rafmacalaba/datause-encoder-data huggingface dataset:
brought more credibility to the findings. The findings from Vietnam are used widely in the Voices of the Poor global studies and findings are quoted in the WDR2000.

Govenmment comments and feedback were taken into account in the final drafts of the PPA site synthesis reports. But Government involvement in drafting was really far more crucial in the national poverty assessment. This is the document which Government has indicated that they will take as their own and use for their own planning purposes.<sup>An</sup> abridged version of _Vietnam: Attacking Poverty_ (about half the length of the original report) is now being produced and will be adopted as an official document of the Government of Vietnam.

**_Diagram 2: Feeding PPA findings into national documentation and plans_**

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Shifting from inequality to relative deprivation provides additional insights into the urban-rural divide and shows the structural relation between the Gini index and the relative deprivation index proposed by Yitzhaki (1979). In Table 4, we report the Gini index together with the mean and the relative deprivation measure proposed by Yitzhaki. As shown by Yitzhaki (1979), the relative deprivation measure is equivalent to the absolute Gini index (the Gini multiplied by the mean) and, according to this measure, urban areas are more relatively deprived than rural areas.

**Table 4: Inequality and relative deprivation (per capita expenditures)**

||(1)|(2)|(3)|
|---|---|---|---|
||**FullSample**|**Urban **|**Rural **|
|Gini|0.352|0.331|0.326|
|Mean|937.3|1104.5|677.0|
|Relative Deprivation|328.1|362.8|219.8|

Source: MHYS 2009-2010. (*) Per capita expenditure are spatially adjusted for rural-urban price differences However, as shown in section 3, the Yitzhaki relative deprivation measure is not scale invariant and the observed difference across areas could be partly due to the difference in the means of the two samples. Indeed, as noted by Wodon and Yitzhaki (2009), changes in means are associated with changes in deprivation in the Yitzhaki measure. Moreover, the reference group in the Yitzhaki measure is defined as all richer individuals and there is no in-built mechanism for the selection of the reference group. Also, if one wishes to study deprivation that arises from labor market exclusion such as unemployment or inactivity, the binary status employed/unemployed or active/inactive cannot be used to estimate individual deprivation. All these considerations motivated our search for a labor deprivation measure.

# **5.2 Relative deprivation in labor space**

In this section, we shift from relative deprivation in the income space to relative deprivation in the labor space. In doing so, we also turn from the household dimension to the individual dimension of relative deprivation. This will allow us to be more precise on assumptions related to the relevant reference group for individuals and for inter-personal comparisons.

Table 5 reports descriptive statistics of labor market outcomes as well as the characteristics of individuals used in the estimations of relative labor deprivation in the paper. The statistics is limited to those individuals who can feasibly be in the labor market, i.e., to working age individuals who are not enrolled in school. In general, employment levels are very low in the sample with only 40 percent of the working age population being employed. Among young people aged 15-29, employment levels are considerably lower than the working age population as a whole, with only one in three people being 14
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# **Table 4**

