rafmacalaba/datause-encoder-data huggingface dataset:
# **III. Data and Descriptive Statistics**

The longitudinal data used in the analysis are drawn from the Bosnia and Herzegovina 2001 Living Standards Measurement Survey (LSMS) and its subsequent waves, from 2002-2004, otherwise known as the “Living in BiH” Survey. The LSMS was first conducted in September-November 2001, a collective effort by the international donor community (World Bank, UNDP, and UK DfiD) and the three local statistical agencies. Some 5,400 households (corresponding to about 9,400 individuals) were interviewed in 2001, about half of which have been interviewed each year through 2004. The sampling frame provided representative data at the national level, for each of the two entities that constitute Bosnia and Herzegovina (FBiH and RS), and by type of municipality (World Bank 2003).

The LSMS, as is typical of multi-topic surveys, includes multiple modules and collects information from households and individuals on their income and consumption behavior, labor market activity, educational attainment or current enrollment, health status and health care utilization, and others. This note uses the labor market module covering individuals’ labor market status, job search behavior, occupation and industry of employment, and wage levels. Survey responses to questions on employment and job search behavior (i.e., whether an individual has been looking for work over the reference period and whether he/she is ready to work in two weeks) allow for the calculation of unemployment consistent with the ILO definition, except in the 2003 wave, where ILOconsistent unemployment indicators could not be computed due to “routing error.” For estimating labor market dynamics, data are drawn from each of the four waves using a total common sample of 6,360 individuals, although the samples of working-age individuals and working-age individuals currently in the labor force are of course much lower. We use both panel weights and wave-specific weights, as appropriate, but the results are invariant to weighting.

7
Figure 14. Debit and Credit card Ownership in Turkey, 2011 <!-- Start of picture text --> Debit Card<br>2011 2014<br>90% 90%<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Turkey BRIC Developing High Rest of ECA Turkey BRIC Developing High Rest of<br>Income Income ECA<br>Credit Card<br>2011 2014<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Turkey BRIC Developing High Rest of Turkey BRIC Developing High Rest of<br>Income ECA Income ECA<br>Lowest quintile q2 q3 q4 top quintile<br>Notes : Developing world excludes Turkey<br>Source : FINDEX 2011 and 2014<br><!-- End of picture text --> The equity adjusted coverage decomposition of debit card ownership in Turkey produces similar results to the analysis of bank account usage at the individual level (Figure 15). However, since the coverage rate of debit card usage in other countries is lower than bank account ownership in general, Turkey’s analogous values across the two indicators place it on a higher ranking in the country distribution in terms of debit card usage (Figure 41). Turkey has a higher dissimilarity index for debit card ownership, which is primarily driven by gender when compared to benchmark countries. The subdivision of the difference in EACR’s across the benchmark countries indicates that debit cards are used at a higher scale in Turkey while the composition effect is more positive across the countries with similar coverage rates (Figure 39).

Between 2011 and 2014, debit card ownership at the individual level fell from 56.6 percent to 43.4 percent in Turkey. On the other hand for three of the four benchmark countries, there were significant increases in coverage rates. Despite the falling coverage, Turkey’s dissimilarity level fell from 24.3 percent to 21.6 percent over the three years, mainly driven by the improvement in the gender component. However, the improvement in dissmilarity is not able to neutralize the negative pressure coming from the declining coverage rate as Turkey’s EACR falls form 42.9 to 33.9 between the two survey periods.

16
Sturzenegger, F., & Tommasi, M. (1998). _The political economy of reform_ . MIT Press.

Sunstein, Cass. 2007. “On the Divergent American Reactions to Terrorism and Climate Change.” _Columbia Law Review_ 107 (2): 503–57.

Sunstein, Cass R. 2009. Going to Extremes: How Like Minds Unite and Divide. Oxford: Oxford University Press Tsai, L.L., 2007. _Accountability without democracy: Solidary groups and public goods provision in rural China_ . Cambridge University Press.

