rafmacalaba/datause-encoder-data huggingface dataset:
To look further back in time at trends in childhood mortality in the two different sets of villages before the intervention began, mortality rates were calculated from the birth histories obtained from women of reproductive age in January-February 1990. In addition, it was possible to go back to the data tapes frxa the 1983 national population census and to produce tables of the proportions dead of children ever-borne alive by age of mother for exactly the same villages included in the intervention trial. The data from the birth histories are more reliable since it was possible to re-check some results with reference to the MRC surveillance data. The level and trends in under 5 mortality are shown on Figure 1. Much the same results are obtained from the indirect estimates from the 1983 census but omission of children ever-born as well as dead children by comparison with the surveillance data make these results difficult to interpret. The data on Figure 1 strongly suggest that whilst there had beer a slow improvement in child mortality before the intervention, the mortality in the two sets of villages chosen for the intervention was not very different despite the different sizes and different participation in the national primary health care system by the two types of villages. A clear demonstration of the role of malaria in the mortality decline can be seen in the graph of deaths by month for PHC and non-PHC villages (see footnote 6).

# THE IMPACT OF **THE** BED-NETS

# General mortality

The data in Table 4 show that the probability of dying for 1-4 years olds fell sharply in 1989-90 to 29% of the 1988-89 level in the PHC villages. Mortality in this age range was unchanged in the non-PHC villages. These results can be expressed in a more detailed way using the rate ratios of the age-specific death rates from the life tables (Table 4).

10
41 Table 2: Resources, Industrialization, Deindustrialization & Urbanization, 1960-2020

|**Specifcation:**|**Long-Diff.**|**Panel Anal**|**ysis (Country**|**FE & Year FE)**|
|---|---|---|---|---|
|Dep. Var. : Urban Share URB (%) in ...|2020|Year_t_|Year_t_|Year_t_|
|Timing for the Panel: Every ...||20 Years|10 years|5 years|
|**Panel A:**Baseline|(1)|(2)|(3)|(4)|
|NRXGDP (%) (1): 2020; (2)-(4):_t_-1|1.02***|0.28**|0.19**|0.14*|
||[0.239]|[0.106]|[0.089]|[0.072]|
|MFGSERV (%) (1): 2020; (2)-(4):_t_|1.09***|0.44**|0.43***|0.38***|
||[0.195]|[0.179]|[0.141]|[0.121]|
|DEINDU (%) (1): 1980-2020; (2)-(4): 1980-_t_|0.04|0.53|0.42|0.41*|
||[0.343]|[0.402]|[0.266]|[0.238]|
|Beta Coef. NRXGDP|0.49|0.16|0.10|0.07|
|Beta Coef. MFGSERV|0.57|0.26|0.26|0.24|
|Beta Coef. DEINDU|0.01|0.10|0.07|0.07|
|**Panel B:**Decomposing NRXGDP|(1)|(2)|(3)|(4)|
|FMXGDP (%) (1): 2020; (2)-(4):_t_-1|0.91***|0.29***|0.22**|0.17**|
||[0.235]|[0.101]|[0.090]|[0.076]|
|AGXGDP (%) (1): 2020; (2)-(4):_t_-1|1.22**|0.20|0.02|-0.03|
||[0.610]|[0.254]|[0.175]|[0.070]|
|Beta Coef. FMXGDP|0.40|0.15|0.11|0.08|
|Beta Coef. AGXGDP|0.22|0.05|0.00|0.00|
|Obs.; Controls; Country FE, Year FE|115; Y; N|347; Y; Y|693; Y; Y|1,387; Y; Y|