# **Climate Regressions for Brazil**

|Independent||Dependen|t Variable: Cro|pland Value|in 1997|
|---|---|---|---|---|---|
|Variables|We|ather Stati|on<br>||Satellite|
||Temp|Climate|All|Temp|Climate All|
|Constant|-623.|1970|1760|6970|12300 8960|
||(1.01)|(3.06)|(2.88)|(5.39)|(8.07) (6.15)|
|January Temp|74.7|-12.8|-26.3|-100.|–121. 140.|
||(1.70)|(0.29)|(0.64)|(0.51)|(0.50) (0.61)|
|April Temp|49.2|-119.9|-112.|-646.|-930. -115.|
||(0.70)|(1.65)|(1.62)|(2.73)|(3.64) (4.68)|
|July Temp|48.8|-32.2|-61.2|626.|855. 654.|
||(1.15)|(0.71)|(1.42)|(7.79)|(9.23) (7.47)|
|October Temp|0.2|51.5|66.7|-261.|-738. -385.|
||(0.04)|(0.89)|(1.22)|(2.12)|(5.58) (2.95)|
|Jan Temp Squared|-1.95|0.87|0.50|2.11|3.63 -1.94|
||(1.87)|(0.83)|(0.51)|(0.46)|(0.65) (0.37)|
|Apr Temp Squared|1.00|3.18|3.71|14.6|21.2 26.8|
||(0.62)|(1.85)|(2.26)|(2.64)|(3.49) (4.62)|
|Jul Temp Squared|-0.58|1.64|2.22|-13.9|-22.7 -16.1|
||(0.53)|(1.38)|(1.97)|(6.34)|(8.87) (6.62)|
|Oct Temp Squared|-2.76|-3.18|-2.97|2.4|16.5 7.6|
||(2..13|(2.37)|(2.40)|(0.81)|(5.12) (2.41)|
|January Precipitation|...|2.19|0.98|...|-85.6 53.3|
|||(4.92)|(2.23)||(1.63) (1.06)|
|April Precipitation|...|-1.46|-1.54|...|6.1 -77.4|
|||(2.59)|(2.87)||(0.11) (1.51)|
|July Precipitation|...|1.66|1.62|...|-24.9 74.6|
|||(3.68)|(3.75)||(0.63) (1.94)|

49
# **I. Introduction**

After two decades of multiparty democracy, Mali was viewed as a democratic success story. The fifth presidential elections were scheduled to take place in March 2012 and another peaceful and democratic transfer of power was widely anticipated. Reality was different, however. A secessionist movement sparked by a Kel Tamasheq<sup>1</sup> rebellion led to a political and constitutional crisis culminating in a coup d’état in March 2012 and an attempt to take over the country by force. The three northern regions of Gao, Timbuktu and Kidal became occupied by various rebel and Islamist factions until early 2013, when a coalition composed of the Malian Army, French troops and the ECOWAS-led African-led International Support Missions to Mali (AFISMA) recaptured the occupied areas.<sup>2</sup> After months of insecurity in the North and two violent attacks in Bamako, a Peace Accord was signed in May and June 2015 between the government and different actors involved in the rebellion. The Accord established a joint vision for peace and prosperity predicated on demobilization and disarmament, the devolution of authority to local governments, and the establishment of conditions for restoring stability and economic recovery in northern Mali. In spite of the Accord, the regions of Gao, Kidal and Timbuktu remain in a state of prolonged crisis, with high levels of insecurity and weak governance. Without army protection most parts of the North, especially Kidal remain inaccessible to those working for the central and local government.<sup>3</sup> Armed bandits are active and IED explosions as well as violent attacks on the MINUSMA peacekeeping forces are regular occurrences.

Under these circumstances, data collection is very difficult. INSTAT, the National Bureau of Statistics, has not been in a position to collect information from northern Mali since the beginning of the crisis. To our knowledge, our surveys implemented by a private survey entity, GISSE, are the only systematic and representative effort to collect data in north Mali since the crisis. They offer a unique database providing a crucial perspective that would otherwise not be reflected in academic analyses and policy level decision-making, and a perspective that is indispensable in any attempt at understanding the situation in Northern Mali.<sup>4</sup> Preceding the Accord on Peace and Reconciliation in Mali ( _Accord pour la paix et la réconciliation au Mali_ ) (hereafter, the Peace Accord) of May and June 2015, four peace accords had been signed between the government and Toureg and Arab armed groups in

> 1 Kel Tamasheq (those who speak Tamasheq) is synonymous for Tuareq.

> 2 Francis David (2013): The regional impact of the armed conflict and French intervention in Mali. NOREF, Norwegian Peacebuilding Resource Centre.

> 3 Assessing Recovery and Development Priorities in Mali’s Conflict-Affected Regions. Draft Report of the Joint Assessment Mission for Northern Mali (January 2016), p. 15-16.

> 4 All data can be downloaded from <u>www.gisse.org.</u>

2
1.

