rafmacalaba/datause-encoder-data huggingface dataset:
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at connecting metropolitan areas. Banerjee et al. (2012) apply the same concept to the case of Chinese railways, calculating the distance from counties to straight lines connecting historic cities and ports, which can be arguably treated as exogenous. The validity of our proposed instruments will be examined using conventional test statistics, such as the exogeneity and overidentifying restriction tests, in the following section.

The summary statistics are shown in table 3. There were an average of 52.6 firms registered per district in 2016. There is significant variation in this. To avoid complexity related to another type of endogeneity, the number of existing firms in each district was measured using the 2015 data. On average, there were 320 firms in each district, with a range from none to more than 25,000 firms. As shown above, the RAI varies considerably from district to district. Similarly, other transport variables also differ from district to district. The market access index was normalized to a range from zero to 100.

Population density was included in the model to control for differences between districts, which was expected to capture the size of the local market. Firms are normally expected to be located near their customers, holding everything else constant. Labor costs may also affect firms’ decisions on where to locate. In theory, firms prefer to locate where wages are low, everything else being equal. Because wage data were not available, poverty and unemployment were included. Presumably, wages tend to be low where poverty and unemployment are high. At the same time, firms may also consider the quality of labor. Firms may want to hire skilled labor regardless of cost. To show this opposite effect, the share of population that attained at least a junior high school education ( _EDU_ ) was included. As discussed, informality of the economy may also be an important factor in firms’ decisions about where to locate.

To control for other unobservable subregional characteristics, five region-specific dummy variables are also included (north western (Bomi, Gbarpolu, Grand Cape Mount), north central (Bong, Lofa, Nimba), south central (Grand Bassa, Margibi), south eastern A (Grand Gedeh, Rivercess, Sinoe), and south eastern B (Grand Kru, Maryland, River Gee)), and
# Poverty lines

How to establish a relevant poverty line for FDPs is another important question. The choice is between international poverty lines such as 1.90 USD/day at PPP value (Ravallion, Chen and Sangraula, 2009), national poverty lines of host countries or contextual poverty lines specific to FDPs. International and national poverty lines can be considered after the use of appropriate deflators. These choices can be controversial for regular populations and carry additional complexities when FDPs are considered.

One question is how to adapt poverty lines for FDPs living inside and outside camps. FDPs in camps often receive shelter, education and health assistance that is not – or not in the same way – provided to the population residing outside camps. This can create a paradox that refugees that pay rents in urban areas would appear much richer than refugees living in camps as their shelter is free of charge and not necessarily accounted for in the consumption aggregate. In traditional poverty estimations, a similar challenge occurs when one considers poverty lines for urban and rural populations since the rental market is often absent in rural areas making it impossible to estimate or impute rent. To allow for the inclusion of rent for urban populations, separate urban and rural poverty lines are estimated, called contextual or regional poverty lines. Only the urban poverty line and its corresponding consumption aggregate would include rent.

Similarly, other services like education and health are extremely hard to value given that cost data for camps’ services are incomplete and grossly available only in aggregate form. Hence, they would also need to be excluded in the contextual poverty line for camps. Given the difficulty to estimate contextual poverty lines, however, this approach is usually not feasible in FDP contexts, while it also adversely affects the comparability of estimated poverty rates. Furthermore, a comparison between camp and non-camp population would ignore differences in housing, health and education between these populations, substantially limiting the value of a comparison. These questions are still largely unresolved and the search for poverty lines adapted to FDP contexts is still in its infancy.

# Comparisons across time and populations

Understanding how poverty among FDPs evolves over time and how this compares to other populations is another essential pillar of poverty measurement.

Tracking FDPs over time can be particularly helpful to monitor changes in livelihoods and changes in displacement status, location as well as the end of displacement.<sup>11</sup> Panel surveys in this respect are useful

> 11 While IOM uses the Displacement Tracking Matrix (DTM), it only provides an aggregated view of the location of FDPs.

