (32 percent of adults who ever applied for an account) applied for a JDY account, and the majority of these accounts were opened in the last 12 months. However, since we can only identify accounts opened during two periods—accounts opened within the past year and prior to the past year—we can only compare traditional and JDY account openings within the last 12 months, i.e. any comparison of accounts opened prior to the past year would include a substantial period where JDY accounts did not exist. Since we cannot include accounts opened during the first 6-months of the JDY scheme, where as many as 130 million accounts where opened,<sup>14</sup> it would be unfair to compare individual characteristics of JDY and traditional account holders from the full sample. However, in the following sections we compare the cost and efficiency of opening JDY accounts, as compared to traditional accounts, in the last 12 months.

We explore whether there are demographic differences between adults who applied for an account within the past year, as compared to adults who applied for an account before JDY accounts were available (Table 2).

Our results suggest that in the last 12 months, groups with traditionally excluded individual characteristics have applied in larger numbers than groups that would otherwise fare better. During this period, women and the poor were about 5 percentage points more likely to apply than men and wealthier adults. Applications were 10 percentage points higher among illiterate adults compared with those who have studied beyond primary school. In the last 12 months, single adults were more likely to apply for an account than married adults, by 7 percentage points. Young adults were more likely to apply as compared to older adults; we find that fewer adults older than 62 or above applied for an account in the last 12 months.

> 14 Official Pradhan Mantri Jan Dhan Yojana website.

11
# **Appendix 2: Modules of public DMS and one commercial software**

# **<u>CS-DRMS</u>**<sup>**17**</sup>

# **Evolution and Versions**

CS-DRMS was first launched in 1985. Following the French version released in 1994, 3 main versions of CS-DRMS (2000+, version 1.3 and version 2.0) have subsequently been released. The current version of CS-DRMS is Version 2.3.

# **Cost of installation and maintenance**

# Commonwealth Countries:

- Cost of Installation: Free of charge

- Maintenance Fee - £1,500 per annum

# Non-Commonwealth Countries:

- Cost of Installation (ranges between £24,000 for Low-Income Countries to £35,000 for High

- Income Countries)

- Maintenance Fee - £1,500 per annum

|**Modules (back-**<br>|**office solution)**<br>|
|---|---|
|**Loans**|Record a variety of financing instruments from various external or domestic creditor<br>sources, - (Multilateral, Bilateral and Commercial).|
|**Securities**|Record a wide range of domestic and external debt securities - Bonds (Zero and Fixed<br>Coupon, Floating Rate, Indexed), T-Bills, Promissory Notes, and Commercial Papers)|
|**Management**|Assess the cost of new borrowing, and perform “what if analysis” on their debt portfolio|
|**Tools**|to check effects of debt restructuring. Sensitivity tests can also be performed on interest<br>rates, exchange rates and exogenous economic variables under different scenarios, and the<br>existing debt portfolio analyzed to identify ‘high’ cost loans for LMO.|
|**Disbursements**<br>**and Projects**|Disbursement monitoring based on different disbursement methods (reimbursement,<br>advance, direct payment, etc.). The system captures the chart of accounts and incorporates<br>a detailed transaction-modelling feature, which allows for the recording of the various<br>events throughout the individual transactions lifecycle. These features provide the data<br>required for financial accounting of debt-related data.|
|**Portfolios**|Used to manage borrowing, guarantee and lending portfolios of Central Government<br>Debt, Public enterprise, Private Sector External Debt, Sub-National Debt.|
|**Reporting**|Over 100 standard pre-formatted reports, user customized reports (report writer, aggregate<br>report wizard, dynamic data query tool) and a dashboard with over 15 data summaries.|

> 17 Provided by Commonwealth Secretariat.

36
To compare the 2009 results with those of 1999, we used the 1999 SAE poverty rates from Minot et al. (2003).<sup>2</sup> These are based on the 1998 VLSS and a 33% sample of the 1999 VPHC. The survey is implemented by GSO with funding from the Swedish International Development Agency and UNDP and with technical assistance of the World Bank. It includes 6,000 households. The 33% sample of the 1999 census includes 5,553,811 households.