,1 ,

|||||Share of t|otal expor|ts destined f|or (percenage|)||_|
|---|---|---|---|---|---|---|---|---|---|---|
|||||of which:|||||o|f which:|
|Exporting Country|World<br>($million)|Developed<br>Countries|EEC(12)|Northi<br>Anmerica|Japan|Eastern<br>Europe|Socialist<br>Asia|Developing<br>Countries|Africa|South-East<br>Asia|
|SUB-SAHARA AFRICA|25,455.6|77.7|47.4|24.4|2.8|2.4|0.6|17.2|9.8|0.3|
|Angola<br>|1,296.4|81.5|34.5|45.0|-|0.3|-|18.0|1.8|0.6|
|Botswana|na|na|na|na|na|na|na|na|na|na|
|Cameroon<br>|924.0<br>|67.5|60.3|5.4|1.2|4.1|-|28.5|22.3|2.0|
|Central African Rep.<br>|135.0<br>|69.5|65.3|2.3|0.6|-|1.5|23.9|9.9|10.6|
|Chad|141.0|32.2|31.3|-|-|-|-|67.8|60.6|1.4|
|Congo<br>|776.9<br>|65.6<br>|31.5<br>|33.3|0.5|0.7|-|33.6|1.4|1.4|
|Cote d'lvoire|2,906.0|64.4|54.4|8.6|0.7|5.3|0.1|25.0|22.8|1.7|
|Ethiopia|358.0|82.3|59.3|13.6|8.0|6.9|0.2|10.5|3.2|0.4|
|Gabon<br>|1,429.0|68.2|50.5|12.7|1.7|1.9|_0.5_|25.6|6.4|8.6|
|Ghana|1,014.0|79.9|40.8|24.6|9.0|11.6|0.1|8.4|1.6|4.3|
|Guinea<br>|na<br>|na<br>|na|na|na|na|na|na|na|na|
|Kenya<br>|961.0|64.1|47.0|10.6|0.6|0.1|0.4|31.4|22.7|5.4|
|Liberia|404.4|96.9|77.9|17.9|0.9|-|-|3.1|2.3|0.2|
|Madagascar<br>|316.6<br>|84.9|58.2|15.4|10.9|3.6|1.9|8.8|3.7|5.0|
|Malawi|286.9|78.3|40.3|13.8|8.7|-|-|18.8|16.1|1.3|
|Mali<br>|192.0<br>|34.7|28.0|3.3|2.2|28.5|0.2|34.0|27.8|6.1|
|Mauritania<br>|427.8<br>|72.6|44.2|1.4|27.0|15.0|0.3|10.1|9.6|0.5|
|Mauritius|997.5|93.2|76.5|15.0|0.2|0.4|-|5.1|3.7|1.3|
|Niger<br>|91.2<br>|70.0|66.6|2.6|0.7|-|_|29.9|29.9|-|
|Nigeria<br>|7,382.6<br>|91.2<br>|41.5|49.1|0.1|-|_|8.8|6.1|0.1|
|Reunion|160.8|89.6|85.8|0.2|2.7|-|-|10.4|10.2|-|
|Senegal<br>|606.3<br>|47.1|45.1|0.4|1.2|-|0.1|29.8|18.9|5.8|
|Sierra Leone|148.0|74.7|65.0|9.3|-|0.7|-|9.0|2.(|0.3|
|Sudan<br>|509.1|39.5|28.6|3.7|6.1|8.6|0.5|51.4|8.6|21.0|
|Swaziland|na|na|na|na|na|na|na|na|na|na|
|Togo|245.1|43.7|12.6|0.9|0.9|6.4|0.3|31.0|14.0|13.7|
|Uganda|436.0|85.2|51.8|28.0|3.6|0.8|-|13.9|10.6|1.4|
|United Rep. Tanzania|346.0|79.0|64.1|3.3|5.3|0.7|1.8|18.2|4.2|11.5|
|Zaire<br>|1,120.0<br>|85.6<br>|57.7<br>|21.7|_5.1_|0.8|3.5|8.8|3.4|2.4|
|Zambia|425.0|75.7|36.0|9.1|27.1|-|12.5|11.7|1.0|5.8|
|Zimbabwe|1,419.0|78.4|40.3|6.1|_5.7_|2.1|2.5|16.9|11.6|3.4|

