rafmacalaba/datause-encoder-data huggingface dataset:
Figure A.5: Annual Remittances (US$) Against Migrant's Annual Income (US$) _Source_ : Authors’ analysis based on data from QSKF <u>Table A.1: Comparision of QSKF and KMS</u>

||QSKF 2010|KMS 2011|
|---|---|---|
|Migrant's Age (years)|38.56|39.81|
||(7.62)|(8.33)|
|Years worked abroad|9.98|10.95|
||(8.53)|(8.40)|
|Migrant's annual earnings (US$)|4807.33|4639.75|
||(1799.63)|(3047.94)|
|Annual remittance received (US$)|2377.77|1845.03|
||(1827.44)|(1985.06)|
|Number of observation|108|1778|

Note: Standard deviations are in parentheses.

Source: Authors' analysis based on data from QSKF and KMS 2011 with a restriction to migrants working in the Arabian Gulf countries.

39
Table 8: Dynamic speci...cation <u>(dep</u> = ln (nt+1) � ln (nt))

||OLS|OLS|OLS|OLS|IV|IV|
|---|---|---|---|---|---|---|
|Constant|-0.028|-0.050|0.008|-0.014|-0.055|-0.055|
||(1.11)|(1.79)*|(0.37)|(0.57)|(1.54)|(1.52)|
|ln (nt)�ln(<br>nt<sup>~~d~~</sup>)|-0.131|-0.115|-0.122|-0.121|-0.118|-0.117|
||(5.34)***|(4.28)***|(5.43)***|(4.73)***|(5.25)***|(4.68)***|
|ln (p0;t)|0.021|0.029|0.015|0.019|-0.005|0.008|
||(3.05)***|(4.16)***|(1.54)|(1.67)*|(0.38)|(0.51)|
|ln (St)|-0.002|0.009|-0.014|-0.002|-0.012|0.003|
||(0.41)|(1.53)|(1.44)|(0.19)|(1.71)*|(0.36)|
|ln (Rt)|-0.008|-0.009|||||
||(1.89)*|(2.04)**|||||
|Partial Corr First Stage|||||0.326|0.334|
|F-stat First Stage|||||18.23|15.84|
|Hansen J Test (p-value)|||||0.179|0.169|
|Observations|146|119|192|153|184|149|
|R-squared|0.33|0.30|0.24|0.18|0.168|0.187|

Robust t statistics in parentheses signi...cant at 10%; ** signi...cant at 5%; *** signi...cant at 1% Instruments in IV estimations for ln(p0): island, ln(size), ln(distance to main destination) Table 9: Dynamic speci...cation with <u>predicted</u> bilateral migration stocks

||OLS|OLS|OLS|OLS|IV|IV|
|---|---|---|---|---|---|---|
|Constant|-0.045|-0.068|-0.019|-0.041|-0.089|-0.090|
||(1.70)*|(2.43)**|(0.85)|(1.65)|(2.17)**|(2.16)**|
|ln (nt)�ln(bn<sup>d</sup><br>t <sup>)</sup>|-0.118|-0.116|-0.124|-0.128|-0.124|-0.127|
||(4.98)***|(4.65)***|(6.00)***|(5.38)***|(5.32)***|(4.91)***|
|ln (p0;t)|0.017|0.026|0.012|0.017|-0.012|0.002|
||(2.35)**|(3.55)***|(1.22)|(1.53)|(0.82)|(0.16)|
|ln (St)|0.004|0.018|-0.004|0.009|-0.004|0.013|
||(0.50)|(2.56)**|(0.44)|(0.77)|(0.57)|(1.62)|
|ln (Rt)|-0.007|-0.008|||||
||(1.58)|(1.74)*|||||
|Partial Corr First Stage|||||0.314|0.320|
|F-stat First Stage|||||16.83|14.74|
|Hansen J Test (p-value)|||||0.203|0.287|
|Observations|145|118|188|151|182|147|
|R-squared|0.30|0.30|0.23|0.18|0.13|0.17|

Robust t statistics in parentheses signi...cant at 10%; ** signi...cant at 5%; *** signi...cant at 1% Instruments in IV estimations for ln(p0): island, ln(size), ln(distance to main destination) 32
identify the nature of relative capture in any given setting, in order to appraise the potential pitfalls of decentralization.” (Bardhan and Mookherjee, 2000, p.139).