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corresponding grade, while the assessments in primary school only cover a random sample of the students in the formal public sector, excluding those children attending private, cooperative, and municipal schools.

In this paper, we combine information from the enrollment database and the National Evaluations conducted in sixth and ninth grades in 2013 to follow the educational trajectories of the cohort of students evaluated that year. Specifically, we focus on the random sample of 19,000 students in public schools assessed in sixth grade (6.5% of the sixth graders in the sector, 5.7% of all sixth graders) and the census of 196,000 students in all schools evaluated in ninth grade. As discussed later, these are critical grades for school dropout in both Guatemala and Honduras. National Evaluations provide data not only on students’ reading and math abilities, but also on several selfreported characteristics including their motivation and interest in learning, parents’ characteristics (e.g. education, occupation), and household resources. While these data are quite rich, records are only complete for about 60% of the random sample, as many students do not fully complete the questionnaires – in all the analysis presented, we drop all incomplete records.<sup>5</sup> Because the analysis is done at the national level, we complement these data with information at the department level on the gender-specific high school wage premium from Guatemala’s national household survey, the _Encuesta de Condiciones de Vida_ (ENCOVI) 2014, as an attempt to capture some basic characteristics of the local labor market that may affect the decision to drop out of school. Table A1 in the Appendix presents the definitions of all the variables that are used in the analysis, as well as some descriptive statistics for the cohort of students who were evaluated in sixth and ninth grades in 2013.

Dropout rates calculated using the administrative enrollment data confirm that a large proportion of youth leave school before graduating from secondary school in Guatemala. In particular, we focus on three different periods in which dropout is particularly high: the transition from primary to lower secondary education ( _Ciclo Básico_ ), during lower secondary education, and the transition from lower to upper secondary education ( _Ciclo Diversificado_ ). Figure 2 shows the dropout rates in each of these periods for the cohort of students under analysis. Fully one-quarter of the students

> 5 In results that are not presented, multiple imputation techniques for the missing data are used, producing results that are highly consistent with those presented.

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# **Measuring Land Tenure at the Individual Level: Lessons from Methodological Research in Armenia**

Sydney Gourlay<sup>1†</sup> Giuseppe Maggio<sup>†</sup> * Anahit Safyan<sup>‡</sup> Alberto Zezza<sup>†</sup> JEL Classification: C8, J16, Q15 Keywords: land tenure; survey methodology; gender; Sustainable Development Goals; household surveys

> 1 Corresponding author: <u>s.gourlay@worldbank.org.</u> †Living Standards Measurement Study (LSMS), Data Production and Methods Unit, Development Data Group, World Bank, Rome, Italy. *Department of Law, Università di Palermo, Palermo, Italy.<sup>‡</sup> International Statistical Cooperation Sphere, State Council on Statistics of the Republic of Armenia, Statistical Committee of the Republic of Armenia, Yerevan, Armenia. This is a publication of the 50x2030 Initiative to Close the Agricultural Data Gap, a multi-partner program that seeks to bridge the global agricultural data gap by transforming data systems in 50 countries in Africa, Asia, the Middle East, and Latin America by 2030. For more information on the Initiative, visit 50x2030.org. The authors thank Heather Moylan and Jaap Zevenbergen for their valuable comments on an earlier draft, Raka Banerjee for her editorial contribution, and the Statistical Committee of the Republic of Armenia and the ICARE Foundation for their hard work in survey implementation. Any remaining errors are the authors' sole responsibility.
Figure 3 Summary Statistics of indicators used to predict consumption in survey-to-survey imputations, by GHS wave <!-- Start of picture text --> 90.00%<br>80.00%<br>70.00%<br>60.00%<br>50.00%<br>40.00%<br>30.00% GHS 2010/11<br>20.00% GHS 2012/13<br>10.00%<br>0.00% GHS 2015/16<br>GHS 2018/19<br><!-- End of picture text --> Note: the figure shows the average value of each indicator in each wave of the GHS. These non-monetary indicators are used to develop the consumption model used in the survey-to-survey imputations for each wave.