Vicente, P.C. and Wantchekon, L., 2009. Clientelism and vote buying: lessons from field experiments in African elections. _Oxford Review of Economic Policy_ , _25_ (2), pp.292-305.

Wantchekon, Leonard (2003). “Clientelism and Voting Behavior: Evidence from a Field Experiment in Benin.” _World Politics_ 55: 399-422.

Weitz‐Shapiro, R., 2012. What wins votes: Why some politicians opt out of clientelism. _American Journal of Political Science_ , _56_ (3), pp.568-583.

World Bank, 2004. World Development Report: Making Services Work for Poor People, World Bank, Washington DC World Bank, 2006. World Development Report: Equity and Development, World Bank, Washington DC World Bank, 2014. _A Measured Approach to Ending Poverty and Boosting Shared Prosperity_ . Policy Research Report, Development Research Group, Washington DC World Bank, 2016. _Making Politics Work for Development_ . Policy Research Report, Development Research Group, Washington DC World Bank, 2017. World Development Report: Governance and the Law, World Bank, Washington DC Yanagizawa-Drott, David. 2014. “Propaganda and Conflict: Evidence from the Rwandan Genocide.” _Quarterly Journal of Economics_ 129 (4): 1947–94.

54
# Figure 17. MNA: Policies to raise potential growth

<!-- Start of picture text --> A. Potential growth and contributions<br>Percent<br>6 Baseline Demographic trends Other factors<br>4<br>2<br>0<br>2011-21 Factor 2022-30 2011-21 Factor 2022-30<br>EMDEs MNA<br><!-- End of picture text --> C. Climate change scenarios

# B. Reform scenarios

<!-- Start of picture text --> Percent Social benefit reform<br>6 Labor market reforms<br>Education and health improvements<br>5 Investment surge<br>Baseline<br>4<br>3<br>2<br>2011-21 2022-30 Reform 2011-21 2022-30 Reform<br>impact impact<br>EMDE MNA<br><!-- End of picture text -->

- D. Female labor force participation scenarios

<!-- Start of picture text --> Percent Investment boost (climate change) Percent<br>6 Disasters (climate change) 4 Baseline Female LFPR improvement<br>Baseline<br>5<br>3<br>4<br>2<br>3<br>2 1<br>2011-21 2022-30 Reform 2011-21 2022-30 Reform<br>impact impact<br>0<br>EMDE MNA 2011-21 2022-30 Reform<br>E. Share of oil revenue in oil exporters F. Climate risk<br>Percent of total revenue Occurrences per year<br>90 25 GCC<br>85 20 Other oil exporters<br>Oil importers<br>80 15<br>75 10<br>70 5<br>65<br>0<br>60 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1960s 1970s 1980s 1990s 2000s 2010s 2020-21<br><!-- End of picture text --> Sources: EM-DAT database; Haver; Penn World Tables; UN Population Prospects; World Bank.

Note: EMDEs = emerging market and developing economies; MNA = Middle East and North Africa. GDP weights are calculated using average real U.S. dollar GDP (at average 2010-19 prices and market exchange rates) for the period 2011-21.

A.-D. Period averages of annual GDP-weighted averages. Production function estimates.

A. Sample includes 53 EMDEs, of which 5 countries are from MNA. “Other factors” include trend improvements in human capital, and stable investment growth relative to its long-term average.

- B. Sample includes 53 EMDEs (5 from MNA).

- E. Unweighted averages of seven MNA economies.

- F. GCC = Gulf Cooperation Council. Includes data for 19 MNA economies. Occurrences of natural disasters.
and 36 primary schools were randomly selected into the control group. Selection was stratified by island group, school ownership (public or private), whether the school received students from communities included in the school readiness intervention, and the number of children in the first 6 grades. One school dropped out of the intervention after the second year, and this was replaced by a randomly selected school. Not all schools were eligible to be selected into the control or treatment groups: two other primary school interventions were being piloted in several schools which are excluded from the evaluation’s population. This included all schools on the island of ‘Eua. Note that schools in the treatment and control groups could include students from the school readiness intervention’s treatment communities, control communities and unassigned communities.