Quantitative empirical evidence of elite capture of specific decentralized programs certainly exists, but it is not particularly abundant. Galasso and Ravallion (2005) find that the targeting performance of the Food for Education program in Bangladesh is worse in communities where land inequality is greater, and argue that this reflects greater capture of the benefits by the elite when the poor are less powerful. Examining India, Rosenzweig and Foster (2003) show that increases in the population weight of the poor result in increases in the likelihood of receiving pro-poor projects (such as roads) in villages with elected _panchayats_ , but not in villages with more traditional leadership structures, suggesting that local democracy matters for whether or not decentralization benefits the poor.

There are two principal reasons why empirical studies of elite capture of decentralized programs remain rare. The first reason is that income, consumption or wealth data that are representative at the level of disaggregation required for studying program incidence within villages are extremely rare. Most household surveys are not representative at that level. The second reason is that a key mechanism for capture is influence over the _type_ of expenditure to be financed from available resources – and this raises the difficult question of defining what types of projects benefit the poor more than others.

In this paper, we use a unique combination of data-sets from Ecuador to investigate whether there is any evidence consistent with elite capture in the pattern of project choice by communities that were awarded Social Investment Fund grants. Our data include reliable poverty and inequality estimates at the community level, obtained by combining high-quality information from a household survey with Census data. They also include administrative data on project approvals and expenditures by type and by community, for a period of almost three years. Finally, to account for relative capture visà-vis the central government, we also use province-level data on the results of the presidential election that predate the period under consideration.

Identifying elite capture in the context of Social Funds has proved hard because the Funds typically provide beneficiary communities with a menu of projects to choose 3
is the most important among the poor. The claim that consumers’ subsidies are a form of social protection schemes does not really hold if we observe data for LPG across countries.

Prices for electricity appear less diverse, but that can be explained by the way the prices are listed—in kilowatt hours (average across tariffs blocks). As a percentage of the free market price, electricity subsidies are the highest in Tunisia. The lowest subsidies are for Libya (30.6 percent) and Morocco (42.3 percent) but still high. To reach the market price, Libya would have to increase prices by 44 percent, an increase that would not go unnoticed by the population, and Tunisia would have to increase prices by 583 percent, a staggering figure.

Prices for gasoline and diesel are closer to the free market price for most countries except the Islamic Republic of Iran and Libya. The Islamic Republic of Iran and Libya in particular would have to raise prices of gasoline fivefold and more than sevenfold, respectively, to reach the free market price. For the Islamic Republic of Iran in 2014 this finding is remarkable given that this country went through a comprehensive reform of the subsidies system in 2010 that supposedly eliminated most subsidies and was costly in terms of cash transfers administered to the population in compensation of the removal of subsidies.

Table 2: Energy Unit Prices and Subsidies, in US$ at PPP (2014)

||Price|Subs.|Subs.<br>(%)|Increase<br>(%)|Price|Subs.|Subs.<br>(%)|Increase<br>(%)|
|---|---|---|---|---|---|---|---|---|
|||_LPG (1_|_3 kg) _|||_Electricity (_|_kWh, av.)_||
|Djibouti|28.3|2.8|9.1|10|||||
|Iran, Islamic Rep.|1.9|9.7|83.3|500|0.18|0.25|58.5|140.7|
|Libya|2.9|27.4|90.4|947|0.26|0.11|30.6|44|
|Morocco|10.4|20.7|66.6|199.8|0.21|0.15|42.3|73.2|
|Tunisia|9.8|20.9|68|212.7|0.11|0.63|85.4|583|
|||_Gasolin_|_e (L)_|||_Diese_|_l (L)_||
|Djibouti|3|-0.1|-2||2.1|0.3|11.1|12.5|
|Iran, Islamic Rep.|0.5|2.3|83.3|500|0.4|2.3|84.8|557.1|
|Libya|0.2|1.6|87.7|714.7|0.2|1.6|88.1|740|
|Morocco|3.1|0|0||2.4|0.2|7.5|8.1|
|Tunisia|2.5|0.2|9.1|10|2.1|0.4|17.4|21.1|

_Source:_ World Bank estimations from Household Budget Surveys.

_Note:_ PPP = purchasing power parity.