Source: UNCTAD, Handbook of Iternational Trade and Develooment Statistics, (Geneva: UNCTAD) and UN COMTRADE records. Staistics for Guinea were not available and expons from Botswana and Swaziland are recorded in totals for the South African Customs Union. Several smaller countries like C-imbia were excluded from dhe above due to the low value of their exports. The percenages may not sum to 100 since about 3 percent of Sub-Sahara Africa's exports are not allocated by destination.
Upands: Buy-Back of Commorcia Debt 7

# Uganda: External Debt Profile

<!-- Start of picture text --> Othr 24%<br>IMF 15%<br>Elatetal<br>269; _ ._ Multiai<br>VeNd Rank 61%<br><!-- End of picture text --> undertake an exhaustive inventory of Uganda's external debt by individually contacting every major creditor; and (ii) assist the Governmenmt in developing a viable extemal debt strategy. At the same time, the World Bank was formally requested to collaborate closely with the oDnsultants and the Government's team within the central bank and the Ministry of Finance.

## Developing an External Debt Strategy

An analysis of the composition and profile of Uganda's DOD and debt service obligations revealed the extremely difficult external financial position that the country found itself in. First, it became abundantly clear from the high debt-service ratio that the existing level of foreign exchange earnings of the economy was insufficien **t** o service the stock of outstanding debt. This was notwithstanding the fact that most recent debt had been contracted on highly concessional terms, with the average interest for new commitments in 1992 of 1.7 percent and
one of the war intensity measures. The specifications also include the interaction between the war intensity measure and the young cohort indicator, the interaction of the war intensity measure and the 2000 DHS data indicator, and the interaction of the young cohort indicator and the 2000 DHS data indicator. All regressions also include province and child age fixed effects as well as child gender, poverty status, an indicator for the dataset, and the same household control variables used in the previous tables. In column 1 of Table 7, the dependent variable is the years of education achieved by the individual. For all three war intensity measures, the coefficient on the triple difference variable is negative and significant at the 10 percent level. Increasing by one standard deviation the proportion of days during which killings occurred (mean proportion is 12.4 percent, standard deviation is 6.2) implies a decline of 0.15 years of schooling based on the coefficient in column 1 using Measure A. Using Measure B and moving a child to one of the three provinces that experience the most genocide killings is correlated with a drop in schooling for the exposed children of 0.33 years of school. Finally, using Measure C, an increase of one standard deviation in the number of mass grave sites and memorials in a province (mean number is 9.6, standard deviation is 6.2) implies a drop of 0.14 years of schooling. The results indicate that the negative impact on educational outcomes was stronger in the provinces where the intensity of the genocide was higher, suggesting that the substantial negative shock to schooling levels observed between 1992 and 2000 could be directly due to the 1994 genocide.

In Table 7 columns 2 to 7, we provide further detail by examining the relationship between war intensity and the probability to complete grades 1 to 6. Each cell in the table represents a separate regression, with the grade listed at the top of the column and the coefficient for the triple difference variable reported in the table. Every regression includes the control variables previously discussed. The coefficient on the triple difference variable is always 16
Figure 6. Romania. Distributional Impact of the Tax and Benefit System in 2016 <u>(benefit/burden as a share of market income + pensions)</u> <!-- Start of picture text --> 140%<br>120%<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>-20%<br>-40%<br>Poorest 2 3 4 5 6 7 8 9 Richest<br>All direct taxes Non-pension contributions All Direct Transfers<br>Indirect subsidies All indirect taxes Education<br>Health Net cash position Total<br>Share of Market Income + Pensions<br><!-- End of picture text --> Source: Own estimates based on the 2016 Romanian HBS.

