rafmacalaba/datause-encoder-data huggingface dataset:
do happen in peripheral regions, but the analysis indicates that the picture of African internal conflicts as primarily rural events is inexact. The risk of conflict in a location depends on the value of the location. Here we have proxied the value of the location by the population that resides within it.

Although the effect of distance is moderate relative to population concentration, the results still indicate that countries with populations that are largely concentrated around the capital have fewer internal conflict events than countries with populations that are spread out, or, even more strongly, are also concentrated in locations far from the capital.

Our results might contribute something to the discussion on the usefulness of partition to solve internal armed conflicts (see Kaufmann, 1996 and Sambanis, 2000 for a discussion). The relatively small importance of distance from the capital relative to population mass indicates that partition might not be a very effective solution, in the sense that the total risk of conflict events in the two separated countries will only be marginally smaller. However, territories with large and clearly separated population concentrations might conceivably have perceptibly lower risks of conflict events as two or more countries surrounding each concentration than as one `difficult' political geography (our analysis does not say anything about the risk of interstate war between such entitites, however).

The results for other variables in the model are mainly consistent with our expectations and previous studies such as Buhaug & Gates (2002) and Buhaug & Rød (2006). One exception is the weak effect of distance from the capital in our analysis relative to theirs. This may be explained by their choice to use the distance from the capital to what they define as the geographical center of the conflict area as the distance measure. We, in contrast, use the distance from the capital to each individual conflict event. A more likely explanation for the difference in finding may be that they include data for all of Africa whereas we only analyze Central Africa. In particular, the conflicts in densely populated Rwanda and Burundi weigh more heavily in our analysis than in theirs.

This paper is a first in a series of papers that seek to retest empirical hypotheses using geographically disaggregated data. Up to now, with the exception of the work of Buhaug and associates and some country studies, quantitative studies of civil war have 33
versions of the program have been tried since then, Trabajar I, II and III. In terms of design, Trabajar II (TII) and III are more similar to each other than either are to Trabajar I (TI). There were substantial design changes between Trabajar I and II. The inter-provincial allocation of spending was reformed, moving away from a largely political process to an explicit formula based on the estimated number of poor unemployed workers in each province. TII also put greater emphasis on creating assets of value to poor communities. Poverty measures were included in the center's budget allocation rules and in the selection criteria for sub-projects. The poverty focus was also made clearer to provincial administrators. TIII was very similar to its predecessor in design. The main difference was that greater emphasis was placed on the quality of sub-projects, to assure that the assets created were of value to the communities. The World Bank has supported TIl and TIII by loans (disbursed against the wage payments), and through technical support on program design, monitoring and evaluation. All results quoted for Tlll in this paper relate to the first 16 months of its operation, up to November 1999. (TIII is ongoing at the time of writing, and is scheduled to finish in mid 2000.) From the point of view of this paper, an important difference between the three versions of the program is in the level of funding. In Trabajar I, disbursements by the center (covering wages for participating workers) averaged $77 million per annum; for TIl this rose to $160 million per annum, and it then fell to $98 million per annum under TIII. As we will see, there were also differences in levels of funding between sub-periods.

Survey-based impact evaluation methods have been used to assess the gains to participating workers and their families from TIl and TIII. Propensity-score matching methods were used to construct a comparison group to surveyed Trabajar participants from an identical national sample survey implemented at the same time. Income gains were then estimated by comparing incomes of the Trabajar participants with the matched comparison group. The results have indicated that Trabajar jobs are well targeted to the poor; Figure 2 14
# **Increasing but Uneven Trade Integration**

<!-- Start of picture text --> Figure 16. Distribution of MICS by Annual<br>Growth Rate in Trade Share of GDP,<br>1988-98<br>209<br>15<br>r '5<br>o0 9<br>5<br>**<br><0 0-4 4-8 >8<br>Percentage mean groth rates, 1988-98<br>Source World Bank data.<br><!-- End of picture text --> 47. The economies of MICs became more **Growth Rate in Trade Share of GDP,** outward-oriented in the 1990s. The trade **1988-98** share of GDP in these countries grew **209** annually at an average rate of 3 percent in **15** 1988-98, a rate similar to that of high-income r '5 countries. Trade expansion was, however, o0 **9** very uneven across the MICs and **5** concentrated in specific regions (Figure 16). ** Eight of the thirteen countries with the fastest <0 0-4 _4-8_ >8 increase in openness were in Latin America, Percentage **mean groth** rates, 1988-98 all with annual growth in their GDP trade _Source_ World Bank data. shares of above 5.3 percent. Eight of the thirteen countries with the slowest increase in openness were in Africa and the Middle East, each with annual growth in trade shares of below 0.2 percent and some with negative growth.

