rafmacalaba/datause-encoder-data huggingface dataset:
- 30 -

**TABLE 8: Willingness to Pay to Reduce Travel Time to Secondary School In Rural Peru: June 1985 Intis per year and Percent of Income**

||||I|NCOME Q|UARTIL|E|||||
|---|---|---|---|---|---|---|---|---|---|---|
|Travel Time to<br>Nearest<br>School|<br>Poore<br>Intis|st 25%<br>% of<br>Income|Next<br>Intis|25%<br>xof<br>Income|Next<br>Intis|25%<br>X of<br>Income|Ri,<br>Intis|ast 25%<br>% of<br> <br>Income|All<br>Gr<br>Intis|Income<br>oups<br>Z of<br>Income|
|1 Hour|169|2.3%|182|1.4%|203|1.0%|374|<br>0.8%|232|<br>1.1%|
|2 Hours|412|5.7%|455|3.5%|508|2.A%|934|<br>1.9%|577|<br>2.8%|

# E. Food Stamps, Food Subsidies and the Poor in Jamaica

One issue that often arises in poverty alleviation efforts is: To what extent do a program's benefits reach the poor? This is often referred to as the targeting issue. Food subsidies have often come under attack because they seem to benefit the wealthier income groups more than they do the poor. Some kind of food stamp scheme is often put forth as a more effective way of channeling assistance to the poor. The Jamaica Survey of Living Conditions allows one to compare the two different programs since it collects information on boch food stamps and the consumption of subsidized food items. In a report recently put out by the Statistical Institute of Jamaica, the beneficiaries of these two programs were identified according to per capita expenditure levels. The results are reproduced in Table 9.

It turns out that although the value of food subsidies in Jamaica is a larger portion of the budgets of poor households than of rich households, in money terms wealthier households receive more benefits than poorer ones; the
# **<u>Figure 6 continued</u>**

# (b) By agricultural and non-agricultural sources of income

<!-- Start of picture text --> 100%<br>8 5<br>17<br>19 25<br>80%<br>60% 43<br>44 33<br>40%<br>22<br>16 28<br>20%<br>17 20<br>10<br>0% ‐2<br>National Urban Rural<br>Remittances Agricultural income<br>-20% Non-agricultural income Household demographics*<br>Other factors**<br><!-- End of picture text -->

* Household demographics are a sum of the share of adults, the share of workers, the share of agricultural workers, and the share of non-agricultural workers out of total number of a household’s members **Other factors include <u>consumption-to-income ratio and capital income.</u> _<u>Source</u>_ <u>: World Bank staff calculations based on data from Nepal Living Standards Survey 1995 and 2010</u>

An important caveat in this analysis is that one cannot attribute causality of the results of the decomposition results on poverty reduction. For example, an increase in remittances (as a result of a household member migrating abroad) could simultaneously lower labor income for the rest of the household members if other household members do not participate in the labor market. A growth in labor income driven by higher labor market participation might make the household ineligible for other public transfer programs. Accordingly it would be incorrect to interpret the changes in labor income or remittances as strictly “causing” the observed poverty reduction. Nevertheless, this accounting exercise is useful in identifying empirical regularities and focusing attention on factors that are quantitatively more important in describing distributional changes. The rest of the section attempts to put into perspective magnitudes of changes in the three identified main drivers of poverty reduction: remittances, labor income, and demographic factors.

## **_Migration and remittances_**

Nepal has experienced a drastic increase in remittances received from abroad since the late 1990s (World Bank 2006; World Bank 2013). While work-related emigration is not a new phenomenon in Nepal, what characterizes Nepal’s emigration pattern in recent years is the massive outflow of labor force to destinations other than India, which had been by far the most popular destination for Nepali migrant workers since the beginning of the 19<sup>th</sup> century (Seddon 2005). Based on data from 14
# **Table 6: The Determinants of Greenfield FDI in the MENA region, Q1 2003 – Q4 2012**