6
Table B8: Mechanism at play, excluding major source countries of foreign investment

|VARIABLES|(1)<br>CR5 (no GBR )|(2)<br>CR5 (no USA )|(3)<br>CR5 (no JPN )|(4)<br>CR5 (no DEU )|(5)<br>CR5 (no FRA )|(6)<br>CR5 (no CHN )|
|---|---|---|---|---|---|---|
|Uncertainty (standardized)|-0.189|-0.683|-0.231|-0.121|-0.191|-0.162|
||(0.603)|(0.616)|(0.607)|(0.553)|(0.603)|(0.597)|
|Uncertainty _×_ Developing|1.626**|2.220***|1.740**|1.641**|1.687**|1.712**|
||(0.705)|(0.721)|(0.708)|(0.656)|(0.709)|(0.712)|
|Observations|2,329|2,273|2,312|2,313|2,295|2,294|
|R-squared|0.831|0.818|0.828|0.827|0.828|0.832|

Notes: The unit of observation is the destination-year level, _it_ . “CR5” stands for the concentration ratio of the top 5 investing firms; countries with fewer than 5 foreign investors on average are excluded. “HHI” refers to the Herfindahl-Hirschman concentration index, calculated using the “CR5” sample. “Uncertainty” is based on the “T3” variable from the World Uncertainty database (Ahir et al. 2022), which counts the occurrence of the word “uncertainty” from Economist Intelligence Unit (EIU) reports for 143 countries since the 1950s. A higher value indicates greater uncertainty. Uncertainty in year _t_ is approximated as the logarithm of the average number of uncertainty-related words in EIU reports over the three years preceding the investment (standardized to have a mean of zero and a standard deviation of 1). The analysis includes country fixed effects and (world) region by year fixed effects. Countries are classified into 7 regions based on World Bank classifications. Destination-specific control variables include the logarithms of population and GDP (source: Conte et al. (2022)) and the average exchange rate over the three years preceding the investment (national currency/USD, Penn World Tables, mark 10.1, Feenstra et al. (2015)). Standard errors are clustered at the destination country level. Significance levels: *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

33
Figure 5: Major Violent Events and Corporate Line Firms: Event Study <!-- Start of picture text --> Response of Log Active Firms to Major Violent Events<br>-3 -2 -1 0 +1 +2 +3 +4 +5 +6 +7 +8<br>Months Since Major Violent Event<br>Including district and month FEs, district linear & quadratic trends. 90% confidence intervals shown.<br>.05<br>0<br>-.05<br>-.1<br><!-- End of picture text --> _Notes:_ Coefficients from regression of Log Active Firms on 3 leads and 8 lags of Major Violent Event (=1) with district and month fixed effects and district linear and quadratic time trends. 90% confidence intervals shown. See text for details.

31
be biased. Furthermore, the large number of zeros in the dependent variable leads to a truncated sample under log-transformation (Head and Mayer 2014). A common solution is to employ a Poisson Pseudo Maximum Likelihood (PPML) estimator that has the desirable properties of being consistent under heteroskedastic error terms and that naturally incorporates zero observations (Head and Meyer 2014). PPML can be used to estimate the parameters of a constant elasticity model in its multiplicative form and thus avoids the dropping of zeros. Santos Silva and Tenreyro (2006) further show that the PPML estimator leads to the same set of first-order conditions as an efficient and consistent non-linear least square estimator. For the PPML estimator to be consistent and efficient, it is therefore not necessary for the data generating process to follow a Poisson likelihood function.

Table 5: Gravity models of bilateral rescue lending: Full sample (1800-2015) and by era

|||<br>Dep. var|<br>iable: Bilateral rescu|<br>e lending||
|---|---|---|---|---|---|
||Full Sample<br>1800 - 2015<br>(1)|Pre-WWII<br>1800 - 1945<br>(2)|Bretton Woods<br>1946 - 1973<br>(3)|Modern Era<br>1973 - 2015<br>(trade linkages) <br>(4)|Modern Era<br>1984 - 2015<br> (banking linkages)<br>(5)|
|Trade exposure|0.34***|0.99*|0.32**|0.50***||
||(0.10)|(0.54)|(0.15)|(0.12)||
|Financial exposure|||||0.26***<br>(0.06)|
|Distance|-0.35**|-0.95**|-0.63**|-0.30*|-0.55***|
||(0.15)|(0.48)|(0.28)|(0.18)|(0.17)|
|UN voting|||6.60***|2.54**|5.44**|
||||(1.45)|(1.25)|(2.41)|
|(Former) colony|1.12***|1.37***|1.53***|0.69***|1.18***|
||(0.29)|(0.46)|(0.34)|(0.26)|(0.22)|
|Constant|9.67***|7.10|8.25***|0.67|-0.43|
||(1.26)|(4.63)|(2.65)|(1.53)|(1.92)|
|Observations|15429|449|1736|12042|1283|
|R-squared|0.825|0.997|0.921|0.730|0.961|
|Creditor FE||||||
|Debtor FE||||||
|Episode FE||||||
|Controls||||||