_Notes:_ Robust SE (clust. at the country level in cols. (2)-(4)) in parentheses. The six variables have the following summary statistics in col. (1): URB: mean = 52.1; SD = 19.0; min = 13.3; max = 100.0; NRXGDP: mean = 7.9; SD = 9.0; min = 0.4; max = 63.9; MFGSERV: mean = 70.5; SD = 9.9; min = 34.2; max = 93.6; DEINDU: mean = 4.6; SD = 5.0; min = 0.0; max = 21.3; FMXGDP: mean = 4.4; SD = 7.5; min = 0.0; max = 59.8; and AGXGDP: mean = 2.2; SD = 2.7; min = 0.0; max = 37.1. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.
|**Table 4: Effects of Agent Rollout**|**on Airtel Mob**<br>|**ile Money Tr**<br> <br>Depe|**ansactions(Jan**<br> <br>ndent variable:|**uary to Nove**<br>Number of m|**mber 2017 Adm**<br>onhtlytransac|**inistrative D**<br>tions|**ata)**<br>|
|---|---|---|---|---|---|---|---|
||Any<br>transaction|Send P2P<br>transfer|Receive P2P<br>transfer|Cash in|Cash out|Bill pay|Airtime or<br>data top up|
|Panel A: ITT for all clusters|(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Agent|-1.360|-0.000|0.009|-0.132|-0.044|-0.100|-1.047|
||(1.107)|(0.015)|(0.022)|(0.130)|(0.061)|(0.083)|(0.989)|
|R-squared|0.164|0.009|0.014|0.010|0.026|0.149|0.181|
|Control group mean|2.395|0.032|0.047|0.296|0.196|0.157|1.606|
|Panel B: ITT byAirtelpriority||||||||
|Agent*High priority|-2.090|0.013|0.024|-0.201|0.036|-0.187|-1.794|
||(2.181)|(0.024)|(0.032)|(0.252)|(0.080)|(0.167)|(1.980)|
|Agent*Low priority|-0.637|-0.014|-0.006|-0.064|-0.123|-0.013|-0.306|
||(0.457)|(0.017)|(0.031)|(0.064)|(0.091)|(0.016)|(0.214)|
|R-squared|0.165|0.008|0.014|0.009|0.027|0.150|0.181|
|F-test p-value (Agent*High = Agent*Low)|0.515|0.360|0.496|0.599|0.188|0.302|0.456|
|<br>Control group mean - High priority|3.641|0.031|0.046|0.427|0.164|0.289|2.670|
|Control group mean - Low priority|1.116|0.032|0.049|0.161|0.228|0.023|0.514|
|Observations|5236|5236|5236|5236|5236|5236|5236|
|Number of months|11|11|11|11|11|11|11|
|Number ofphone numbers<br>Note: Analysis at the phone number level f<br>phone numbers in the Airtel transactions d<br>shows results from OLS regressions of the<br>for the agent rollout and equal to zero oth<br>interacted with two dummy variables indic<br>more time rolling out agents and a larger f<br>stratified by priority rating. All regressions<br>of the dependent variables. Standard erro<br>*** p<0.01. To deal with multiple hypothe<br>†† q<0.05, ††† q<0.01.|476<br>or 476 Airtel p<br>ata since we d<br>dependent va<br>erwise. Panel<br>ating Airtel pri<br>raction of thes<br>include rando<br>rs clustered at<br>sis testing, we|476<br>hone numbe<br>o not have th<br>riables on a d<br>B shows resul<br>ority rating. I<br>e clusters rec<br>mization strat<br>the cluster le<br>calculated sh|476<br>rs reported by o<br>e necessary inf<br>ummy variable<br>ts from similar r<br>n clusters with h<br>eived an agent t<br>a dummies and<br>vel in parenthes<br>arpened q-value|476<br>ur survey res<br>ormation to<br>that is equal<br>egressions, w<br>igher priorit<br>han clusters<br>household h<br>is. Statistical<br>s and indicat|476<br>pondents. We<br>map these to o<br>to 1 if the clust<br>here the agen<br>y, the professio<br>with low priori<br>ead age. Contr<br>significance lev<br>e statistical sig|476<br>do not inclu<br>ur study loca<br>er was rando<br>t rollout dum<br>nal services f<br>ty. The rando<br>ol group me<br>els: * p<0.10<br>nificance leve|476<br>de other<br>tions. Panel A<br>mly selected<br>my is<br>irm spent<br>mization was<br>ans are means<br>, ** p<0.05,<br>ls: † q<0.10,|