# **4. The 2009 poverty map**

# **_4.1 Consumption models_**

The first step in the poverty mapping method is to select common explanatory variables in the census and household survey. After carefully screening the questionnaires and examining the data (comparing summary statistics), we have 20 household variables (Table A.1 in Appendix). We also constructed commune level data from the census and merged these variables with the household survey. For example, we constructed the percentage of Kinh people per commune, the average household size of communes, the proportion of households per commune having motorbike, etc. Note that these variables are comparable across the census and survey by construction. The total number of explanatory variables for estimation of the expenditure model is 39.

We estimate six separate regressions of log of per capita expenditure for six regions. To allow for the difference in coefficients between urban and rural areas, we interact the urban variable with all the remaining explanatory variables.

A forward stepwise technique is used so that only variables which are significant at least at the 5% level are kept. We select explanatory variables that are robust in explaining expenditure. This means that these variables have unchanged signs and are significant when the models are changing. The regression results of the large models are presented in the tables in Appendix I. The R-squared is very high. The Mekong River Delta has the lowest R-squared

> 2 In Section 6 we note that caution must be exercised in comparing poverty between 1999 and 2009 as the underlying household survey data are not strictly comparable.

8
|**Table 6B: Estimates of elastic**|**ity of firms with respect topr**<br>**R&D intensity**|**ice foer Tanzania by sector an**<br>|**d by Tanzanian trading partner**<br>|
|---|---|---|---|
||di diidd b|**Elasticity Estim**|**tes**|
||R&D expentures ve y<br>|||
||sales(times 1000)for the US*|**Africa**<br>**EU**|**ROW**|
|**SERVICES**||||
|telecommunications|52-high|2.5<br>13.4|20|
|banking|4-low|3.3<br>3.3|10|
|insurance|4-low|3.3<br>3.3|10|
|professional services|116-high|2.5<br>13.4|20|
|air transport**|medium|1.9<br>10|15|
|road transport|low|3.3<br>3.3|10|
|rail transport**|medium|1.9<br>10|15|
|water transport**|medium|1.9<br>10|15|
|**MANUFACTURING**||||
|miningandquarrying|0-low|3.3<br>3.3|10|
|beverages and tabacco|14-low|3.3<br>3.3|10|
|grain milling***|7-low|3.3<br>3.3|10|
|processed food|7-low|3.3<br>3.3|10|
|textiles and apparel|13-low|3.3<br>3.3|10|
|petroleum refineries|2-low|3.3<br>3.3|10|
|chemicals|34-medium|1.9<br>10|15|
|fertilizers and pesticides***|34-medium|1.9<br>10|15|
|iron, steel and metal products|7-low|3.3<br>3.3|10|
|glass and cement|0-17-low|3.3<br>3.3|10|
|*Based on average R&D expenditures s f<br>**We evaluate transportation as a mediu<br>These are transportation, chemiicals and<br>multinationals operatingi in the US was<br>TechnologyLinkages," at 'http://www.n|or theyears 2004 and 2005. The averag<br>m R&D sector since three sectrors dom<br>computers and electronics. Moreover,<br>performed in the same three sectors. See<br>sf.gov/statistics/seind04/c4/c4s5.htm.|e for all US industries was 36. See table<br>inate R&D expenditures of US multinati<br>about two-thirds of all R&D expenditure<br>"U.S. and International Research and D|E1 for details.<br>onals operating abroad.<br>s of foreign<br>evelopment: Funds and|
|**SOURCE**: R&D and sales data from N<br>_Development: 2005, Data Tables_. Avai<br>***Food is the proxy for grain mlling; ch<br>steel and metalproducts; forglass and ce|ational Science Foundation, Division of<br>lable at: http://www.nsf.gov/statistics/n<br>emicals is the proxy for fertilizers and<br>ment we use an average ofplastics, rub|Science Resources Statistics,_Survey of I_<br>sf10319/content.cfm?pub_id=3750&id<br>pesticides; primary and fabricated metals<br>ber, mineral and woodproducts.|_ndustrial Research and_<br>=3.<br>are the proxies for iron,|