_Note:_ Poisson Pseudo Maximum Likelihood regressions using gross bilateral commitments during crises, wars and disasters as dependent variable (in real USD.) All explanatory variables enter as pre-crisis values. The models include creditor, debtor and disaster fixed effects. Robust standard errors are clustered at the creditordebtor dyad. ***, **, and * indicate statistical significance at the 1%, 5% and 10% level. See Table A2 in the Appendix for a list of included countries.

33
A key component of our empirical strategy relies on the distance of the household from the active fault-line and the epicenter of the earthquake. We obtained the digital mapping of the Himalayan Frontal Thrust from publicly available geological maps. We then calculated the linear distance to the fault-line and the distance to the epicenter using each household’s GPS location and using the Haversine formula.<sup>9</sup> These spatial data were supplemented with data on the average and maximum slope of the Union Council (a group of 4-5 geographically contiguous villages) to account for the hilliness of the terrain. Finally, we also complement these data with statistical village level data provided in the 1998 village population census to examine correlations between population characteristics and the intensity of the earthquake. We use these data, together with retrospective information from the household survey in 2009 to investigate pre-existing correlations between socioeconomic characteristics and earthquake intensity.

Table 1 provides descriptive statistics for the variables used in the study. There are 4670 individuals in our sample, of whom 49.8 percent (2324) are male and 46.5 percent have obtained some formal education. The average household was 18.8 Km from the fault-line and 38.6 Km from the epicenter; as expected, the bulk of the sample lies in the mountainous regions of the two provinces surveyed.

Particularly relevant are answers to the questions on trust. We asked the following question from one male and one female respondent within each household, typically the head of household and his/her spouse and coded the responses in a binary fashion as “yes” or “no”: _Imagine you are walking down a street and dropped a Rs. 1000 note without noticing. __________ was walking behind you without you knowing and picked it up. What is the likelihood that they would return it to you?_ This departs from the usual question asked in the World Values Survey (WVS) on trust: “ _Generally speaking, would you say that most people can be trusted or that you can’t be too_

> 9 Wikipedia has the clearest descrption: <u>http://en.wikipedia.org/wiki/Haversine_formula</u>

11
The covariate coarsening was determined by inspecting the histograms of the variables to see how communities naturally separated.

To minimize the threats from spill-overs, we only matched non-peacekeeping deployment communities that were relatively far away from any deployment communities. Given the difficulties of moving from place to place in Liberia, we felt that this would accomplish a lot. There is still the possibility that information about peacekeepers’ activities could spread, creating spill-overs over arbitrary distances. These kinds of spill-overs would push our estimates toward the null. Our analysis can be understood, therefore, as capturing the effect of the material presence of peacekeeping deployments, above and beyond whatever effects may have occurred due to diffuse knowledge of their presence in the country.

We used a simulation based power analysis to determine the sample size necessary for a household survey with at least 80% power to detect non-negligible treatment effects (e.g. 0.1 differences in proportions) with 95% confidence under moderate levels of intra-cluster correlation and treatment effect heterogeneity.<sup>14</sup> We determined that a sample of approximately 25 deployment and 25 non-deployment communities with sample sizes of about 15 respondent households per community would meet power requirements to estimate effect sizes of policy relevance. The precise number of communities was determined through the matching procedure, and ultimately we selected 29 peacekeeping and non-peacekeeping communities.<sup>15</sup> We limit the analysis to rural communities outside the capital of Monrovia. The reason for excluding Monrovia is that it is, due to population density and economic vibrancy, a case apart from the rest of the country. In each selected commu-

> 14The power analysis followed recommendations in Gelman and Hill (2007, 449-454).