24
<!-- Start of picture text --> Mtoe Estimates of Technical Potential of Renewable Energy Resources<br>90,000 BUNDP (2000), Johansson at al (2004}<br>Bide Vries ot al. (2007)<br>80.000<br>Mice<br>70,000 ace<br>ane<br>60,000<br>. 1.009<br>400<br>50,000 aon<br>400<br>40,000 200<br>q<br>30,000 Hydropower Ocean<br>Renewable Energy Technologies<br>20,000<br>10,000<br>0<br>Solar Wind Geothermal Biomass Hydropower Ocean<br>Renewable Energy Technologies<br><!-- End of picture text -->
Figure 2: Services GVC participation, Gross Exports and Services GVC participation by Type <!-- Start of picture text --> GVC participation and gross exports SERVICES<br>Services<br>IRL 0.2<br>0.2<br>SGP<br>0.1<br>VNM BEL<br>EST CYPSVK HUNCZE MYSTHA HKG 0.1 0.1 0.1<br>KHM TUNCOLISLKAZLTUPERLVACHLSVNHRVBGRCRIMARGRCNZLZAFSAUPRTARGTURMEXROUIDNDNKNORFINPHLCHNAUSTWNBRARUSPOLAUTISRKORITASWECANESPJPNNLDCHEROWFRAINDDEUGBR USA 0.0 0.0 0.1 0.0 0.1 0.0 0.1 0.0 0.1 0.0 0.0 0.1<br>MYS KOR DEU IND GBR BRA MEX CHN USA<br>6 8 10 12 14 Backward Forward<br>Gross exports log (2015)<br>Source: OECD; own calculations<br>Figure 3: GVC Participation for IT and Information Services<br>GVC participation and gross exports IT AND INFO<br>Backward GVC participation and gross exports IT AND INFO Forward GVC participation and gross exports IT AND INFO<br>LUX<br>IRL LUX SGP<br>IRL<br>LUX<br>KHM KAZBRN THAPERTUNCOLSAUMLTTURMEXCYPLTUHRVISLCHLSVNIDNLVAGRCVNMZAFNZLESTSVKPRTCRIMARBGRHKGTWNBRAARGCZENORHUNAUSDNKMYSPHLCHNRUSROUKORJPNCANAUTPOLITAFINSGPBELFRAISRROWESPNLDSWEGBRCHEDEUUSA IND KHM KAZBRN THAPERTUNCOLSAUMLTTURMEXCYPLTUHRVISLCHLSVNIDNLVAGRCVNMNZLZAFESTSVKPRTCRIMARBGRHKGTWNBRAARGCZENORHUNAUSDNKMYSPHLCHNRUSROUKORJPNCANAUTPOLITAFINSGPBELFRAISRROWESPNLDSWEGBRCHEDEUUSA IND KHM KAZBRN THAPERTUNCOLSAUMLTTURMEXCYPLTUHRVISLCHLSVNIDNLVAGRCVNMNZLZAFESTSVKPRTCRIMARBGRHKGTWNBRARGCZENORHUN A DNKMYSPHLUSCHNRUSROUKORJPNCANAUTPOLITAFINBELFRAISRROWESPNLDSWEGBRCHEDEUUSAIRLIND<br>0 5 10 0 5 10 0 5 10<br>Gross exports log (2015) Gross exports log (2015) Gross exports log (2015)<br>80<br>60<br>40<br>20<br>0<br>.8<br>.6<br>.4<br>.2<br>0<br>.6<br>.4<br>.2<br>0<br>2<br>1.5<br>1<br>.5<br>0<br>.2<br>.15<br>.1<br>.05<br>0<br><!-- End of picture text --> Source: OECD; own calculations Figure 3: GVC Participation for IT and Information Services Source: OECD TiVA, Own calculations Outward-oriented economic reforms that took place in India during the 1990s increased service imports and helped firms access the best available services inputs from abroad. India eased several equity FDI restrictions in manufacturing and services. Backward GVC participation is positively associated with FDI. This is one of the conclusions that emerges from the World Development Report (World Bank 2020). Generally, inward FDI is accompanied by knowledge spill-overs and this facilitates participation in GVCs (where new knowledge is acquired). FDI also allows for upgrading of existing activities in GVCs. As can be seen in Figure 4, firms that have foreign ownership import a higher share of their inputs and export a higher share of their output than wholly domestic owned firms. While foreign ownership restrictions have been eliminated in several sectors over the years, they remain important for banking, insurance, retail 5
<mark>Ruf, F., 2008. Côte d'Ivoire: la terre de plus en plus chère. Accessed at: https://agritrop.cirad.fr/547253/1/document_547253.pdf</mark> <mark>Sheahan, M. and Barrett, C.B., 2017. Ten striking facts about agricultural input use in Sub-Saharan Africa.</mark> _<mark>Food Policy</mark>_ <mark>,</mark> _<mark>67</mark>_ <mark>, pp.12-25.</mark> <mark>Shrestha, M., 2017.</mark> _<mark>Get rich or die tryin': perceived earnings, perceived mortality rate and the value of a statistical life of potential work-migrants from Nepal</mark>_ <mark>. The World Bank.</mark> <mark>Terjesen, S., Sealy, R. and Singh, V., 2009. Women directors on corporate boards: A review and research agenda.</mark> _<mark>Corporate governance: an international review</mark>_ <mark>,</mark> _<mark>17</mark>_ <mark>(3), pp.320-337.</mark> <mark>Udry, C., 1996. Gender, agricultural production, and the theory of the household.</mark> _<mark>Journal of political Economy</mark>_ <mark>,</mark> _<mark>104</mark>_ <mark>(5), pp.1010-1046.</mark> <mark>Von Braun, J. and Webb, P.J., 1989. The impact of new crop technology on the agricultural division of labor in a West African setting.</mark> _<mark>Economic development and cultural change</mark>_ <mark>,</mark> _<mark>37</mark>_ <mark>(3), pp.513-534.</mark> <mark>World Bank, 2018. Piecing Together the Poverty Puzzle.</mark> <u><mark>https://openknowledge.worldbank.org/bitstream/handle/10986/30418/9781464813306.pdf</mark></u> <mark>World Bank 2019.</mark> _<mark>Cote d'Ivoire Economic Update.</mark>_ <mark>Washington, D.C. : World Bank Group. http://documents.worldbank.org/curated/en/277191561741906355/Cote-dIvoire-Economic-Update</mark> 3
<!-- Start of picture text --> 6.5<br>—<br>a<br>=<br>a<br>i—<br>=<br>=<br>==<br>oD<br>—<br>Ss 2.5<br>= 2.2<br>of<br>=> o~TM 1.3 1.4<br>ESs 0.70.0.8 10° o.6—o8 0.70.8 o.5<br>=e 0.30.3 0.30.2 0.3-~9.20-3<br>= 0.0<br>oa > <— Ped — J? <—<br>We roa we roa we oo<br>cod Ss s<br>Ghana Kenya Zambia<br>No Contract and not known to be on WC | On Contract but not known to be on WiC<br>| Mo Contract but Known to be on WC | On contract and known to be on VC<br>Source: Authors' calculations based on survey responses<br><!-- End of picture text -->