74
<mark>Sarsons, H., 2015. Rainfall and conflict: A cautionary tale.</mark> _<mark>Journal of development Economics</mark>_ <mark>,</mark> _<mark>115</mark>_ <mark>, pp.6272.</mark> <mark>Siegle, J. et al., 2011, 'Africa and the Arab Spring: A New Era of Democratic Expectations', Special Report, Africa Center for Strategic Studies, Washington DC</mark> <mark>Stanbury, P. and Lynott, J., 1992. Irrigation management and conflict resolution. In</mark> _<mark>Inequality and the Commons, the Third Biennial Conference of the International Association for the Study of Common Property</mark>_ <mark>(pp. 1-26).</mark> <mark>Sumner, A., Hoy, C. and Ortiz-Juarez, E., 2020. Estimates of the Impact of COVID-19 on Global Poverty.</mark> _<mark>UNU-WIDER, April</mark>_ <mark>, pp.800-9.</mark> <mark>OECD/SWAC, 2014, An Atlas of the Sahara-Sahel: Geography, Economics and Security, West African Studies, OECD Publishing. http://dx.doi.org/10.1787/9789264222359-en</mark> <mark>Van den Brink, R., Bromley, D.W. and Chavas, J.P., 1995. The economics of Cain and Abel: Agro-pastoral property rights in the Sahel.</mark> _<mark>The Journal of Development Studies</mark>_ <mark>,</mark> _<mark>31</mark>_ <mark>(3), pp.373-399.</mark> <mark>Vanden Eynde, O., 2018. Targets of violence: Evidence from india’s naxalite conflict.</mark> _<mark>The Economic Journal</mark>_ <mark>,</mark> _<mark>128</mark>_ <mark>(609), pp.887-916.</mark> <mark>Woolcock, M., 2014. Engaging with fragile and conflict-affected states.</mark> <mark>World Bank, 2020. Fragility and Conflict : On the Front Lines of Fight Against Poverty, World Bank. Available at :</mark> <u>https://openknowledge.worldbank.org/bitstream/handle/10986/33324/9781464815409.pdf</u> 17
employment were the next major category of shocks. About 2% of the respondents reported very bad living conditions because of family problems and 1% because of jail sentences or lawsuits.

One of the difficulties with measuring the events that have happened in order to assess the impact of the shock is that these events are usually self-reported by respondents. The problem, then, is that we may not have a measure of the event itself but rather a report of the event _conditional_ on being a significant negative economic shock from the perspective of the household. Thus, shocks reported by households will be endogenous, resulting in an upward bias in the impact of the event if more salient events are those whose socio-economic impact is larger.

Using information on the survival status of our sample of baseline respondents, as well as the panel respondents with whom they resided when they died between the baseline and 2004 rounds, we will measure deaths not based on the panel respondent identification of the shock but from the actual event itself.<sup>2</sup> However, for understanding how an adult death may be correlated with other negative events, we will also use the household reports of agricultural shocks with the caveat above.

Table 2 shows the distribution of the occurrence of a death in the household (categorized by year and sex of the deceased) for the 2,611 households with full information, used in the analysis. These are the households interviewed in 2004 with at least one member who was included in the baseline data for 1991-94. In total 961 individuals we had interviewed in 1991 died before 2004. This study restricts the deaths considered to those individuals who were aged between 20 and 55 at the time of their death, included in the baseline data, and were living with at least one other panel respondent at the time of their death. The data show that these

> 2 Household questionnaires that measure adult death through explicit questions on recent mortality episodes rather than a “shock” module in the household questionnaire will also avoid this issue. Other data which can also be used include information on events from a community questionnaire or rainfall data, both of which result in less variation as they measure covariate events.