# **Estimations by Broad Industry and Source Country Grouping**

**The Determinants of Greenfield FDI in the MENA region, Q1 2003 – Q4 2012** _Dependent Variable: Log GFI inflows (in millions of USD)  by broad sector and source_ LSDVC Estimates

|_Sector_|Resources a|nd Energy|Non-resource M|anufacturing|
|---|---|---|---|---|
|_Source Countries_|Strong<br>Institutions|Other|Strong<br>Institutions|Other|
||(1)|(2)|(3)|(4)|
|Political Instabilityt-1|-0.19|0.09|-0.91**|-1.23***|
||(0.45)|(0.42)|(0.36)|(0.41)|
|Economic Controls|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Observations|604|604|604|604|
|Number of Countries|17|17|17|17|
|_Sector_|Tradable|Services|Non-tra|dables|
|_Source Countries_|Strong|Other|Strong|Other|
||Institutions||Institutions||
||(5)|(6)|(7)|(8)|
|Political Instabilityt-1|-0.73**|-0.79**|-0.20|-0.45|
||(0.32)|(0.40)|(0.35)|(0.43)|
|Economic Controls|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Observations|604|604|604|604|
|Number of Counties|17|17|17|17|

Bootstrapped standard errors in parentheses ***p<0.01, ** p<0.05,* p<0.10. Note: Other= MENA+Rest of the World 31
Van den Berg, G., C. Dauth, P. Homrighausen, and G. Stephan. 2018. Informing employees in small and medium sized firms about training: results of a randomized field experiment. IZA Discussion Paper Series, no. 11963.

- van der Horst, M. 2019. Internalised Ageism and Self-Exclusion: Does Feeling Old and Health Pessimism Make Individuals Want to Retire Early? Social Inclusion, 2019, Volume 7, Issue 3, Pages 27–43.

- Veríssimo, J., P. Verhaeghen, N. Goldman et al. 2022. Evidence that ageing yields improvements as well as declines across attention and executive functions. Nat Hum Behav 6: 97–110.

- Vickerstaff , S. and M. Van der Horst. 2021. The Impact of Age Stereotypes and Age Norms on Employees’ Retirement Choices: A Neglected Aspect of Research on Extended Working Lives. Front. Sociol., 01.

- Vodopivec, M., D. Finn, S. Laporšek, M. Vodopivec, and N. Cvörnjek. Increasing employment of older workers: Addressing labour market obstacles.” Journal of Population Ageing, 12: 273–298.

WHO 2021. Global Report on Ageism. Geneva: World Health Organization.

World Bank. 2016. Live Long and Prosper: Aging in East Asia and Pacific. World Bank: Washington, D.C.

- World Bank. 2017. Why Should We Care About Care? The Role of Childcare and Eldercare in Armenia. World Bank: Washington, D.C.

World Bank. 2019. The Changing Nature of Work. World Bank: Washington, D.C.

World Bank. 2020. A Silver Lining: Productive and Inclusive Aging for Malaysia. World Bank: Washington, D.C.

World Bank. 2021a. Vietnam - Adapting to an Aging Society. World Bank: Washington, D.C.

- World Bank. 2021b. Aging and the Labor Market in Thailand : Labor Markets and Social Policy in a Rapidly Transforming and Aging Thailand. World Bank: Washington, D.C.

- World Bank. 2022a. Georgia Human Capital Review. World Bank: Washington, D.C.

- World Bank. 2022b. Charting a Course Towards Universal Social Protection: Resilience, Equity, and Opportunity for All. World Bank: Washington, D.C.

- Zwick, T. 2009. Why Pay Seniority Wages? ZEW - Centre for European Economic Research Discussion Paper No. 09-005. Retrieved at SSRN: http://dx.doi.org/10.2139/ssrn.1394351

- Zwick, T. 2011. Why Training Older Employees is Less Effective. ZEW - Centre for European Economic Research Discussion Paper No. 11-046. Retrieved at SSRN: http://dx.doi.org/10.2139/ssrn.1886428

39
and Assessment project (NAFORMA) is being established to monitor carbon changes. An FAO forest inventory is being used to set up the baseline while satellite imagining, biomass measurement and GPS are being used for verification in the field and for filling any gaps in the forest inventory. The government has also worked closely with NGOs to develop MRV protocols that will be taken to the villages where local people will be trained to use them.