> 15“Community” in this study refers to the administrative unit of the “clan,” which is the third tier administrative unit in Liberia below county and district, but above village. Clans contain clusters of villages that are linked on the basis of traditional ties, and therefore circumscribe domains of routine economic and social interaction. “Clan” in this context should not be confused with a family unit. It refers specifically to a geographic area. On average, a clan contains about 1,000 households (the average size for a household is between 5 and 6 people). In Monrovia, there are no clans, but rather administrative blocks that the Liberia Institute of Statistics and Geo-Information Services (LISGIS) has demarcated and that have approximately the same population as clans. LISGIS lists 673 clans and 165 administrative blocks, and these 838 units partition the entirety of Liberia’s territory.

xii
In Bamako, Gao, Timbuktu, Kidal and Mauritania baseline interviews took place between 4 and 23 June 2014. Baseline interviews in Niger were conducted from 1-10 August 2014.<sup>10</sup> Phone interviews started in August 2014<sup>11</sup> and are ongoing: for this paper we use those implemented till February 2015. The phone interviews were relatively short (they last about 20-25 minutes) and follow a pre-defined set of questions. Most questions were the same every month but some changed to capture new developments. Upon completion of the phone interview, respondents were rewarded with airtime credit worth approximately $ 2 (FCFA 1,000).

The baseline survey was conducted shortly after a violent incident in Kidal where on the 21<sup>st</sup> of May 2014 fighting broke out between the Malian army and rebel groups, which led to the latter retaking control of large parts of the north of Mali. The enumerator team reported that this event might have influenced people’s perceptions about the quality of the Malian army and their confidence in the government. Another issue that may have created biases is that refugees and IDPs may have answered strategically to certain questions hoping this would bring aid or opportunities. The age of the target population is not very exact since many respondents do not know their precise age, but relate it to historical events such as the 1973 drought.

**Table 1: Number of Responses and Response Rate**

||**Baseline**<br>**2014**<br>**Jun **|<sup>**Round**</sup><br>**1**<br>**2014**<br>**Aug**|**Round**<br>**2**<br>**2014**<br>**Oct**|**Round**<br>**3**<br>**2014**<br>**Nov**|**Round**<br>**4**<br>**2014**<br>**Dec**|**Round**<br>**5**<br>**2015**<br>**Jan **|**Round**<br>**6**<br>**2015**<br>**Feb**|
|---|---|---|---|---|---|---|---|
|IDPs|100|100|100|100|100|100|100|
|Refugees|181|176|176|178|181|179|181|
|Returnees|220|219|214|216|217|220|218|
|**Total (N)**|**501**|**495**|**490**|**494**|**498**|**499**|**499**|
|**Difference (n)**|**-**|**-6**|**-5**|**4**|4|1|0|
|**Response rate (%)**|**100**|**98.8**|**97.8**|**98.6**|**99.4**|**99.6**|**99.6**|

> 10 Due to difficulties with authorities in the camp in Burkina Faso (which was originally selected for the survey) had to be dropped and replaced by the camp in Niger. This delayed the fieldwork by a few weeks.

> 11 No phone interview was conducted in September 2014 during which the data collection team was processing data from the baseline survey and the first monthly phone survey in August. In the refugee camp in Mauretania cell phone coverage is too sporadic and interviews are carried out by an enumerator who relays the responses back to the call center.

6
<!-- Start of picture text --> x 10°<br>4 0 =<br>0.8 ——===== GovConsumption exp ~~05 Sor...) To=--=- OutputEmploy|"<br>é we 4 === = Invest<br>= 5-5<br>== 04 &= 2 | — —__-<br>a2 0.2 8-25a | ye -*<br>0 Metis 3 ‘2 =i ’ *. 7 : . ;<br>2 4 6 6 10 12 2 4 6 6 10 12<br>years years<br><!-- End of picture text -->

<!-- Start of picture text --> 3 7 r + . t -60.0914 2 r r t ' r 13<br>—— HH bond holdings —TB<br>----- Firm borrowing isi -----NFA<br>725 — ae -60.0916 4<br>B e B .<br>Fy & | \ 12<br>al =<br>f2 -60.0918 ayee eR RR Poem eS<br>715 ————S— -60.092 0 41<br>2 4 6 8 10 12 2 4 6 8 10 12<br>years years<br><!-- End of picture text -->
# **Figure 1 (continued). Full-sample, correlations between infrastructure indicators vs per capita GDP, PPP (constant 2005 int'l $)**