11
15

# **_<u>Fig. 6: Proportion of total household deposits in the banks participating in the deposit</u> insurance system (as of 01/07/2005)_**

<!-- Start of picture text --> 100% 1.5<br>75%<br>60.9<br>50% 98.5<br>25%<br>39.1<br>0%<br>by number of deposits by volume of deposits<br>deposits low er than 100,000 deposits higher than 100,000<br>Source: DIA<br><!-- End of picture text -->

# **VI. CONCLUSIONS AND POLICY RECOMMENDATIONS**

World Bank research suggests that the adoption of an explicit deposit insurance system can have a positive impact on financial sector stability and help create a more level playing field, but it is not a silver bullet. Deposit insurance should not be considered in isolation of other key reforms that must be undertaken to avoid the potentially negative effects resulting from the change in incentives that derive from adopting a DIS. The Russian experience provides an illustration of these findings. Whereas some immediate and possibly temporary benefits can be attributed to the new DIS in terms of containing the underlying structural and solvency problems is some deposit-taking institutions, it is also potentially costly in terms of significant new fiscal contingencies. There is a window of opportunity to strengthen the legal and institutional framework to mitigate the potential costs and avoid future instability that could discredit the DIS. Additional policy reforms are required, in particular in the following areas:

**_(a) Enforcing prudential regulation._** The vigorous enforcement of CBR-enhanced prudential standards—including the adoption of: stricter fit and proper tests for owners, directors and managers of financial institutions, stricter corporate governance rules (see below), and timely and more complete disclosure, as well as the adoption of consolidated and risk-based supervision and IT audits—and a progressive increase in solvency requirements (including minimum capital requirements) are important disciplining measures that are critical for the future stability of the Russian banking system. In this respect, the current limit of 25 percent of capital established on large exposures is
10

|**Table 4. Macro-estimated returns to one additional ye**|**ar of schooling**|
|---|---|
|Effect|Source|
|No significant effect of years of schooling on economic growth|Benhabib and<br>Spiegel(1994)|
|Each additional year of schooling attainment in a country is<br>associatedwithabout 30%higherGDPpercapita|Heckman and<br>Klenow(1997)|
|A one-year increase in the average years of schooling of the<br>labor forceraises output per workerbetween5% and15%|Topel (1999)|
|A one year increase of years of schooling associated with<br>0.30% peryear fastergrowth|Bils and Klenow<br>(2000)|
|Macro-estimated rate of return to schooling between 18% and<br>30%|Krueger and Lindahl<br>(2001)|
|A one-year increase in average education raises per capita<br>income between3% to 6%|Bassanini and<br>Scarpetta (2001)|
|A one year increase in the mean years of schooling is<br>associated with a rise in per capita income by 3%-6%, or a<br>higher growth rate of one percentage point|Sianesi and van<br>Reenen (2003)|
|No evidence of wide social returns to education based on<br>cross-county regressions|Pritchett (2006)|
|Macro-estimated rate of return to schooling 27%|de la Fuente and<br>Domenech (2006)|
|Macro-estimated rate of return to schooling between 9.0 and<br>12.3%|Cohen and Soto<br>(2007)|
|Macro returns to years of schooling 36.9%, or each year of<br>schooling is statistically significantly associated with a long-run<br>growth rate thatis 0.58 percentage pointshigher|Hanushek and<br>Woessmann (2008)|
|Controlling for physical capital stock, the rate of return to the<br>average year of schooling is 12.1%|Barro and Lee (2010)|

Such a function has also been applied in a macro context where _Y_ refers per capita income or output per worker in country _i_ and _S_ to the mean years of schooling of the population or the work force. The enrolment ratio at different levels of education has also been used in lieu of years of schooling as an independent variable. Table 4 gives typical results of application of such function fitted to pooled cross-country-time data.

Another methodological line of estimating the contribution of education to economic growth has been instigated by the work of Solow (1956, 1957) who added technical change ( _T_ ) as an independent variable in an aggregate production function, along with the traditional factors of production physical capital ( _Kp_ ) and labor input ( _L_ ) in order to explain output ( _Y_ ). Omitting time subscripts for expository simplicity, the Solow function takes the form (3) _Y = f (Kp, L, T)_ Schultz (1961a,b) challenged Solow‘s formulation and added education as a determining factor of income. A stream of empirical research followed along two alternative measures of the education input: "Schultz-type" and "Denison-type" growth accounting.
# **Recall Length and Measurement Error in Agricultural Surveys**

