rafmacalaba/datause-encoder-data huggingface dataset:
4% of returnees reported that their households were victims of the crisis,<sup>15</sup> compared with 14% of IDPs and 1% of refugees. Returnees thus not only experienced fewer losses in terms of life, consumer durables and livestock, they also left fewer people behind during the crisis, and reported the less trauma from the crisis: fewer victims in the household, neighborhood and/or ethnic tribe conveys a lower level of prosecution, trauma and mistrust and a lower level fear that might be conducive to returning home.<sup>16</sup> Looking at the sense of security returnees in Kidal feel less secure than returnees in Gao or Timbuktu (to offer a frame of reference, Figure 14 also includes responses by IDPs in Bamako to the security questions). 86% of returnees to Kidal feel very insecure or insecure at night (Figure 14). Even during the day, 64% of the returnees in Kidal feel very insecure or insecure compared to 31% in Timbuktu and 19% in Gao. During the day, the majority of returnees in Kidal feel very insecure or insecure at home (68%). The sense of insecurity is also very high in Gao at night, where around 83% feel either very insecure or insecure compared with 53% Timbuktu. During the day and at home at night, the majority of returnees feel secure or very secure in Gao and Timbuktu.

**Figure 14: Perceptions of security of returnees and IDPs, by region (June 2014) (%)**

<!-- Start of picture text --> How secure do you feel alone How secure do you feel alone How secure do you feel alone<br>at home during the day? in the street during the day? on the street at night?<br>2 5 6<br>31 34 28 32 28 17 14 18 24 14 8<br>18 43<br>10 55 24 52<br>33 44 42 64 58<br>41<br>54 32 30<br>54<br>29 18 21 24 14 23 20 26 23 34<br>7 7 8 4 6 4 9 10<br>Very insecure Insecure Very insecure Insecure Very insecure Insecure<br>Secure Very secure Secure Very secure Secure Very secure<br><!-- End of picture text --> Source: Listening to Displaced People Survey, 2014.

> 15 Victims of the crisis refers to death as well as physical violence.

> 16 Respondents in our survey have been exposed to less violence than the average population in northern Mali. Comparing our results to Afrobarometer findings, the latter finds that 15% of the population in Timbuktu and 8% in Gao reported death in their family as a result of the conflict. In our survey, the 5% of those originating from Timbuktu do so and 7% for those from Gao.

20
# **Table 1: Sierra Leone in Comparative Perspective**

|**Country**<br>**Name**|**Literacy**<br>**rate,**<br>**adult**<br>**total (%**<br>**of**<br>**people**<br>**ages 15**<br>**and**<br>**above)**|**Ratio of**<br>**young**<br>**literate**<br>**females**<br>**to males**<br>**(% ages**<br>**15-24)**|**Mortalit**<br>**y rate,**<br>**infant**<br>**(per**<br>**1,000**<br>**live**<br>**births)**|**Mortality**<br>**rate,**<br>**under-5**<br>**(per**<br>**1,000)**|**Improved**<br>**sanitation**<br>**facilities**<br>**(% of**<br>**populatio**<br>**n with**<br>**access)**|**Improved**<br>**water**<br>**source (%**<br>**of**<br>**population**<br>**with**<br>**access)**|**GNI**<br>**per**<br>**capit**<br>**a**<br>**(PPP)**<br>**const**<br>**ant**<br>**2005**<br>**(HD)**|
|---|---|---|---|---|---|---|---|
|Sierra Leone|40.9|71.1|113.7|174|13|49|737|
|Burundi|66.6|99.2|87.8|141.9|46|72|368|
|DRC|67.0|84.7|111.7|169.9|23|46|280|
|Mozambique|55.1|81.6|92.2|135|17|47|898|
|Rwanda|70.7|100.5|59.1|91.1|54|65|1133|
|Liberia|59.1|114.9|73.6|102.6|17|68|265|
|Guinea|39.5|61.7|81.2|129.9|53|19|863|
|**SL rank**|**6**|**6**|**7**|**7**|**7**|**4**|**4**|