National level teams also perform visits at the district and village levels to monitor forest conservation; but it is clear that local governments and NGOs have a very important role in REDD+ monitoring. In fact, the local communities essentially perform the monitoring tasks with random supervision of other parties. Local governments and NGOs work closely with the village leaders and environmental committees.

NGOs often use satellite imaging and remote sensing technology to set up a baseline for the village forest. NGOs normally have a station in the vicinity of the villages and use local monitoring of trees through their personnel working closely with the communities. Thus, monitoring is performed at a small scale according to a list of indicators/outcomes for specific activities. NGOs use project managers at the village level to report the outcomes through emails/phone communication and scheduled visits. The monitoring visits are random and every year there is monitoring and evaluation of each project relative to pre-established outputs.

The local government monitors forest conservation by keeping records of permits and also working closely with village committees. The environmental committee acts as the authority at the village level and keeps records and collection of licenses. The process of monitoring forest conservation from the point of view of the local government is as follows. The village patrols the forest and if somebody is found illegally harvesting, the patrol fills out a form reporting illegal activities and issues a fine. A copy of the report it is sent to the district office that keeps a record of it. The local government also uses some funds from the central and district governments to assist in remote patrolling through collective 21
Laeven, 2004 and Turk-Assis, 2009). Due to lack of data on barriers to exit for several countries, we only focus on bank entry. Specifically, _Contestability_ is a matrix which includes the following variables: (i) initial capital required for a bank to start operations, (ii) the number of requirements that have to be satisfied by institutions applying for a banking license, and (iii) the percentage of applications for licenses that have been denied. These data come from the World Bank 2007 Survey on Bank Regulation and Supervision.<sup>9</sup> Columns 3 to 5 of Table 7 compare the mentioned entry requirements in MENA with those of other regions. We find that with the exception of East Asia, where capital requirements are also very high, capital requirements in MENA and, in particular, in GCC countries significantly exceed those of all other regions. Also, the number of procedures a bank must comply with to obtain a license is higher in MENA relative to most regions.<sup>10</sup> When it comes to the number of applications denied, the rejection rate in MENA also exceeds those of all other regions.

Third, we consider a measure of the scope, access, and quality of credit information to capture the extent to which credit information is widely available and shared by all banks. Dell’Ariccia et al. (1999) show that to the extent that access to credit information is limited, incumbent banks are more able to exercise market power and to limit bank entry. Hence, we expect a positive association between the scope, access, and quality of credit information and the degree of competition across countries. We measure the _Credit information environment_ as the average over the period 2005-2008 of the index of credit information compiled by the World Bank _Doing Business Indicators._ This index takes values from 0 to 6 with larger numbers representing greater scope, access, and quality of credit information. Column 6 of Table 7 shows that MENA’s credit information index (2.9) is relatively low compared to other developing regions. In fact, it is only higher than Sub-Saharan Africa (2.0) and Former Soviet Union (2.0).

> 9 These data can be found at

> http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:20345037~pagePK: 64214825~piPK:64214943~theSitePK:469382,00.html

> 10 Across countries in MENA, there is no variation in the number of requirements for entry. Across all countries, banks are required to present (1) draft by laws, (2) intended organizational charts, (3) financial projections for the first years of operation, (4) financial information on potential shareholders, (5) background/experience of future directors, (6) background/experience of future managers, (7) sources of funds, and (8) intended  market differentiation.