<!-- Start of picture text --> Ss uw oe 2.3<br>Ss<br>=<br>—_2<br>a 1.7<br>=e .<br>= > 1.5<br>= 1.3<br>= 1.1<br>a2 a.9<br>—— 0.9 0.8 oO “0.9<br>o2, os O.7 _ ~<br>co i oO.<br>ZS 0-453 0.30.3 o. 3<br>= o. o.2-<br>0.0<br>—<br>= “a? ES a? & a? ES<br>Soe Ss oe s od Ss<br>oad & SX<br>Ghana Kenya Zambia<br>Mo Contract and not known to be on VC | On Contract but not known to be on WiC<br>| Mo Contract but Known to be on VC | On contract and known to be on VC<br>Source: Authors' calculations based on survey responses<br><!-- End of picture text -->
39 employ a fixed quota each year. The result is legendarily overmanned enterprises and bureaucracies in which the government or public enterprises as of 1988 employed 70 percent of all university graduates and 63 percent of those with education at the intermediate level and above'<sup>1</sup> . This obviously induced large economy-wide distortions in the supply and demand for educated labor (Assaad, 1994).

5' This preponderance of educated labor in the public sector is probably not atypical, although most labor force surveys only calculat **e** mnployment by econonic sector, nt eniployer. Gersovitz and Paxton (1995) calculate that in Cote d' Ivoire in 1986-88, 50 percent of al workers between 25 and 55 with any post primary schooling or above worked in the public sector.
simple probit model estimates, and the coefficient becomes statistically significant at the 1% level.

The models in Panels C and D indicate weaker negative associations between financial literacy and informal credit, and the negative effects shown are not significant at any conventional levels. Interestingly, the significance of the year 2009 crisis dummy and income shock dummy in the fixed effects model appear to highlight the use of informal credit as a primary way to deal with shocks.

# **5.3** **_Real Effects of Financial Literacy_**

We turn now to the real consequences of financial literacy. In this section, we examine the relationship between financial literacy and financial vulnerability indicators, such as respondent level of spending capacity and availability of unspent income. Tables 7 and 8 replicate the same four sets of estimates as the previous tables, using as dependent variables (a) an ordinal spending capacity variable, ranging from 1 (high spending) to 5 (low spending), and (b) an ordinal variable capturing the availability of unspent income, ranging from 1 (low frequency) to 5 (high frequency).