# **(a) Transport (continued)**

<!-- Start of picture text --> SGP<br>DEUHKG<br>BDI ETHMOZMWIMDGUGANPLMLIBFATZABGDGMBZMBBENLSOKHMKENTJKTCDMRTNGAKGZCMRVNMMDAINDPAKNICMNGPHLIDNHNDGEOAGOLKAMARAZEBOLCHNPRYGTMJORNAM EGY DOMSLVALBUKRCOLBIHJAMPERTUNECUTHADZAMKDKAZZAFBRACRIURYPANBGRROMVENMUSTURARGRUSMEXMYSCHLBWALVAHRVLTUPOLSVKESTHUNTTOCZEPRTKORISRSVNNZLTWNESPITAGRCFINJPNDEUFRAGBRSWEAUSBELDNKHKGAUTNLDCANCHEIRLUSASGPKWTARENOR ETHMOZMWIUGAMDGNPLBFATZAGMBZMBKHMKENTJKNGAKGZVNMMDAINDPAKNICMNGPHLIDNHNDGEOAGOLKAMARAZEARMBOLCHNPRYGTMJORNAM EGY DOMSLVALBUKRCOLJAMPERTUNECUTHADZAMKDKAZZAFBRACRIURYPANBGRROMVENMUSTURARGRUSMEXMYSCHLBWALVAHRVLTUPOLSVKESTHUNTTOCZEPRTKORISRSVNNZLTWNESPITAGRCJPNFINFRAGBRAUSSWEBELDNKAUTNLDCANCHEIRLUSAKWTARENOR<br>ARM BDI MLIBGDBEN MRT BIH<br>LSO CMR<br>TCD<br>6 7 8 9 10 11 6 7 8 9 10 11<br>log of real per capita GDP log of real per capita GDP<br>8 7<br>6<br>6<br>5<br>4<br>4<br>Quality of port facilities Quality of air transport<br>2<br>3<br>0 2<br><!-- End of picture text --> 11
Figure 7: Regional Level Education Deprivation a) b) <!-- Start of picture text --> Urban Educational Deprivation by Geopolitical Zone - GHS Rural Educational Deprivation by Geopolitical Zone - GHS<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>child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total<br>Wave 1_NC Wave 2_NC Wave 3_NC Wave 4_NC Wave 1_NC Wave 2_NC Wave 3_NC Wave 4_NC<br>Wave 1_NE Wave 2_NE Wave 3_NE Wave 4_NE Wave 1_NE Wave 2_NE Wave 3_NE Wave 4_NE<br>Wave 1_NW Wave 2_NW Wave 3_NW Wave 4_NW Wave 1_NW Wave 2_NW Wave 3_NW Wave 4_NW<br>Wave 1_SE Wave 2_SE Wave 3_SE Wave 4_SE Wave 1_SE Wave 2_SE Wave 3_SE Wave 4_SE<br>Wave 1_SS Wave 2_SS Wave 3_SS Wave 4_SS Wave 1_SS Wave 2_SS Wave 3_SS Wave 4_SS<br>Wave 1_SW Wave 2_SW Wave 3_SW Wave 4_SW Wave 1_SW Wave 2_SW Wave 3_SW Wave 4_SW<br>c) d)<br>Urban Educational Deprivation - by Geopolitical Zone - MICS Rural Educational Deprivation - by Geopolitical Zone - MICS<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>child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total<br>MICS 2016/2017_NC MICS 2016/2017_NE MICS 2016/2017_NW MICS 2016/2017_NC MICS 2016/2017_NE MICS 2016/2017_NW<br>MICS 2016/2017_SE MICS 2016/2017_SS MICS 2016/2017_SW MICS 2016/2017_SE MICS 2016/2017_SS MICS 2016/2017_SW<br><!-- End of picture text -->

# c)

Source: Authors’ estimates based on GHS Wave 1-4 and MICS 2016/17.

Note: Individuals with missing age were dropped from the analysis. Total refers to all individuals with known age.

The below Figures 8 and 9 show the existence of an enormous heterogeneity in educational deprivation between states. In Bauchi 50% of the children of the age group of 15-17 years, have never attended and are not currently attending school, whereas there are also states where close to 100% of the children have at some point attended school. It however also shows that at least some of the over 60% of children in Bauchi not in school when 5-9 years old, as Figure 8 displays, will have likely been in school at least at some point when reaching towards the oldest child age category.

21