10
9 Table 3. Shares of Individual Republics in Total FSU Exports, in 1989, 1990 and 1991

||1989|1990|1991|
|---|---|---|---|
||----<br>|**_(-% )_**<br>|**__-----_**<br>|
|Armenia|0.10|0.11|0.10|
|Azerbaijan|0.60|0.70|0.73|
|Georgia|0.50|0.50|0.04|
|Estonia|0.20|0.19|0.07|
|Latvia|0.40|0.28|0.19|
|LiUzuania|0.80|0.66|0.52|
|Belarus|3.40|3.36|3.79|
|Moldova|0.50|0.40|0.36|
|Russia|76.90|76.95|77.31|
|Kazakhstan|1.50|1.74|1.77|
|Kyrgyzstan|0.10|0.09|0.03|
|Tajikistan|0.50|0.60|0.64|
|Turkmenistan|0.20|0.18|0.22|
|Ukraine|12.90|12.87|12.74|
|Uzbekistan|1.40|1.36|1.48|
|FSU-total|100|100|100|

Sources: CIS Goskomstat; World Bank; and UN ECE.
# **B.2. Ordered-Probit Estimation and Marginal Effects Results**

**Table B.1. Meta-Regression Analysis: Results of the Random-Effect and Ordered-Probit Estimations**

|**Variables**|**E**|**stimate mode**|**ls**|**Marginal**|**effects from th**<br>|**e Ordered-**|
|---|---|---|---|---|---|---|
||||||**Probit**||
||**OLS**<br>**t-stat**|**Random-**<br>**Effect**<br>|**Ordered-**<br>**Probit**|**Beneficial**|**Detrimental**|**Insignificant**|
||**Out**|**t-stats**<br>**come(omitte**|**d = economic**|**welfare)**|||
|Equity|-2.916|-0.087|-0.710|-0.129|0.068|0.061|
||(2.384)|(0.202)|(0.826)|(0.146)|(0.081)|(0.067)|
|Social inclusion|-1.579|-0.087|-0.845|-0.153|0.081|0.072|
||(1.137)|(0.202)|(0.616)|(0.106)|(0.060)|(0.048)|
|Environment|-23.81***|-0.826***|-16.02***|-2.901***|1.530***|1.372***|
||(2.486)|(0.242)|(1.533)|(0.263)|(0.269)|(0.272)|
||**Type o**|**f construction**|**(omitted = n**|**ew system)**|||
|Upgrade of system|-1.004|-0.083|-0.360|-0.065|0.034|0.031|
||(1.624)|(0.219)|(0.557)|(0.100)|(0.054)|(0.047)|
|New link|8.549**|0.671|7.601***|1.377***|-0.726***|-0.651***|
||(3.614)|(0.579)|(1.464)|(0.280)|(0.171)|(0.187)|
|Upgrade of link|0.494|-0.273|0.930|0.168|-0.089|-0.080|
||(2.473)|(0.204)|(0.905)|(0.161)|(0.082)|(0.081)|
|Other|6.243**|0.360*|6.467***|1.171***|-0.617***|-0.554***|
||(2.479)|(0.216)|(1.029)|(0.188)|(0.136)|(0.132)|
||**T**|**ransport mod**|**e(omitted =**|**road)**|||
|Rail|4.371**|0.051|-0.506|-0.092|0.048|0.043|
||(1.985)|(0.196)|(0.398)|(0.069)|(0.035)|(0.035)|
|Waterway|-0.125|-0.128|-0.957*|-0.173**|0.0914**|0.0819*|
||(1.722)<br>**Conn**|(0.256)<br>**ection type (o**|(0.501)<br>**mitted = urb**|(0.085)<br>**an-urban)**|(0.046)|(0.043)|
|Urban-rural|2.415|-0.021|-0.281|-0.051|0.027|0.024|
||(1.847)|(0.167)|(0.477)|(0.085)|(0.046)|(0.039)|
|Urban-gateway|1.748|-0.002|0.705|0.128|-0.067|-0.060|
||(1.430)|(0.253)|(0.559)|(0.102)|(0.053)|(0.051)|
|**H**|**eterogeneity fa**|**ctor(omitted**|**= does not lo**|**ok for hetero**|**geneity)**||
|Geographic|-8.173***|-0.483*|-1.033*|-0.187*|0.099|0.0884*|
||(2.333)|(0.276)|(0.627)|(0.107)|(0.064)|(0.046)|
|Sectoral|-7.844**|-0.208|-0.836|-0.151|0.080|0.072|
||(3.212)|(0.323)|(1.127)|(0.203)|(0.109)|(0.096)|
|Geo-sectoral|<br>-6.372***|<br>-0.406**|<br>-1.245**|<br>-0.226**|<br>0.119*|<br>0.107**|
||(2.077)|(0.195)|(0.595)|(0.104)|(0.065)|(0.044)|
|Land|<br>-4.015|<br>0.018|<br>4.980***|<br>0.902***|<br>-0.476***|<br>-0.426***|
||(2.456)|(0.312)|(0.853)|(0.192)|(0.098)|(0.138)|
|Labor|-5.624***|0.194|0.291|0.053|-0.028|-0.025|
||(1.802)|(0.284)|(0.852)|(0.154)|(0.082)|(0.073)|
|Temporal|-4.004|0.201|1.690*|0.306*|-0.161*|-0.145*|
||(2.693)|(0.318)|(0.907)|(0.161)|(0.086)|(0.083)|