First, we present the results of the imputation into the 2018/19 GHS. We do this to verify that the NLSS and GHS surveys are indeed comparable and to check whether, using GHS data, we can replicate – or at least get close to – the official NLSS-based poverty headcount rate for 2018/19 of 39.1 percent.<sup>19</sup> The results show an imputed poverty headcount rate of 41.9 percent. The imputed headcount rate at the US$3.20 poverty line shows a similar difference of 2 percentage points (72.3 percent in the imputed results vs. the official estimate of 71.0 percent), while the imputed Gini coefficient is very similar to the official NLSS estimate (34.9 vs. 35.1). The small gap between the imputed GHS-based and actual NLSSbased poverty estimates can be explained by differences in some of the non-monetary variables used in the consumption model (reported in Table 2), which are likely driven by the two surveys having different data collection schedules. Yet, we argue that the difference is small enough and, in a context of limited data availability, this result lays reasonable pre-conditions to impute back into previous GHS rounds.

> 19 The imputation in the 2018/19 GHS should be considered as a check to the validity of the imputations in previous rounds. The official poverty estimate for 2018/19 should be calculated using NLSS data.

14
<!-- Start of picture text --> Figure 1: Poverty Rates in Cali, 1999<br>(Headcount Rates)<br>6<br>1 Km 2 5<br>4<br>1 7<br>3<br>8<br>9<br>12<br>13<br>14<br>20 19 11<br>10<br>16<br>15<br>18<br>Low: <20%<br>M iddle: 21% - 40%<br>17 High: 41% - 50%<br>V. High: 51% - 70%<br>Ext . High: > 70%<br>Source: Own calculations based on EPSOC (1999)<br><!-- End of picture text --> Figure 2: Distribution of Poverty, Cali, 1999 <!-- Start of picture text --> 6<br>2<br>1 Km<br>5<br>4<br>7<br>1<br>3 8<br>9<br>12<br>13<br>20 11 14<br>19 10<br>16<br>15<br>18<br>17<br>14 185,000 poor<br>16 65,000 poor<br>17 3,200 poor<br>Source: Ow n calculations based on EPSOC (1999)<br><!-- End of picture text --> 17
<mark>Table 2a: Transition Matrices - Synthetic vs. Actual Panel Data Chile 1996 and 2006</mark>

|<br>Status in 1996, 2006|<br>Lo<br>|<br>wer Bou<br>Estimate|<br>nd<br>s|<br>Truth|<br>Up<br>E|<br>per Bou<br>stimate|<br>nd<br>s|
|---|---|---|---|---|---|---|---|
||[1]|[2]|[3]|[4]|[5]|[6]|[7]|
|Poor,Poor<br>|7.46<br>|6.68<br>|5.35<br>|4.64<br>(3.19,6.07)|2.96<br>|2.65<br>|2.46<br>|
|Poor,Non-poor<br>|7.70<br>|10.35<br>|11.09<br>|19.59<br>(16.87,22.29)|25.39<br>|20.55<br>|21.78<br>|
|Non-poor,Poor<br>|0.13<br>|0.92<br>|2.25<br>|2.96<br>(1.79,4.11)|4.64<br>|4.94<br>|5.13<br>|
|Non-poor,Non-poor<br>|84.71<br>|82.06<br>|81.31<br>|72.82<br>(69.77,75.85)|67.02<br>|71.85<br>|70.63<br>|
|R-squared|0.21|0.31|0.43|.|0.21|0.31|0.43|
|Residual Correlation|0.26|0.22|0.22|.|0.26|0.22|0.22|
|Observations|823|823|823|823|823|823|823|
|Data source: CASEN, <br>Note: R-squared is cal|1996-200<br>culated f|6.<br>or oppo|site halv|es of the total 19|96 samp|le. Res|ults are|