Data were collected on both child outcomes and behaviors that the interventions were trying to influence for both interventions. For the school readiness intervention, the Tongan Early Human Capabilities Index (TEHCI) survey measured school readiness and collected data on home activities, and surveys of treatment and control communities were conducted to measure community activities that supported school readiness. For the CLRW intervention, classroom observations were conducted to measure teaching practices and the Tongan Early Grade Reading Assessment (TEGRA) measured students’ early grade reading skill. These data are discussed in more detail in the sections on the respective interventions. Table 1 presents a timeline for the interventions and data collection by birth cohort. These cohorts are approximate, as children may enter first grade later and sometimes earlier than age 6.

As noted above for both interventions, there is a distinction between intervention and treatment. For the school readiness intervention, we are measuring the effect of providing community leaders with support to establish and maintain CPBAs, not the effect of a child’s exposure to a CPBA. For the reading intervention, we are measuring the effect of providing teachers with training, materials and coaching, not the effect of exposure to the new pedagogic approach. In both cases, the effect size of the intervention underestimates the effect size of the underlying treatment. As a result, the effects of the two interventions are not directly comparable primarily because all children in the CLRW treatment schools are exposed to the underlying treatment while a much smaller fraction of children in the community are exposed to a CPBA. However, 10
|TV sets, radio, cassette-tape, disc, video, VCD, DVD player|23.8|-5.6|-5.8|-6.9|-4.8|
|---|---|---|---|---|---|
||||_0.58_|_0.67_|_0.44_|
|Computer & equipment|18.4|1.1|1.1|2.1|0.9|
||||_0.91_|_0.72_|_0.66_|
|Toys (include baby cars)|8.8|0.3|1.8|2.2|4.7|
||||_0.01_|_0.55_|_0.00_|
|Cinema / sports / traditional arts (admission/fees)|10.6|-2.6|-4.3|-4.6|-4.9|
||||_0.01_|_0.79_|_0.74_|
|Interest payments|224.6|18.2|-174.6|-47.6|-207.5|
||||_0.00_|_0.00_|_0.00_|
|Grains & cereal products: purchased|87.3|4.1|-10.7|0.3|-16.0|
||||_0.00_|_0.00_|_0.00_|
|Grains & cereal products: home production|66.8|0.7|18.2|-7.2|15.1|
||||_0.00_|_0.00_|_0.00_|
|Grains & cereal products: all sources|154.0|4.7|7.5|-6.9|0.9|
||||_0.00_|_0.00_|_0.00_|
|Meat and poultry|482.3|10.7|-84.1|-62.6|-83.3|
||||_0.00_|_0.00_|_0.00_|
|Fish and seafood|351.9|4.3|-51.6<br>_0.00_|-20.9<br>_0.00_|-45.5<br>_0.00_|
|Milk, cheese, and eggs|346.7|-2.6|-30.0|-27.1|-47.2|
||||_0.00_|_0.53_|_0.00_|
|Fruits and nuts|269.5|7.5|-40.3|-20.5|-102.1|
||||_0.00_|_0.00_|_0.00_|
|Spices and condiments|99.7|-5.1|-16.3|-15.7|-18.5|
||||_0.00_|_0.45_|_0.00_|
|Prepared food (taken home)|677.7|50.4|-88.6|44.5|-29.5|
||||_0.00_|_0.00_|_0.00_|
|Food & non-alcoholic beverages (eaten away from home)|1086.4|-66.6|31.6|104.2|71.5|
||||_0.00_|_0.00_|_0.04_|
|Alcoholic beverages: drink at home|130.0|-6.1|-8.3|4.0|-17.7|
||||_0.51_|_0.03_|_0.00_|
|Alcoholic beverages: drink away from home|80.6|-11.0|-9.0|-3.1|-20.4|
||||_0.92_|_0.40_|_0.01_|
|Cigarettes, tobacco, etc.|104.0|-4.3|-8.2|-3.3|-19.9|
||||_0.14_|_0.14_|_0.00_|
|**Stock held per household (units)**|_units_||_uni_|_ts_||
|Bicycles|0.7|0.0|**0.3**|**0.0**|**0.1**|
||||_0.00_|_0.00_|_0.01_|
|Motorbicycles|1.2|0.0|**-0.2**|**0.2**|**0.0**|
||||_0.00_|_0.00_|_0.00_|
|Cars|0.1|0.0|0.0|0.1|0.1|
||||_0.90_|_0.08_|_1.00_|
|Vans|0.3|0.0|**-0.5**|**0.1**|**-0.4**|
||||_0.00_|_0.00_|_0.00_|