The results in Table 7 show that older individuals, as well as those in the lowest income quartiles, are more likely to experience low spending capacity. The addition of the financial literacy variables in the ordered probit models of Columns A2 and A3 indicates that financial literacy also matters for spending; those who are more financially literate are less likely to report low spending capacity during the financial crisis. The IV estimates in Panel B continue to confirm the negative association between financial literacy and low spending capacity.

24
Jensen, R. (2002) “Equal Treatment, Unequal Outcomes? Generating Sex Inequality through Fertility Behaviour”. _Mimeo_ . Harvard Kennedy School of Government.

Jones, G. and M. Karim (2005) “ _Islam, the State and Population Policies”_ . Hearst & Co. London.

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Karim, M. (2004) “Socio-economic Development, Population Policies, and Fertility Decline in Muslim Countries”. _The Pakistan Development Review_ . 43 (4 ): 773789.

Lehrer, E. (1996) “Religion as a Determinant of Marital Fertility”. _Journal of Population Economics_ . 9: 173-196.

Lukito, R. (2006) “The Enigma of National Law in Indonesia: the Supreme Court’s Decisions on Gender-Neutral Inheritance”. _Journal of Legal Puralism_ . 52: 147167.

Mazrui, A. (1994) “Islamic Doctrine and the Politics of Induced Fertility Change: an African Perspective”. _Population Development Review_ . 121-132.

McQuillan, K (2004) “When Does Religion Influence Fertility?”. _Population and Development Review_ . 30: 25–56.

Mize, L. and B. Robey (2006) “35 Year Commitment to Family Planning in Indonesia: BKKBN and USAID’s Historic Partnership”. _Mimeo_ . John Hopkins Bloomberg School of Public Health.

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Moulasha, K. and G. R. Rao (1999) “Religion-Specific Differentials in Fertility and Family Planning”. _Economic and Political Weekly_ . 34 (42–43): 3047–3051.

29
Table 11.3 Description of Models In Case Studies, Part 3

|NAKE OF PLAN<br>..................<br>..|BWAMANDA<br>INSURANCE<br>PLAN<br>........................|ST. ALPHONSE<br>INSURANCE<br>PLAN<br>.......................|BOKORO<br>HEALTH<br>ZONE<br>"AAONNEMENTU<br>...........................|CASOP/UNTZA<br>..................|
|---|---|---|---|---|
|VERIFICATION<br>OF<br>INSURANCE<br>STATUS|AtLhospital cases must<br>bereferred<br>fromHC.<br>MC checks im<br>eership card<br>against<br>family<br>health<br>card. Clerk athosp. aelso<br>checks card against ember-<br>ship register|Clerk checks meitership<br>card and compares to<br>national<br>ID card.<br>Sautimes clerk checks<br>against<br>mwb, register<br>|Registration form with all<br>member names exists In<br>dupticate<br>at heaLth<br>cente<br>and with household.<br>Famil<br>form presente**d**uring<br>consultation.|Present meebership card<br>(with photo) or give<br>r employee<br>ID number<br>y|
|ENROLLMENT<br>PERIOD|8weeks per yr, Feb-Apr,<br>following<br>harvest<br>period|Any time during<br>**year,**<br>except<br>sick people<br>aren't<br>aLlowed to join and receive<br>care immedistely**at**emaber<br>**rates**|Any timeduping<br>year.<br>-|Any time during year.|
|DURATION<br>OF BENEFI<br>INVESTMENT<br>OF FUND|T<br>One year<br>FuidIsinvested**at**CDI<br>(2.5-3X<br>interest<br>per month<br>or loaned to pharmacy to<br>purchase drugs.|One year<br>Funds are kept**at**perish.<br>) Not Invested.|One year<br>Local expenses<br>**are**dedcted<br>from revenues,<br>balence<br>entirely<br>used for referred<br>patients**and**central<br>operating costs.|As long as monthly<br>dues<br>paid<br>None|
|FINANCIAL SITUATIO<br>OF PLAN|N<br>Every year, plan**has**<br>covered**expnses.**In 1989<br>plan will need to**use**<br>surplus from previous years<br>to cover expenses|Unknown<br>|Underfinanced prior to 198<br>10Xmorkup at present<br>utilization<br>rate of membe|9 Unknown<br>rs|
|TOTAL PLAN REVENU<br>(INCL. INTEREST)|E 1986:unknown<br>1987: 2,245,752 Z<br>1988: 4,958,855 Z<br>1989: 10,887,846 Z (proj.)|Unknown|1986: unknown<br>1987:unkrxmn<br>1988: 2,646,000 Z<br>1989: 5,332,800 Z (proj.)|1989: 403,980 Z|
|TOTAL PLAN EXPENS|ES<br>1986: unknown<br>1987: 1,736,878 Z<br>1988: 4,802,190 Z<br>1989: 11,125,106 Z (proj.)|Unknown|UnknIwn|Unknown|
|FINANCIAL SITUATIO<br>OF HEALTH<br>FACILITY|N Cost recoverysthospitel<br>increased by30Xbetween<br>185**and**_1988._<br>Health centers recover over<br>lOOK of costs.|Highly profitable.<br>Over<br>50 excess revenue over<br>expensesIn 1989<br>(projected).|Health centers cover their<br>operating expensesinc.<br>supervision and<br>adeinistration<br>by BCZS|Unknown, but looks<br>profitable|
|CHANGES<br>IN<br>UTILIZATION:<br>........................................|Hoepitel utilization<br>**rates**<br>higher for members then for<br>nor-mmsbers.<br>.....................................................|Unknown<br>......................................|Planmebers**have**11to 12<br>timeshigher utilization<br>than uminsured&urWmloyed<br>4%(mubers)<br>conume31X|MNasers<br>seemto make more<br>follov-Lp<br>visits than<br>non-mtbers|