_Source_ : World Bank (2012b) While the link is not easily established, it is generally accepted that in addition to its capability for policy making/coordination and regulation, the quality of ―upstream‖ public sector institutions has an impact on the ―downstream‖ performance of government on the provision of services and infrastructure (World Bank 2012a). Consequently, the poor status of the civil service – reflected in Sierra Leone‘s poor ranking on WGI‘s ̳Government Effectiveness‘ indicator - remains an issue of serious concern for development partners and has been often identified by national governments as an area requiring urgent reforms (World Bank 2012b). Over the years during and after the civil war, the public service in Sierra Leone experienced a progressive depletion of professional and technical staff at the middle and senior levels. This is starkly evident when comparing the composition of Sierra Leone‘s (core) civil service to other African countries. In Sierra Leone today, over 87 percent of the personnel are in the lowest (―blue collar‖) grades 1-5. While this is an improvement on the 92% three years ago, it is still very low. Top management grades represent only slightly over 1 percent of civil service employment. Professional and technical staff 5
Bertaud, Alain and Stephen Malpezzi, “Measuring the Costs and Benefits of Urban Land Use Regulation: A Simple Model with an Application to Malaysia,” _Journal of Housing Economics_ , 10(3), (2001): 393-418.

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21
12 private capital to SSA. To put this comparison into proper perspective, it should be noted, however, that the share of private flows in total resource flows to SSA is considerably lower than that for moat other developing regions.<sup>8</sup>

# Outstandino Stock

The decline in the importance of OSECs during the 1980s can also be observed from data on the outstanding debt of the developing countries (see Appendix Table 2). Appendix Table 3 gives the share of OSECs in total outstanding debt, and in public and publicly guaranteed debt of selected SubSaharan African countries.<sup>9</sup> Averages for these shares, which were respectively 17 and 24 percent in 1985, declined sharply in the latter part of the decade. In 1988, they ,ere 9 and 12 percent, respectively.

# New Commitments

While net flows (given in Table 1) continued to be negative, new commitments of OSECs to developing countries registered an upturn in 1988 and 1989, indicating that the post-1982 decline might be over (see Table 2)10. New commitments to developing countries which amounted to US$30.4 billion in 1987 increased by 8 percent in 1988, and a robust 23 percent in 1989.11 The 1988 a Computations based on Appendix Tables IA and 1B show that, for all developing countries, the share of private capital in total resource flows was well above 60 percent from 1975 to 1982, declining to about 30 percent in the late 1980s. In the case of SSA, the comparable figure was around 40 percent, and by 1984, private flows became negative.

9 The stock figures are from the Creditor Reporting System (CRS) of the OECD (Appendix Table 2), and the Debtor Reporting System (DRS) of the World Bank.

10 Data on new commitments are provided by the OECD, Secretariat of the Export Credit Group.

11 The growth rates reported in this Section are based on SDR values to exclude the changes in the US$ exchange rate.
# Figure 5. Macroprudential policy

# A. Macroprudential policy in EMDEs

<!-- Start of picture text --> Index<br>2013 2000 2007<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>EMDEs ECA EAP LAC MNA SAR SSA<br><!-- End of picture text -->

# C. Macroprudential tools

# B. Macroprudential tools

<!-- Start of picture text --> Average number of instruments 2017<br>5 2000<br>2007<br>4<br>3<br>2<br>1<br>0<br>EAP ECA LAC MNA SAR SSA<br><!-- End of picture text -->

# D. Macroprudential institutions in EMDEs

<!-- Start of picture text --> Percent Number of countries<br>0 20 40 60 80 100 70 2017 2016<br>Limits on foreign exchange positions, 60<br>banking sector 50<br>Capital conservation buffer 40<br>Limit on leverage ratio 30<br>20<br>Loan-to-value limits, hosueholds<br>10<br>FX limits: household credit 0<br>FX limits: corporate credit Macro- Housed in Macro-<br>Loan-to-value limits, hosueholds prudential Central Bank prudential<br>authority authorityʹs<br>Countercyclical capital buffer<br>powers<br><!-- End of picture text --> Source: Cerutti, Claessens, and Laeven (2017); IMF Annual Macroprudential Policy Survey; World Bank. Note: EMDEs = Emerging market and developing economies, EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, and SSA = Sub-Saharan Africa.

A. Sample includes 123 EMDEs. Unweighted average of the Macroprudential Policy Index of Cerutti, Claessens, and Laeven (2017). The Macroprudential Index measures the tools used by authorities and is based on a simple sum of 12 tools, including the countercyclical capital buffer and loan-to-value ratios.