50
**Figure 1. The oil price plunge of 2014–16 in perspective**

# **A. Nominal oil and metals price indexes**

# **B. Real oil prices**

<!-- Start of picture text --> Index, 100=Sept. 2010 US$/bbl<br>200 Oil 140<br>Metals 1970-2017 average<br>120<br>150<br>100<br>80<br>100<br>60<br>50 40<br>20<br>0 0<br>D. Oil production, cumulative change since<br>December 2010<br>mb/d mb/d<br>2 6 U.S. shale<br>5 Iran, Islamic Rep.<br>1 4 Libya<br>3 Net<br>2<br>0 1<br>0<br>-1 -1<br>-2<br>-3<br>-2<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2018<br>2011Q1 2011Q3 2012Q1 2012Q3 2013Q1 2013Q3 2014Q1 2014Q3 2015Q1 2015Q3 2016Q1 2016Q3 2017Q1 2017Q3 2018Q1 2011Q1 2011Q3 2012Q1 2012Q3 2013Q1 2013Q3 2014Q1 2014Q3 2015Q1 2015Q3 2016Q1 2016Q3 2017Q1 2017Q3 2018Q1<br><!-- End of picture text -->

# **C. World oil balance**

Sources: Energy Information Administration, International Energy Agency, World Bank.

A. Last observation is March 2018.

B. Real oil prices are calculated as the nominal price deflated by the international manufacturers unit value index, in which 100=2010. World Bank crude oil average. Last observation is March 2018.

C. Oil supply balance is the difference between global oil production and consumption, in million barrels per day (mb/d). Last observation is 2018Q1.

D. Last observation is 2018Q1.

15
consume (e.g. wheat, cooking oil) and contribute mainly to the intensive margin of how much of the food items they consume and not much to the extensive margin of whether they consume foods from specific food categories. Third, dietary diversity of mothers and children is already at a very low level and, thus, unlikely to decline further despite the COVID-19 pandemic. Fourth, the PSNP transfers may not be sufficiently nutrition-sensitive, pointing to the need for further refinements to make the program nutrition-sensitive (Berhane et al., 2020).

Though the PSNP program had little differential impact on the diet diversity of mothers and their children, it could conceivably improve the quality of their diet. The cash and in-kind transfers from the PSNP program may allow participants to substitute lower quality foods (e.g. cereals and beans) with higher quality diet such as meat, dairy, milk and eggs. To gauge if this is indeed the case, we group dairy, flesh foods and eggs together into “animal source food” and vitamin A rich fruits and vegetables into another group. Table 7 presents regression estimates for mothers and children separately. We find that there has been a sharp decline in animal source food by mothers whereas children’s intake of animal products has changed little in the COVID-19 period. On the contrary, consumption of vegetables and fruits increased both for mothers and children, though it is statistically insignificant for mothers. The increase in vegetable and fruits consumption is particularly large for children. We do not find any evidence of a protective role of the PSNP program on the diets of mothers and children. This is not surprising given that the composition of the in-kind transfers is unlikely to have changed in the COVID-19 period, and restrictions in access to markets may mean cash transfers are unlikely to make a meaningful difference in the range of foods available to households.