<mark>Note: R-squared is calculated for opposite halves of the total 1996 sample. Results are constrained to the panel sample of households whose heads are between 25 and 65 years old. Results are weighted using household-level survey-sampling weights. Columns [1] and [5] show a simple model with household time invariant characteristics. Columns [2] and [6] add sub-national controls and region fixed effects. Columns [3] and [7] add interactions between household time invariant characteristics and sub-national controls. Results in column [4] show actual panel mobility. 95 percent confidence interval between</mark> <u><mark>parentheses. Upper bound estimations are based on 50 repetitions.</mark></u> 23
road transport. To identify a causal relationship, the study will use a quasi-experimental method, IV, where the treatment, defined as changes in transportation costs, will be instrumented by the distance between a firm’s location and a working station of the railroad.

In addition, the study exploits the fact that other transport corridors in Mozambique developed at different speeds and identifies two sets of control firms to match to the treated firms in the Maputo transport corridor: firms in the Beira corridor (that have access to a new port but no railroad) and firms in the Nacala corridor (that have no access to a new port or railroad). To isolate the impact of the Maputo railway rehabilitation, the study will use a matching DID estimation that assumes that the only factor making the trajectory of these three sets of firms different during the sample period is that they were exposed to different transport choice sets. The impact of the Maputo railway rehabilitation is not yet known since only the baseline survey information is available; a follow-up survey will be conducted in 2011.

Sequeira (2011) also discusses a "soft" transport infrastructure project focusing on corruption in Southern African ports.<sup>36</sup> By collecting original data on bribe payments made to customs officials and to port operators in the two competing ports of Durban and Maputo, the study is able to trace differences in bribe schedules to the organizational structure of each port. By observing how firms adapt their shipping and sourcing decisions to the type of corruption faced at each port — which enters the calculation of the overall cost of using each port — the study estimates the impact of corruption at ports on the behavior of South African firms. The estimates show that corruption imposes a distortion in terms of ―diversion‖ — i.e., firms travel on average an additional 322 kilometers, more than doubling their transport costs, just to avoid ―coercive‖ corruption at a port.

This effect is only observed for firms facing a higher probability of being coerced into a bribe because of the kind of product they ship. Firms are willing to incur higher costs to avoid corruption because of an aversion to the uncertainty surrounding bribe payments at the most corrupt port (Maputo). The uncertainty in Maputo seems linked to the short time periods caused by high job turnover among customs officials. Firms also respond to different types of corruption by adjusting their sourcing decisions for inputs — domestically or internationally — since corruption at ports increases the cost of using the port and thus directly affects the relative cost of imports.

While this project is not an impact evaluation of an intervention to reduce corruption in ports, it provides two sets of valuable insights on such interventions because it considers the entire chain between competing port bureaucracies setting bribes and user firms making shipping and sourcing decisions. First, the study shows that, depending on the type of corruption that

> 36 The project is described more extensively in Djankov and Sequeira (2010).

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# **3. Soundness of Kenya’s Banking Sector**

Since 2000 there has been a significant improvement in financial stability, as indicated by the financial soundness indicators in Table 3. The aggregate indicators, however, mask a significant variation across different ownership groups.