Note: The coefficients measure the time fixed effects relative to 2007:Q1. The figures in italics are p-values for a test for whether there is a difference between the fixed effects between that year and the previous year. Values are shown in the prices of 2007. Boldfaced numbers are statistically different from the previous year at the 5% level; yellow cells indicate lower real expenditure per capita in 2009 than in 2008. Number of observations: 176,141 individuals.

# **Are Survey Data Essential?**

Thailand is unusual in the quality and frequency with which it collects household survey data. Could one have determined the effects of the 2008 recession in a cheaper way, for instance using Losers and Winners in Recession: Thailand 2008-09 24
<!-- Start of picture text -->
|<br>_ jf #F p.|<br>100% Te<br>80%.<br>60%.<br>40% —<br>Ee ————El<br>20%<br>2001 2002 2003 2004 2005 2006 2007 2008<br>-—&Gcc —*— Non-GCc —+— Non-GCc Private-led —— Non-GCc State-led<br><!-- End of picture text -->
The international price of coal, represented by the price of coal in Australia, one of the main coal exporters in 2019, was $77.89/MT and the international price of natural gas, represented by LNG import price in Japan, the main importer in 2019, was $10.57/MMBTU (World Bank, 2020). These prices are very close to the high values of coal and gas prices considered in our analysis. As fuel prices are volatile, the relative economics of renewable sources for power generation could be affected due to fuel price volatility. This will be discussed in the next section.

Let us compare the LCOEs of various technologies when these technologies are assigned the middle value of the capital costs from Table 3. In this case, solar PV and hydro would be cheaper than fossil fuel-based technologies when we use high fuel prices, which reflect the market prices in 2019 in many countries around the world under all discount rates considered. Onshore wind and geothermal would also compete with fossil fuels only if the renewables have high capacity factors, as specified in Table 3. In the United States, where fossil fuels are relatively cheaper, solar PV would compete with fossil fuels with low fuel prices only at a high capacity factor (30%) and with a discount factor of less than 6%. With the middle value of capital costs from Table 3, other renewables have higher LCOEs than fossil fuels in the United States, even at a lower discount rate (5%) because of low fuel prices.

As mentioned in Section 1, solar power has been supplied at very low prices (< US$20/MWh) through auctioning in many countries in the past few years. Our analysis, however, does not find LCOEs below $20/MW unless the discount rate is 6% or lower, the capacity factor is very high (30%), and economic life is very optimistic (30 years). A 30% capacity factor is high for solar. Similarly, 30 years of economic life is highly optimistic for solar PV. With the highest capacity factor reported to date in MENA countries (27.5%), the lowest capital costs reported to date ($618/kW in India) and an optimistic value of economic life (25 years), the LCOEs would be $20.8/MWh, $22.6/MWh, $24.6/MWh and $30.8/MWh for 5%, 6%, 7% and 10% discount rates, respectively (Figure 3). These LCOEs are higher than $20/MWh. Thus, there is a disconnect between the recent auction prices for solar and the LCOEs, even with highly optimistic assumptions. The lowest value of LCOE calculated by IRENA (2020) for utility-scale solar PV is much higher than the low auction prices observed recently.