# **6. Summary and Concluding Remarks**

We combine data from a pre-pandemic face-to-face survey with post-pandemic phone survey from Ethiopia, finding that household food security deteriorated in the aftermath of the onset of the pandemic. About half of the households surveyed reported that food security had worsened compared to the same pre-pandemic period. Market closures, food price increases and loss of income appear to be the most important aspects in which the pandemic impacted livelihoods. We do not find significant changes in mothers’ and children’s diets, despite some changes in the composition of diets. Consumption of animal source foods declined significantly, perhaps due to closure of markets associated with the pandemic.

17
## **Trade Data (Values and Quantities of Imports)**

Data on the quantity and value of imports for each of the five years of our sample were acquired from the electronic database of the commercial company AcademyService.<sup>11</sup> This dataset provides information on the Russian tariff structure at the tariff line level, i.e., the 10-digit level.

For the year 2003, we received a comparable dataset from the Ministry of Economic Development and Trade. At the request of the Ministry, we recalculated all results with the data provided from the Ministry and compared the results with the database from the commercial firm Academy Service. We found the results to be extremely close. We examined the few cases of discrepancies and concluded that the differences were due to the fact that the company Academy Service corrected for outlier errors in the reported data from the Customs Service.

## **Tariff data**

The source of information on tariff rates is the Decree of the Government of Russian Federation on import duties #830.<sup>12</sup> The decree is available, for example, at <u>www.base.consultant.ru</u>

# **IV. Methodology for calculation of the tariff rates.**

## **MFN Tariff Rates**

Most tariffs in the Russian Federation are simple ad valorem tariffs. For these tariff lines, the MFN tariff is straightforward and no calculations are necessary.

However, between 1,609 and 1,792 tariff lines out of 11,000 plus tariff lines had a potential specific tariff component. Most of these tariff lines with a potential specific tariff component where subject to a combined tariff structure. For most products with the combined tariff, the maximum of the two tariffs is the tariff that applies.<sup>13</sup> For these tariff lines we adopt the following methodology for calculation of the tariff.

> 11 <u>http://www.ftinform.com.</u>

> 12 We looked at five editions of the decree: first, dated by 11.30.2001 for 2001; the second, dated by 02.06.2003 for 2002 rates, for 2003, 2004 and 2005, we took December 31 of the respective year. 13 Footwear is an exception where the sum of the specific and ad valorem tariffs is applied and there are a limited number of tariff lines where only specific tariffs apply.

10
# **The Role of Gender in Agent Banking: Evidence from the Democratic Republic of Congo**<sup>**1**</sup>

Richard Chamboko<sup>a</sup> , Robert Cull<sup>b</sup> , Xavier Giné<sup>b</sup> , Soren Heitmann<sup>a</sup> , Fabian Reitzug<sup>a</sup> and Morne Van Der Westhuizen<sup>a</sup> aInternational Finance Corporation, USA bWorld Bank, USA

**JEL classification :** G21, G23, G29

**Keywords:** gender; agent banking; mobile money; microfinance institutions; financial inclusion

> 1Chamboko: <u>rchamboko@ifc.org; Cull: rcull@worldbank.org; Giné: xgine@worldbank.org; Heitmann: sheitmann@ifc.org; Reitzug: freitzug@ifc.org; Van Der Westhuizen: mvanderwesthuize@ifc.org. We thank</u> participants at the NOVAFRICA conference in Lisbon for feedback and Bernardo Ribeiro and Santiago Rodrigo Cesteros for superb research assistance. Support for this project was provided by the Mastercard Foundation-IFC Partnership for Financial Inclusion and the World Bank. All remaining errors are our own.