48. VWhile the MICs have made **Figure 17. Trade Restrictiveness Index, 2000** substantial progress in reducing trade 60 barriers and in general have less 50 *MIC * LIC restrictive trade regimes than low-income countries (Figure 17), trade-related 4 reforms remain an important agenda item 3 0 in many of these countries. First, almost **af** 20 one-third of MICs still have high trade *_ restrictions (Figure 17). Second, in countries that have substantially reduced **0** 1-2 3 **4** 5 trade barriers (tariffs and non-tariff Trade Restrictiveness Index barriers), there is a sizable unfinished (2001) agenda of complementary reforms to **_Source_** HeritageFoundation(2001) maximize the benefits from trade liberalization and enhance its impact on growth. These complementary, "second-generation" reforms include, for example, improvements in domestic competition policies, strengthening of trade-related institutions, modernization of product standards systems and conformity assessment infrastructure, reform of traderelated finance, and promotion of trade in services.

49. Volatility in terms of trade (TOT) remains important for a number of MICs. The MICs faced on average a TOT decline of 0.55 percent annually during 1988-98 (Table 4). While this is less than the I percent annual decline faced by low-income countries, it contrasts with the stable TOT faced by high-income countries. The MICs lost on average 0.4 percent of GDP annually as a result of TOT decline. The average TOT deterioration in the MICs was relatively large in this period, since the oil price was exceptionally low in 1998 and the MICs include several oil exporters (e.g., Venezuela, Mexico, Algeria, Nigeria, Gabon, Trinidad and Tobago), although several MICs in Central and Latin 17
<!-- Start of picture text --> fan)<br>m (NN All HH tracked (N)<br>HE HH intensively tracked (N)<br>So<br>So<br>a ol<br>QQ<br>q<br>v<br>><br>»o<br>=<br>:o ai ee ee, ee — —<— ——_—<—<br>-10 0 10, 20° 30 40 50 60 70. 80 90 100<br>Date of interview relative to median date in Imada<br><!-- End of picture text --> <!-- Start of picture text --> wy<br>- [-) AL HH tracked (N)<br>ME HH intensively tracked (N)<br>><br>QQ<br>q<br>v<br>><br>vo<br>wy<br>= an Hldia 1 iia dal<br>5 20° ; 35 : 50 ; 65 ; 80 95<br>Date of interview relative to median date in Imada<br><!-- End of picture text -->
ascertain. The ES methodology includes over 20 distinctly defined codes applied as the result of the screening process, including such wide-ranging possibilities as another firm purchased the existing establishment, while retaining the original name or that a screening interviewer could only encounter a fax line. This broad range of codes is a reflection of the challenge of confirming firms’ operating status in practice and is underlined by a central conundrum to using surveys to determine firms’ exit from the market: how can “dead” firms be surveyed to confirm that they have in fact left the market? What is more, with the passing of time, firm contact information may change: firms may simply move, change their phone number or location, or be subject to a buy-out or merger. Each of these can affect the ability of data collectors to assess a firm’s operating status at a later date. Complicating this fact is the risk that original contact information may not be accurately recorded through simple implementation error.

We utilize two definitions of firm exit to help mitigate the effect of potential bias resulting from these challenges. Both measures are “positive-confirmation” variables and begin from the assumption that firms previously operating continue to exist unless it is directly confirmed that they have exited the market.<sup>7</sup> The first definition, _strict_exit_ , is a variable that considers firms as exiting the market if (1) it is confirmed in the screening process that the firm is now out of operation; (2) it is confirmed that the firm or its available contact information now corresponds to an ineligible activity or status, such as a fully state-owned firm, an out-of-universe activity, has moved abroad, or is no longer registered; or (3) the listed contact information leads to a dead line or non-operating phone line, with all other efforts to obtain contact information exhausted. A second measure, _weak_exit_ , further includes (4) cases where contact information is incorrect and no new records are available. In this way, the _strict_ definition provides a semi-conservative measure of firm exit, while _weak_ additionally accounts unobtainable contact information.