16
influencing migration patterns. First, the sample includes the majority of provinces which attracted FDI.<sup>16</sup> Second, unlike the VHLSS data, the sampling framework of which is based on permanent registration status (thus excluding temporary migrants), the VMS includes migrants with temporary or no household registration. Coverage of the latter migrants is important since many migrants who work for foreign firms tend to hold a temporary status.<sup>17</sup> Third, the VMS includes detailed information on individual characteristics and migration outcomes in destination areas, this permits an analysis of differential patterns of migration depending on job outcomes. Finally, since one can identify from the VMS where migrants came from, this in turn makes it possible to examine the characteristics of their origin areas.

In order to study the characteristics of migrants relative to non-migrants, the data on nonmigrants are taken from the VHLSS 2004. The VHLSS 2004 was conducted by the GSO of Vietnam with the technical support of the World Bank and is generally recognized to be of high quality and representative of all of Vietnam. The VHLSS 2004 data are also used to compute the province (and urban/rural) level variables in origin and destination areas.

The first and second columns of Table 1 compare the descriptive statistics of migrants vs. nonmigrants constructed from the VMS 2004 and VHLSS 2004 respectively. For the sample of migrants taken from the VMS, individuals between 15-59 years of age who work, and those who do not work but sought employment, are included. As a comparison group, the sample of nonmigrants is constructed from the VHLSS 2004. Non-migrants are defined as those who are 15-59 years of age and either work, or don’t work but sought employment, and who resided at the same

> 16 The eleven provinces covered by the VMS accounted for 83.3 percent of foreign employment in 2004 (author’s calculation based on the _Enterprise Survey_ data (GSO)).

> 17 According to the VMS, only 10.2 percent of the migrants who work for foreign firms possess permanent registration status.
<!-- Start of picture text --> 1B: Sub-national sample<br>Weak Link to Contemporaneous Conflict Strong Link to Cumulative Conflict<br>2 (correlation=0.088) s (correlation=0.283)<br>~ Qo ° ~ Bo “°<br>g%o CBMGES g oo 0 eeBio @8. oe, %0°, a<br>BBe eh0 5, 9 oTe po! S958 :<br>“ro 08 aesFHP me BRaro8 eq, 28~ & &3 ee«0 PS Boo gagoO” & °<br>2 ite Hoo” e 2 i 0° 0 a8 °<br>=e3 SaBEBE QP of =e3 Pagoi?28 oe B58 2,<br>é& | EERO Eo, 8. é CREELSe el<br>ag] g ee 5 Rg SBR 8°<br>° Racacinoe beat aed- o Santee Coaee SsBo sac te<br>Fatalities per capital (In) Cumulative fatalities per capital (In)<br>© Poverty rate at1.9USD(PPP) ———— Fitted values<br><!-- End of picture text -->

<!-- Start of picture text --> 1A: Cross-country sample<br>Weak Link to Contemporaneous Conflict Strong Link to Cumulative Conflict<br>o= (correlation=0.183) o= (correlation=0.301)<br>~ ° . °<br>- a = °% 5<br>8B,© > oRBO 9%,ot g9 8 BB]= Sgoa099° ge ° °8<br>ba=2 - ° 0 hte?Sh oPF9 @of o @3 0 %oS° Sp9 - ehBROO° | os ©<br>£e48 > S fe 2 BS8 oP, 0<br>F |, Sods0 em ae Re> © 0, P|Bel (0%8 . 9oe QeZX, ©os08<br>é 00 BBB car0, & oF | 0 Bo SoRgeege<br>R1 BdgordR ORES o ot R180 do MR, Fe<br>{9orGod passPemba 9?f°° o {ocrPp: aSEDOSMoa ah Pa<br>Fatalities per capita (In) Cumulative fatalities per capita (In)<br>© Poverty rate at1.9 USD(PPP) === Fitted values<br><!-- End of picture text -->
An Assessment Of Reform Options For The Public Service Pension Fund In Uganda Beside maturity, differences in these ratios are also due to demographic aging patterns. A higher old-age dependency ratio increases, other things equal, both the IPD and expenditure shares, but differences in future dependency ratios first impact IPD before also translating into higher pension expenditure shares.