Philip Wollburg<sup>a</sup> Marco Tiberti<sup>a</sup> Alberto Zezza<sup>a</sup>

## **JEL classifications: Q12, Q18, O12, C81, C83**

## **Keywords: agriculture, measurement error, recall, survey design**

a Development Data Group, The World Bank. The authors would like to thank for their comments Chris Barrett (Cornell University), Isis Gaddis (World Bank and IZA), Lakshman Nagraj Rao (Asian Development Bank), Gero Carletto (World Bank), Aparajita Goyal (World Bank), Sydney Gourlay (World Bank), Talip Kilic (World Bank), Heather Moylan (World Bank), Ilaria Lanzoni (World Bank), and seminar participants at the 2019 AIEAA Conference and the Joint UNU-MERIT/School of Governance Seminar in November 2019. This analysis was supported by Trust Fund -TF0B1463.
# **1. Introduction**

Globally, the number of forcibly displaced people is unprecedented. At the end of 2020, the number of people forced to flee had reached 82 million, including 26 million refugees and 48 million internally displaced people (IDPs)<sup>1</sup> (UN High Commissioner for Refugees [UNHCR, 2021]).<sup>2</sup> Forcibly displaced women and men face specific barriers to taking up economic opportunities and accessing services compared to their host communities, which create higher risks of being trapped in poverty than many of their hosts (World Bank, 2017). Qualitative research underlines that women and men experience and respond differently to forced displacement (Forced Migration Review, 2018). However, little is known about how gender inequality influences poverty rates of forcibly displaced people, largely due to the scarcity of representative data adequate to investigate these differences.

We use the 2017-18 Somalia High-Frequency Survey to examine the role of gender-based disadvantage in poverty rates experienced by IDPs and non-IDPs. Three metrics of gender disadvantage are investigated: first, we use the traditional approach to measuring the impact of gender on poverty, via household headship; second, we classify households according to their demographic characteristics; and third, by the number and sex of earners in the household. To our knowledge, this is the first study to go beyond headship in analyzing the gender dimensions of poverty in situations of internal displacement.

> <u>[†]</u> Lead Economist, Gender Group, World Bank Group.

> <u>[‡]</u> Economist, Poverty and Equity Global Practice, World Bank Group.

> <u>[§]</u> Economist, International American Development Bank, Washington, D.C.

> 1 Internally displaced persons (IDPs), are “persons or groups of persons who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or human-made disasters, and who have not crossed an internationally recognized state border” (UN 1998 Guiding Principles on Internal Displacement E/CN.4/1998/53/Add.2 11 February 1998)

> 2 Forcibly displaced people include refugees, internally displaced people, and asylum seekers (see <u>https://migrationdataportal.org/themes/forced-migration-or-displacement#definitions)</u>

1
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1 if the country has laws mandating paid or unpaid paternity leave and 0 otherwise ( _Paternity_

# _Leave_

Last, the ES data we use were collected in different years across countries between 2007 and 2016. This raises the possibility that global factors that vary over time could be affect women’s employment. If these factors also vary systematically with maternity leave across countries, our main results could be spuriously affected. Thus, we control for the year the ES was conducted in the country ( _Trend_ ).<sup>7</sup> One variable that we did not mention above is the fertility rate. Higher fertility rates are known to adversely affect women’s labor force participation rates; at the same time, maternity leave policies may be systematically correlated with the fertility rate. In our sample, fertility rate is very highly correlated with GDP per capita (correlation of -0.78). Thus, we expect that the effect of fertility rate on female workers is already accounted for via the GDP per capita control. Nevertheless, we experimented with controlling for fertility rate in all the specifications discussed below.<sup>8</sup> However, controlling for fertility rate and even its interaction with maternity leave made no qualitative difference to any of the results discussed below.

# **3. Main empirical results**

All the regression results discussed below are based on the Ordinary Least Squares regression method. Sampling weights are used throughout to ensure that the results can be extrapolated to the targeted universe of the private economy.<sup>9</sup>

> 7 Our main results do not change much if we control for year dummies (year fixed effects) instead of the year trend. 8 Data source for fertility rate is WDI, World Bank. As for GDP per capita and other macro‐level controls, we use average values of fertility rate taken over 5 years prior the year the ES was conducted in the country. Results controlling for fertility rate are available on request from the authors.

> 9 Sampling weights are normalized so they sum to unity across all firms within a given country. Thus, all countries have equal weightage.

15