B. Macroprudential instruments include counter-cyclical capital buffer, limits on leverage ratios and credit growth, capital conservation buffer, etc.

C. Based on data for 2017 in 98 EMDEs. Broad-based tools include the counter-cyclical capital buffer, limits on leverage ratios and credit growth, capital conservation buffer, etc.

D. Number of EMDEs that have an established macroprudential authority, that is housed in the central bank and has legislative powers to implement macroprudential policy.
_Table 4: Weighted average elasticities_

|**Instrument type**||**Weighted aver**<br>|**age elasticities**<sup>**4**</sup><br>||
|---|---|---|---|---|
||_New vehicles_<sup>1</sup>|_Fuel economy_|_Fuel demand_<sup>2</sup>|_Traffic volume_<sup>3</sup>|
|Purchase|-0.42<br>(0.080)|-|-0.16<br>(0.046)|0.00<br>(0.120)|
|Ownership|-0.09<br>(0.008)|-|-0.13<br>(0.033)|-0.09<br>(0.035)|
|Use<sup>4</sup>|-|0.12<br>(0.190)|-0.11<br>(0.005)|-0.04<br>(0.002)|

<mark>Standard errors in (parentheses).</mark> 1 <mark>Includes studies examining sales and new registrations.</mark>

> 2 <mark>Includes studies examining fuel demand, gasoline demand, and CO</mark> 2 <mark>emissions.</mark>

> 3 <mark>Includes studies examining traffic volume and vehicle miles travelled.</mark>

> 4 <mark>Fuel taxes only; congestion-related taxes not directly comparable.</mark>

Examining the weighted average elasticities, the empirical evidence broadly supports the hypotheses from theory with all elasticities appearing with the expected sign. However, the instruments on purchase and ownership appear to have larger elasticities than anticipated in several cases, particularly for fuel demand. It should be noted that the averages for instruments on purchase and ownership are supported by relatively few studies. Table 5 shows the number of studies used to calculate each average. While there are numerous studies examining use taxes, specifically fuel taxes, far fewer studies examine the effectiveness of purchase and ownership taxes. Notably, some cells do not have corresponding elasticities estimated. This is understandable for the elasticity of fuel economy with respect to purchase and use taxes, since these do not inherently affect fuel economy. Instead, numerous studies examine various feebate schemes, but there is not a standardized elasticity for comparison. For the elasticity of new vehicle sales with respect to use taxes, some authors estimate the impact of total spending on fuel, but this represents a combination of fuel economy and price. It is also worth pointing out, once again, that there are only a few estimates originating from developing countries with the vast majority of studies conducted in mature vehicle markets.

25
level of transport and telecommunications infrastructure development.<sup>30</sup> This analysis is motivated by the hypothesis that a country's effective integration into both its own region and the wider world economy will depend not only on its participation in formal trade agreements, but also on its accumulated level of investment in infrastructure that facilitates trade and other interaction. Although this applies for all economies, this is likely to be especially true for landlocked countries.

Infrastructure data comes from the World Bank's _Development Data Platform_ (DDP). Following Limão and Venables (2001), we use four indicators of a country's level of infrastructure development: (1) the density of roads (i.e. number of km of road per km<sup>2</sup> of land area); (2) the density of paved roads (km of paved road per km<sup>2</sup> of land area); (3) the density of railways (km of rail per km<sup>2</sup> of land area); and (4) the number of telephone main lines per capita.<sup>31</sup> We combine these four indicators into a single measure of a country's infrastructure development by first standardizing the observations on each indicator<sup>32</sup> and then taking the simple un-weighted mean of the non-missing observations across the four indicators for each country.<sup>33</sup> Negative values of the resultant index are associated with levels of infrastructure which are low by global standards, reflecting, _inter alia_ , the existence of limited road and rail networks. By contrast, positive values

> 30 For brevity, we simply refer to transport and telecommunications infrastructure as infrastructure in the remainder of this section.

> 31 For each country, these four indicators are themselves measured by their mean values over the sampleperiod (we ignore missing values in the calculation of means).  This raises some endogeneity concerns as a result of possible reverse causation from a country’s rate of growth to its level of infrastructure.  However, the results that we report in table 5 below remain essentially unchanged if we instead use start-of-period (i.e. 1992) values for the four indicators in the construction of _Infra_ .