# **<u>Measurement Issues</u>**

Our measures of firm exit are necessarily conservative for several reasons. First, short of a full-scale, annual establishment census, the ES methodology necessarily relies upon a sample of active

> 7 These definitions are almost necessarily conservative compared to a “negative-confirmation” definition, where firms are assumed as exiting the market unless they are actively confirmed in a survey or census.  As the ES data relies upon a randomized sample of firms, and not a full enumeration or census, we choose to take this more conservative approach. 10
Moldova is close to other CIS countries and the EU – wages obtained outside Moldova put upward pressure on wages at home both by reducing the supply of labor and by increasing reservation wages (international Monetary Fund 2005; Prohnitchi and Oprunenco 2010). During 2004-2010, real wages increased at an annual rate of 7.8 percent, even though wage increases were much more moderate during the financial crisis (Statistics Moldova; National Bureau of Statistics). Another consequence of labor outmigration is that the employment rate among the population in Moldova is low, at only 38.5 percent in 2010 (Expert Grup, 2011).<sup>**3**</sup> As opposed to the situation in many other low- and middle-income countries, emigration from Moldova does not seem to have caused a major brain drain, as the average educational attainment of the population is higher among the general population than among the migrants – most emigrants come from rural areas and many work as construction workers, especially in Russia (Walewski et al., 2008). However, the emigration rate among people with tertiary-level education is nevertheless also high (3.4 percent), among the world top-ten rates (World Bank 2011).

In terms of sector structure, during the period 2000-2010, Moldova’s economy became more servicebased as private services (including business, information technology and personal services) grew rapidly, while agriculture and other primary sector activities lost in importance. The share of government services increased modestly (see Figure 3).

**Figure 3. Sectoral composition of GDP 2000-2010 (%)**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60% Private services<br>50% Government services<br>Industry<br>40%<br>Agriculture, forestry, fishing<br>30%<br>20%<br>10%<br>0%<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> Source: Statistics Moldova.

Within the primary sector, agricultural activities continued to dominate while forestry and fishing (including aquaculture) remained of minor importance. Stagnant growth of the primary sector has a

> **3** The employment figures may suffer from measurement problems due to uncertainties on the number of temporary migrants.

6
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27
elsewhere, but use these as a springboard for a discussion of the implications for sovereign debt restructuring later in the paper.<sup>7</sup>

# _The Numbers_

Starting with the 1980s, EM sovereign debt restructurings with private creditors have involved some US$325 billion in principal (see table 1) with official creditors (Paris Club) accounting for only US$29 billion (8.3 percent of the total).<sup>8</sup>

**Table 1: Sovereign Debt Restructurings with Private Creditors – 1980s and after**

|**Plan/Country**|**Amount restructured**<br>**(in US$ billion)**|
|---|---|
|BradyPlan(1989)|200|
|Russia London Club(2000)|32|
|Argentina(2005 & 2010)|76|
|Ukraine(2000)|2.3|
|Uruguay (2003)|5.1|
|Others|7.2|
|TOTAL|**322.6**|

Notes and Sources: For the 1980s, the 1985 Baker Plan is not included as the restructured debt amounts are subsumed under the Brady Plan. The Russian and Argentine pre-crisis swaps are not included, but discussed below. US$6 billion of defaulted debt owed to Argentina‘s private creditors is still unresolved. World Bank (1998), Chuhan and Sturzenegger (2005), Kharas et al. (2001), Paris Club (www.clubdeparis.org ) In contrast, official creditors have accounted for the lion‘s share of sovereign debt restructurings for low-income countries (LICs), which typically have limited access to the international capital markets. As of February 15, 2012, the Paris Club has treated debt amounting to US$556 billion for 88 developing countries under 423 agreements.<sup>9</sup> On the other hand, no such collective arrangement exists for non-Paris Club bilateral creditors who generally engage with debtor countries on a one-on-one basis. Multilateral creditors have provided debt relief through the Heavily Indebted Poor Countries (HIPC) Initiative and the Multilateral Debt Relief Initiative

> 7 For Russia 1998, see Kharas, Pinto and Ulatov (2001), Pinto, Gurvich and Ulatov (2005) and Pinto and Ulatov (2012).  For Argentina 2000-01, see Serven and Perry (2005) and De la Torre, Levy Yeyati, and Schmukler (2003).

> 8 Only last rescheduling by Paris Club for emerging G20 countries included to avoid double counting (e.g. only August 1999 rescheduling for Russia taken into account).