The Net Cash position of the household is measured as the difference between consumable and market income plus <u>pensions, and is equivalent to all the cash transfers to households minus all taxes.</u> Since the influence of specific interventions may be different from that of the overall system, a fundamental question in the policy discussion is whether specific fiscal instruments (or a combination of them) are equalizing. If there were a single fiscal intervention, using the typical indicators such as the Kakwani index<sup>16</sup> to determine whether an intervention is progressive or regressive would be sufficient to unambiguously determine whether that intervention was equalizing. Given there is more than one fiscal intervention, this one-to-one relationship between the progressivity of an intervention and its effect on inequality breaks down. For instance, the proceeds of a regressive indirect tax _could_ be used very effectively in a pro-poor transfer, leading to a situation where post-fisc incomes are more equal than in the absence of that regressive tax (Lambert, 2001). One way to calculate the effect of each fiscal instrument on inequality is to calculate its marginal contribution. The marginal contribution of a tax (or transfer) is calculated by taking the difference between the inequality indicator _with_ and _without_ the tax (or transfer).<sup>17</sup> Table A2.1

> 16 The Kakwani index for taxes is defined as the difference between the concentration coefficient of the tax and the Gini for market income. For transfers, it is defined as the difference between the Gini for market income and the concentration coefficient of the transfer. See, for example, Kakwani (1977).

> 17 Note that there is path dependence in estimating these marginal contributions, since the order in which each intervention is considered matters for the size of the estimated marginal contribution. The estimation approach uses

17
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ception of those living in very inaccessible areas, which is the case for less than 0.4% of the population) and is conducted every three years by the Philippine Statistics Authority (PSA). The sample size in each round is about 50,000 households. It is the only survey data source that allows for the estimation of consumption level and poverty status. It has the downside of not capturing much information on individual household socio-economic variables other than income and consumption. The empirical specification discussed below is, therefore, very parsimonious. The Philippines consist of 17 regions, subdivided into 81 provinces which, in turn, are composed of 1,634 cities and municipalities. The lowest geographic level are barangays (villages), of which the country has a total of 42,046. All four levels can be identified in FIES. For the data used in this study 1,142 of the 1,145 municipalities present in the 2012 round were resampled in 2015 (plus an additional five not present in 2015). However, at the barangay level only 1,641 of the 2,397 present in the 2012 round were resampled in 2015 (and 885 new ones added). This has obvious implications for the choice of fixed effects.

Below, results will be presented for a variety of different samples, defined by varying distance to the typhoon path and the exclusion of higher income households. As explained above, in theory only households that have at least one child under age 18 and fall under their respective provincial rural/urban poverty line qualify for the program. But limiting the sample to poor households poses two problems: Firstly, they would be able to keep the benefit for 13
The CIRI database contains three separate measures of women’s rights – economic rights (variable “wecon”), social rights (variable “wosoc”) and political rights (variable “wopol). As for our other country level variables, we use average values of the CIRI variables taken over 2005 to 2009. Before proceeding to the results for the CIRI indices, we confirm that all our main results for the _Non-discrimination_ variable discussed in the paper survive even if we control for the CIRI measure of women’s economic, social and political rights (simultaneously or individually in separate regressions) with the exception that the interaction term between female population and _Non-discrimination_ variable becomes insignificant at the 10 percent level in some of the specifications when we control for the CIRI indices (separately or simultaneously). Regarding estimation results for the CIRI indices in place of our _Non-discrimination_ variable, we focus here on the measure of women’s economic rights as this seems most appropriate for our purposes; results for the other two CIRI variables are mentioned very briefly in what follows. The CIRI measure of women’s economic rights is highly correlated with (log of) GDP per capita– correlation coefficient of 0.59 in our sample (compared with a correlation of 0.33 between GDP per capita and our _Non-discrimination_ variable). This is not surprising given that unlike our _Nondiscrimination_ variable, the CIRI measure is a broad measure and therefore highly correlated with the broader macro environment as captured by GDP per capita.

Base regression results using the CIRI measure for women’s economic rights are provided in Table 8. The CIRI variable for women’s economic rights does show a sharp positive correlation with female employment that is economically large and statistically significant at less than the 1 percent level (column 1). However, this positive relationship between the CIRI measure and female employment is weak in the sense that it is not robust to basic controls such as GDP per capita (column 2) or other macro level variables such as the level of education among females or the education gap between males and females (columns 3 and 4). Results for 20