11
# **TABLE A26:  State Department / Amnesty International (HUM / PTS)**

## **A26: State Department / Amnesty International - Human Rights Database**

http://www.humanrightsdata.com http://www.unca.edu/politicalscience/images/Colloquium/faculty-staff/Gibney Doc/Political Terror Scale 1980-2004.xls We gather data from two different studies that have compiled a set of human rights indicators, drawing from the State Department's and Amnesty International's Human Rights Reports The State Department’s Country Reports on Human Rights Practices cover global human rights practices in the previous calendar years. Reports are generated through data gathered by the State Department from all of its embassies and representations throughout the world.

The Amnesty International’s Annual Reports cover global human rights conditions for the previous calendar years. Reports are based on information collected through Amnesty activists as well as from other sources such as media reports

**The Cingranelli-Richards (CIRI) Human Rights Dataset** contains standards-based quantitative information on government respect for 13 internationally recognized human rights for 192 countries. It is designed for use by scholars and students who seek to test theories about the causes and consequences of human rights violations, as well as policy makers and analysts who seek to estimate the human rights effects of a wide variety of institutional changes and public policies including democratization, economic aid, military aid, structural adjustment, and humanitarian intervention.

The **Political Terror Scale** (PTS) was originally codified by Prof. Marc Gibney of the University of North Carolina. The Index captures the reality of domestic political terror, capturing issues such as: imprisonments, tortures, rule of law, security, disappearances and executions.

In the table below we list the variables included in each of the governance indicators. We use data from the 1996, 1998, 2000, and 2002-2005 reports.

|**Table A26: State Department / Amnesty International(192 develope**|2005 <br>**d and devel**|2004 <br>**opin**|2003 <br>**g cou**|2002 <br>**ntrie**|2000 <br>**s)**|1998|1996|
|---|---|---|---|---|---|---|---|
|**Voice and Accountability**||||||||
|Restrictions on domestic and foreign travel (CIRI)|X|X|X|X|X|X|X|
|Freedom of political participation (CIRI)|X|X|X|X|X|X|X|
|Imprisonments because of ethnicity, race, or political, religious beliefs? (CIRI)|X|X|X|X|X|X|X|
|Government censorship (CIRI)|X|X|X|X|X|X|X|
|**Political Stability**||||||||
|Frequency of political killings (CIRI)|X|X|X|X|X|X|X|
|Frequency of disappearances (CIRI)|X|X|X|X|X|X|X|
|Frequency of tortures (CIRI)|X|X|X|X|X|X|X|
|Political terror scale (PTS)|X|X|X|X|X|X|X|
|**Government Effectiveness**||||||||
|NA|..|..|..|..|..|..|..|
|**Regulatory Quality**<br>||||||||
|NA|..|..|..|..|..|..|..|
|**Rule of Law**||||||||
|Independence of judiciary (CIRI)|X|X|X|X|X|X|X|
|**Control of Corruption**||||||||
|NA|..|..|..|..|..|..|..|
|_Country coverage_|192|192|159|159|159|159|159|

77
<!-- Start of picture text --> Food Share in Total Expenditure<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile<br>95% Confidence Interval OLS<br>RIF-OLS<br>.4<br>.2<br>0<br>-.2<br>-.4<br><!-- End of picture text --> 114