> 9 Visit http://www.clubdeparis.org/

7
O.A.M.D.G.

Apart from assets, the _Familias_ dataset also reports two kinds of debt held by the households: mortgages and cash debt. Only 244 households report a mortgage, ranging in size from 22,300 to 35m COP, but 7,496 households hold cash debts, ranging from 2,800 to 48m COP, with an average of 544,000 COP. Summing up assets and debt, households in our sample hold on average around 4.5m COP of net assets, or around 2,200USD, which corresponds to 37 months of salary. Estimated wealth<sup>22</sup> varies widely, however, from a 28m COP debt to net assets of 463m COP. A scatter plot relating estimated net wealth to the age of the household head shows a familiar pattern.

<!-- Start of picture text --> 10 20 30 40 50 60 70 80 90<br>age of household head<br>1.50e+07<br>1.00e+07<br>mean net wealth<br>5000000<br>0<br><!-- End of picture text --> Figure 1: Asset holdings and age If we explore the two asset classes, fixed and mobile, apart, another interesting relationship emerges. Table 5 and Table 6 show participation in fixed and mobile assets

> 22 We estimate asset holdings by multiplying the different assets, both fixed and mobile, with their respective prices and summing up, deducting any reported debt.

19
The change in the wage rate for labor is not as high as the other factors. Prices of other factors increased more in the Southeast and LSCB regions. This shows a higher stress on the resources of these regions as a result of a world price shock.

**<u>Table 4.7: Change in factor prices</u>**

||**Region**|**Base**<br>**Level**<br>**(TRY)**|**Sc. 1**|
|---|---|---|---|
|**Labor**||1.00|-0.81|
||**West**|0.27|12.29|
||**Central**|0.11|14.44|
|**Rainfed Land**|**East**|0.11|13.29|
||**Southeast**|0.23|15.63|
||**LSCB**|0.14|20.33|
||**West**|0.64|8.79|
||**Central**|0.26|8.91|
|**Irrigated Land**|**East**|0.27|9.15|
||**Southeast**|0.53|10.45|
||**LSCB**|0.70|12.14|
||**West**|0.13|12.12|
||**Central**|0.11|9.79|
|**Water**|**East**|0.05|10.92|
||**Southeast**|0.09|16.69|
||**LSCB**|0.13|22.06|
||**Urban**|0.81|-2.31|

_Source: Results from TACOGEM-W._ The most important conclusion of this simulation is that change in world prices have significant welfare implications differentiated among urban and rural households. A change in prices brings about a decline in import demand while boosting the export supply. Consequently, domestic prices increase and strike the urban households through consumption while causing an expansion in the income of rural households. Furthermore, increasing agricultural world prices hampers the industrial sector as well. This effect is a result of the direct competition for resources with agriculture, and in intermediate demand for agricultural products that are now relatively more costly. This causes a second wave of problems for urban households by suppressing their income.

41
- 31 -

Table 2. Qualitative and Quantitative Treatment of Gender-Specific Interventions

||Average No. of Interventions|Average Rating Index|
|---|---|---|
|Total|2.15<br>(62)|2.92<br>(62)|
|Regional Variation|___|__|
|Africa|1.7<br>(14)|2.93<br>(14)|
|E. Asia|1.9|3.3|
||(16)|(16)|
|S. Asia|3.6<br>(8)|4.0<br>(8)|
|EMENA|2.1|2.4|
|LAC|(18)<br>1.6|(18)<br>2.0<br>|
||(6)|(6)|
|Country Socioeconomic Factors|||
|Income Level|||
|Low|2.03<br>|3.13<br>|
||(16)|(16)|
|Lower-middle|2.33|2.79|
||(12)|(12)|
|Upper-middle|2.17|3.50|
|_____<br>____<br>____<br>_____<br>____<br>____|(3)|(3)|
|Employment in Service Sector<br><0%|2.13|3.12|
||(7)|(7)|
|0-0.9%|<br>2.56|3.14|
||(7)|(7)|
|15-25%|2.25|2.84|
||(8)|(8)|
|>25%|1.75|3.90|
||(2)|(2)|
|Female Secondary Enroll. Rate|||
|<br><20%|1.67|3.00|
||(11)|(11)|
|20-35%|<br>2.17|2.80|
||(6)|(6)|
|_>35%_|<br>2.08|3.08|
||_(6)(6_||

Notes: Figures in parentheses represent number ofprojects. The number of interventions is inclusive of<sup>interventions such</sup> as monitoring, needs assessment and government policies.

To assess the results of Bank-assisted projects with gender specific interventions, we examined available PCRs and PARs. Overall, sixty-five projects had PCRs and/or PARs.