It is also important to note that alternative indexation of wage records and benefits changes the estimated IPDs but not dramatically in most countries. Moving from wage to price indexation of benefits reduces IPD, on average, by some 15 to 20 percent, with a lower reduction the higher the discount rate.

Current pension spending levels will fail to reflect reforms that are being gradually phased in or changes to indexation which have large compounded impacts. The IPD numbers capture this, therefore providing a more accurate assessment of the long term burden compared across countries.<sup>38</sup>

> 38 Another relevant measure for the purposes of assessing sustainability is the ratio of IPD to the wage bill subject to contributions.  Using this measure highlights the problems of poor countries such as Senegal in servicing its IPD despite having a relatively low IPD/GDP ratio.

- 95 -
Giller, Ken E., Jens A. Andersson, Marc Corbeels, John Kirkegaard, David Mortensen, Olaf Erenstein, and Bernard Vanlauwe. 2015. "Beyond conservation agriculture." _Frontiers in plant science_ 6: 870.

- Giller, Ken E., Renske Hijbeek, Jens A. Andersson, and James Sumberg. 2021. "Regenerative agriculture: an agronomic perspective." _Outlook on agriculture_ 50 (1): 13-25.

Grima, Nelson, Simron J. Singh, Barbara Smetschka, and Lisa Ringhofer. 2016. "Payment for Ecosystem Services (PES) in Latin America: Analysing the performance of 40 case studies." _Ecosystem services_ 17: 24-32.

Jack, B. Kelsey, Seema Jayachandran, Namrata Kala, and Rohini Pande. 2022. "Money (Not) to Burn: Payments for Ecosystem Services to Reduce Crop Residue Burning." _National Bureau of Economic Research_ No. w30690.

Jayachandran, Seema, Joost De Laat, Eric F. Lambin, Charlotte Y. Stanton, Robin Audy, and Nancy E. Thomas. 2017. "Cash for carbon: A randomized trial of payments for ecosystem services to reduce deforestation." _Science_ 357 (6348): 267-273.

- Jones, Maria, Florence Kondylis, John Loeser, and Jeremy Magruder. 2022. "Factor market failures and the adoption of irrigation in rwanda." _American Economic Review_ 112 (7): 2316-2352.

- Karlan, Dean, Robert Osei, Isaac Osei-Akoto, and Christopher Udry. 2014. "Agricultural decisions after relaxing credit and risk constraints." _The Quarterly Journal of Economics_ 129 (2): 597-652.

Khangura, Ravjit, David Ferris, Cameron Wagg, and Jamie Bowyer. 2023. "Regenerative agriculture—A literature review on the practices and mechanisms used to improve soil health." _Sustainability_ 15 (3): 2338.

Khonje, Makaiko G., et al. 2018. "Adoption and welfare impacts of multiple agricultural technologies: evidence from eastern Zambia." _Agricultural Economics_ 49 (5): 599-609.

LaCanne, Claire E., and Jonathan G. Lundgren. 2018. "Regenerative agriculture: merging farming and natural resource conservation profitably." _PeerJ_ 6: e4428.

- Lorenzetti, Lorenza Alexandra, and Andrea Fiorini. 2024. "Conservation Agriculture Impacts on Economic Profitability and Environmental Performance of Agroecosystems." _Environmental Management_ 73 (3): 532-545.

- Lowder, Sarah K., Jakob Skoet, and Terri Raney. 2016. "The number, size, and distribution of farms, smallholder farms, and family farms worldwide." _World development_ 87: 16-29.

- Lymbery, Philip. 2021. "An urgent call for global action to shift to regenerative farming." _Nature Food_ 2 (11): 846-847.

Makate, C., M. Makate, N. Mango, and S. Siziba. 2019. "Increasing resilience of smallholder farmers to climate change through multiple adoption of proven climate-smart agriculture innovations. Lessons from Southern Africa." _Journal of environmental management_ 231: 858-868.

- Michler, J. D., K. Baylis, M. Arends-Kuenning, and K Mazvimavi. 2019. "Conservation agriculture and climate resilience." _Journal of environmental economics and management_ 93: 148-169.

22