> 32 For each observation _i_ on the infrastructure indicator _I_ , we standardize by applying the formula _Si_ = ( _Ii_ – _M_ )/ _s_ where _S_ denotes the standardized value, _M_ the sample mean across observations on that indicator, and _s_ the corresponding sample standard deviation.

> 33 This is equivalent to assuming that the four different types of infrastructure enter as perfect substitutes to a transport services production function (Limão and Venables, 2001, p 472).

26
# **A6.   Supplementary results**

The following tables report results and robustness checks not included in the main paper for brevity.

brevity. **A6.1 Robustness: Choice of initial Cobb-Douglas split** αα

**Table A2: Counterfactual change in women’s LFPR: Using barriers and preferences from 1995, 2005**

||Labor<br>on|market<br>ly|Human<br>on|capital<br>ly|Preferen|ces only|A|ll|
|---|---|---|---|---|---|---|---|---|
||1995|2005|1995|2005|1995|2005|1995|2005|
||(1)<br>|(2)<br>|(3)<br>|(4)<br>|(5)<br>|(6)<br>|(7)<br>|(8)|
|**Panel A: Labor for**<br>|**ce participatio**<br>|**n rate, diffe**<br>|**rence over**|**2015: wom**<br>|**en aged 25-**<br>|**34 (pp)**(α<br>|= 0. 0)<br>||
|Costa Rica|16.7|3.6|4.5|0.8|-24.2|<br>-12.7|<br>-19.0|-16.8|
|El Salvador|-6.7|-1.4|-0.5|-0.5|39.2|<br>34.4|<br>35.2|32.3|
|Panama<br>|20.6<br>|9.7<br>|9.4<br>|6.1<br>|8.3<br>|10.8<br>|10.0<br>|11.3|
|**Panel B: Labor for**|**ce participatio**|**n rate, diffe**|**rence over**|**2015: wom**|**en aged 25-**|**34 (pp)**(α=|0. 90)||
|Costa Rica|14.2|1.7|0.3|3.1|-22.6|<br>-11.6|<br>-12.3|-11.1|
|El Salvador|-6.5|-1.0|0.6|0.7|38.4|<br>33.4|<br>36.0|32.4|
|Panama|20.4|7.4|15.8|17.0|4.4|9.1|32.7|30.8|

**Table A3: Counterfactual change in men’s LFPR: Using barriers and preferences from 1995, 2005**

||Labor<br>on|market<br>ly|Human<br>on|capital<br>ly|Prefere|nces only|A|ll|
|---|---|---|---|---|---|---|---|---|
||1995|2005|1995|2005|1995|2005|1995|2005|
||(1)<br>|(2)<br>|(3)<br>|(4)<br>|(5)<br>|(6)<br>|(7)<br>|(8)|
|**Panel A: Labor for**<br>|**ce participatio**<br>|**n rate, diffe**<br>|**rence over**<br>|**2015: wom**<br>|**en aged 25-**<br>|**34 (pp)**(α=<br>|0. 0)<br>||
|Costa Rica|-2.7|-0.5|1.7|1.0|-1.0|<br>-2.2|<br>0.4|-0.6|
|El Salvador|0.2|0.0|0.1|-0.1|7.3|<br>6.4|<br>7.4|6.3|
|Panama<br>|-2.3<br>|-1.4<br>|2.4<br>|1.6<br>|0.9<br>|1.0<br>|2.2<br>|1.7|
|**Panel B: Labor forc**|**e participatio**|**n rate, diffe**|**rence over**|**2015: wom**|**en aged 25-**|**34 (pp)**(α=|0. 90)||
|Costa Rica|-2.9|-0.4|3.3|2.1|-0.5|<br>-1.9|<br>1.7|0.5|
|El Salvador|0.4|0.2|1.5|0.5|7.2|<br>6.2|<br>7.5|6.3|
|Panama|-2.4|-1.8|3.3|3.2|1.1|1.0|3.3|3.2|

46
2017, down to 16 percent from 24 percent, while medium firms represent a greater share and small firms have increased in share slightly. The biggest change in the sample has come in the location of the firms. Aleppo was nearly 19 percent of the sample in 2009, in 2017 it was just 9 percent. Damascus has increased from 42 percent of the sample to nearly 63 percent. Homs has fallen from 11 percent of the sample in 2009 to 6 percent of the sample in 2017.<sup>4</sup>