**Table 3.  Financial Soundness Indicators (%)**

||**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**2007**|**2008**|
|---|---|---|---|---|---|---|---|---|---|
|Regulatory capital to risk weighted assets|17.5|17.1|17.4|17.2|16.6|16.4|16.5|18.0|18.4|
|Regulatory Tier 1 capital to risk weighted assets|14.2|14.5|14.1|14.7|16.3|16.0|16.4|16.8|16.2|
|Non-performing loans to gross loans|37.2|39.4|39.6|34.9|29.3|25.6|21.3|10.9|8.4|
|Non-performing loans net of provisions to total<br>capital|78.7|78.8|77.8|60.7|52.7|40.1|28.6|15.1|10.8|
|Return on Assets|0.5|1.6|1|2.3|2.1|2.4|2.8|3.0|2.9|
|Return on equity|4.9|15.7|10.9|23.2|22.0|25.0|28.6|27.5|28.6|
|Net interest income to gross income|42.1|43.6|43.8|50.5|50.7|50.2|50.2|50.1|48.3|
|Non-interest expenses to gross income|65.4|58.6|66.9|62.9|63.9|55.7|52.8|50.8|48.7|
|Liquid assets to total assets|29.5|34.4|33.7|33.2|32.4|33.1|30.5|35.1|34.6|

Source: Central Bank of Kenya Banks are generally well-capitalized with an overall capital adequacy ratio of 18 percent comparable or considerably above that in other emerging economies and above the 8% recommended by Basel Core Principles (Table 4) **.** Liquidity ratios have been maintained above the minimum statutory requirements while earnings measures have improved steadily. Significant improvement in the stability of the banking sector has been reflected in asset quality. The banking sector experienced high levels of non-performing loans for many years averaging about 30% of gross advances before year 2003. However, the ratio declined significantly to stand at 8.4% as of December 2008. Notwithstanding this improvement, this ratio is still significantly higher than in other emerging markets as of 2007 (Table 4). A big proportion of the NPLs are concentrated in a few government-owned and –influenced banks as well as other adequately capitalized banks. Banks have passed significant provisions onto their books, thanks to strict prudential requirements, and hence the high level of NPLs does not pose a systemic threat to the Kenyan banking system.

8
of schooling and housing will be determined solely by _A_ 0 , and will be strictly increasing in _A_ 0 .<sup>17</sup> Thus we can treat _A_ 0 as the indicator of schooling and pre-reform housing.

The household chooses between family farming and supplying _S_ of labor (1 ≥ _S_ ≥ 0 ) to an outside activity (commercial farming or producing consumer durables). There is a return to wealth in farming, as represented by (twice-differentiable) functions of non-land wealth, _gi_ ( _A_ 0 ) , _i_ =0,1. Heterogeneity in ability at farming is represented by an i.i.d. random variable η . Own food output is _f_ ( _Li_ ) + _g i_ ( _A_ 0 ) +η ( _i_ =0,1), where the non-negative (twice-differentiable) function _f_ is taken to be non-decreasing and at least weakly concave. (Any income from renting-out land can be embedded in _f_ .) Note that _f_ is taken to be unaffected by the reform. Alternatively one can allow for higher output from land due to greater security of land-use rights.<sup>18</sup> While one could readily introduce this feature, we interpret this effect as being picked up by the<sup>_g_</sup> _i_<sup>functions.</sup> The functions _gi_ ( _A_ 0 ) can be interpreted in a number of ways: they can represent economic returns to schooling in farming,<sup>19</sup> or costs of non-land inputs ( _gi_ ( _A_ 0 ) can be negative for some _A_ 0 ),<sup>20</sup> or some other contribution of wealth to output such as through local tax-transfers or (liquidity constrained) access to credit to finance non-land inputs to farming, which we assume is only possible once land can put up as collateral. We assume that the reform does not decrease farmers’ marginal returns to non-land wealth. For the poorest, we also assume that the reform does not increase own food output at given land. More formally:

> 17 This follows from our assumption that utility is separable between food and consumer durables.

> 18 For supportive evidence see Deininger and Jin (2006) (using data for Ethiopia), who also refer to earlier literature, not all of which is supportive.

> 19 Van de Walle (2003) and Ngo (2004) provide evidence that schooling raises agricultural productivity in Vietnam. A more general model would allow for interaction effects with land holding.

> 20 At sufficiently high _A_ 0 some farm-households in the model will employ (presumably un-skilled) wage labor in farming. Given our focus on the poor we do not model this explicitly, but we note some implications along the way.

10