**Table 2: Comparison of 2009 sample with 2017 sample by location, size, and sector**

||**Frequency**<br>**2009**|**Frequency**<br>**2017**|**Percent**<br>**2009**|**Percent 2017**|
|---|---|---|---|---|
|**Total Operational**|508|160|100|31|
|**Location**|**Frequency**<br>**2009**|**Frequency**<br>**2017**|**Percent**<br>**2009**|**Percent**<br>**2017***|
|**Aleppo**|95|15|18.7|9.4|
|**Damascus**|213|100|41.9|62.5|
|**Hama**|42|16|8.3|10.0|
|**Homs**|56|9|11.0|5.6|
|**Lattakia**|45|20|8.9|12.5|
|**Damascus**<br>**Country**|32|0|6.3|0.0|
|**Missing**|25|0|4.9|0.0|
|**Total**|**508**|**160**|**100**|**100**|
|**Size**|**Frequency**<br>**2009**|**Frequency**<br>**2017**|**Percent**<br>**2009**|**Percent**<br>**2017***|
|**Small**|184|61|36.2|38.1|
|**Medium**|202|73|39.8|45.6|
|**Large**|121|26|23.8|16.3|
|**Missing**|1|0|0.2|0.0|
|**Total**|508|160|100|100|
|**Sector**|**Frequency**<br>**2009**|**Frequency**<br>**2017**|**Percent**<br>**2009**|**Percent**<br>**2017***|
|**Manufacturing**|349|108|68.7|67.5|
|**Services**|148|50|29.1|31.3|
|**Others**|11|2|2.2|1.3|
|**Total**|508|160|100|100|

* The percentages provided in these sections are of the 160 operational who were fully surveyed.

The above information also allowed us to generate a rate of exit for firms by size, sector, and location. In this survey in Syria in 2017 it was not possible to contact 203 of the firms, despite attempts to do so. When considering exit rates this raises the question of what to do with these firms: have they exited or are they just not contactable? We provide exit rates in a range with the “restricted” lower bound simply omitting uncontactable firms rather than assuming they exited and the “unrestricted” upper bound assuming uncontactable firms exited the market. This gives an overall exit rate for Syria of between 27.2 percent and 56.3 4 12 firms that were in 2009 listed as in Damascus countryside were not able to be reached, two were temporarily closed, and two permanently closed. The remaining 16 reported that they were working but only 12 reported they were currently operational, however, these 16 firms no longer listed Damascus countryside but instead Damascus. Though not verified, this may be due to a change in the manner in which questions were asked rather than a shift in location of the firms.

4
sectors).<sup>11</sup> Data on these variables are sourced from the World Economic Forum’s _Global Competitiveness Report_ . We also control for the presence of tariffs (sourced from WITS-Trains), in addition to standard geographical and historical factors (Mayer and Zignago, 2006). Our trade data come from WITS-Comtrade, and are disaggregated by BEC 1-digit sector.<sup>12</sup> We estimate the model over the period 2000-2005. (See Tables 5-6 for a description of our data, sources, and sample.)

# **_4.1 Model Specification_**

Initially used because of its explanatory power in empirical settings, the gravity model is now known to be consistent with a rigorous theoretical derivation. In this paper, we use the microfounded gravity model of Anderson and Van Wincoop (2003, 2004). It is now the standard approach taken in the trade literature.

From basic microeconomic principles, Anderson and Van Wincoop (2003, 2004) show that it is possible to derive a gravity-like model of exports from country i to country j in sector k at time t ( _X ijtk_ ):

> Where: _Yitk_ = Output of country i in sector k for year t; _E kjt_ = Expenditure of country j in sector k

> for year t; _Yt k_ = Aggregate (world) output in sector k for year t; σ _k_ = Elasticity of substitution in

> 11 For technical reasons, we have to take the average of our trade facilitation indicators across the importing and exporting countries. This is because importer- and exporter-specific measures, although time varying, are very strongly correlated with the time-invariant fixed effects we use to take account of market size and relative price effects. Estimation using separate measures of exporter and importer infrastructure does not produce meaningful results because of the strength of this correlation.

> 12 This is a very broad product classification, and is intended to give a first indication of potential cross-sectoral differences in the impact of trade facilitation measures.

- 13 -