rafmacalaba/datause-encoder-data huggingface dataset:
- 10 -

assumptions, we can infer a distribution of completed spells by simply doubling the duration of observed incomplete spells for each individual."/ A second approach is to exploit the panel nature of the ENEU data. By tracking individuals over time, we can identify the first quarter in which they find a job or, if they remain unemployed at the end of one year, we code the unemployment spell as being censored. However, knowing that the first job was found (say) after one quarter does not allow us to compute time-to-firstjob. We therefore assume that the first job was found at the end, the middle, or the beginning of the interval. These assumptions correspond to adding 3, 1.5, or 1 month(s) to the incomplete unempLvyment spell reported in the third quarter of 1990. Both approaches, and all three assumptions, yielded similar results, namely, that time-to-first-job is significantly shorter for trainees than the control group. The results reported below are based on the second approach using the most stringent start-time assumption.

We corrected for selectivity bias by applying to the control group the same criteria used to select trainees into the program. We first estimate an equation for the probability of selection into the PROBECAT program, relating program participation to the criteria for which we have information--marital status, number of children and econcmic dependents, schooling, and time spent in unemployment at the selection point. We then limit the sample of controls to "eligible" individuals who have a high predicted probability of program participation. All the employment results presented below are based on comparisons of the trainees with the selectivity-corrected sample of controls who were eligible for, but did not participate, in the PROBECAT program.

Figure 1 plots survival curves for male trainees and controls using the raw duration data on time-to-first-job. These survival curves are defined as a function of time t (in months). They indicate the probability of remaining unemployed t months after entering unemployment. We present separate survival curves for those age 25 and under, and for those over 25 years old. They clearly show that trainees exit unemployment more quickly than do the controls: at three months, 62 percent of young trainees have left unemployment as compared to just 42 percent of controls. The difference is more marked for the older trainees: 72 percent of them have left unemployment within 3 months, versus 33 percent of controls. We estimate that the average duration of unemployment for male trainees aged 25 and under is 1.4 months shorter than that for controls; for trainees over 25, average duration of unemployment is j5/ See Stephen Salant, "Search Theory and Duration Data: A Theory of Sorts", Quarterly Journal of Economics, 1977.
also product and year dummies), while columns (2) and (4) refer to models with product ...xed-e¤ects (including also country and year dummies). We ...nd that the marginal e¤ects of interest are always remarkably similar to the OLS point estimates reported in column (1) of Table 6. Taken together, the results from these two strategies provide evidence that the potential bias imposed by tari¤ binding does not appear to be an important concern in our application.

In the analysis thus far, we have adopted a linear function of rainfall. To account for potential nonlinearity in the policy response to extreme events such as drought, we modify out basic speci...cation by considering instead a set of dummy variables that are intended to capture extreme realizations of rainfall. In columns (1) and (4) of Table 8, we consider a dummy variable that takes the value of one when a realization of rainfall for a given country-year is below the 15th percentile of the rainfall distribution in the period 1988-2006.<sup>18</sup> Consistent with the results reported earlier, the estimates indicate that severe rainfall shortages induce policy makers to set lower tari¤s on agricultural imports. In columns (2) and (5), we introduce a dummy variable that equals one when the level of rainfall for a given country-year is above the 85th percentile of this distribution. The results indicate that larger volumes of rainfall are associated with higher agricultural tari¤s, what is again consistent with the linear speci...cation.<sup>19</sup> The results in columns (3) and (6) reveal that these conclusions remain unchanged when including both these dummy-variables and interaction terms together in the same regression.

# 7 Heterogeneity of e¤ects across products and countries

We now examine the extent to which the impacts of rainfall shocks on agricultural tari¤s are heterogeneous across products and countries. Table 9 presents the results from regressions in which the rainfall variable is interacted with dummy variables for each of the agricultural products included in the UN statistics division of the SITC classi...cation (see Table A.3). The results in columns (1) and (3) suggest that a rainfall shortage leads to tari¤ reductions in most of the products considered. When considering both simple and weighted import tari¤s, we ...nd positive and signi...cant e¤ects for all product categories,

> 18 We de...ne this variable based on the full country-level data on the log of rainfall for this period. We have considered alternative thresholds to de...ne the dummy variable and have obtained qualitatively similar results.

> 19 Extremely high realizations of rainfall in a short period of time can cause ‡oods. Floods may (or may not) reduce agricultural output in a given year. While a ‡ood typically reduces agricultural output in the months it occurs, it also tends to increase agricultural productivity in the following growing season, what has the opposite e¤ect (Banerjee, 2007). Based on annual data on country-level rainfall, it is however di¢ cult to separate ‡oods from consistently higher volumes of rainfall spread over the year.

19
O.A.M.D.G.

examining the share of fixed assets in the portfolio confirmed all findings with high significance, robust to different asset definitions and empirical specifications. A Seemingly Unrelated system of valued demands of the two assets and debt returned the expected signs on all violence variables, however with varying significance. Selection correction with the Inverted Mills Ratio showed that the sample of stayers captured in the survey data is more likely to hold value in both kinds of assets and debt than the base population but not a higher share of fixed assets.

An investigation of mere participation in both assets, i.e. asset movements happening at the poorest fringe of the population, confirmed our model for the homicide rate, but brought the controversial finding that participation in both assets may rise with more guerrilla attacks. This raises the question of whether the guerrilla occasionally still engages in redistribution, or the poorest benefit from abandoned property. In any case, the analysis of participation should be taken with a grain of salt as it only registers the asset movements of about 10% of the population and has to work with small cell sizes.

To the extent that violence falls under the definition of socio-political risk, our findings confirm Stewart and Venieris (1985). In general, however, our paper answers a new research question and to our knowledge the results have no parallels in the established literature. They add a violence perspective to a model of savings in two assets and show how violence can influence portfolio composition, skewing it to an economically suboptimal combination. Violence is shown to incentivize people to hedge against it. It would be interesting for future research to explore whether this effect can shift some fundamental parameters of a community or a country, such as the savings rate or risk aversion. Our results for portfolio choice suggest this may be likely.

Some conclusions bear relevance to policy makers. First, both guerrilla and common violence have consequences that reach beyond their immediate destructive effect. The 41
<!-- Start of picture text --> Corn<br>900,000 160<br>€ 800,000 140 _<br>Z 700,000 120 @<br>éS$ 600,000500,000 100 =&<br>= so 2<br>% 300,000 =<br>100,000 20<br>00<br>05 10 15 20 25 30 35 40<br>Time period - 1971/72 to 2007/2008<br><!-- End of picture text --> <!-- Start of picture text --> Wheat<br>700,000 300<br>ec<br>z 600,000 250<br>500,000 8<br>8= 400,000 200 &£<br>3 150 2<br>Hy 300,000 §<br>200,000 100 Fi<br>100,000 50<br>00<br>05 10 15 20 25 20 a5 40<br>Time period - 1971/72 to 2007/2008<br><!-- End of picture text -->
_Table 3: Annual government spending, share of GDP and child benefits, 2018/19_

||Gov|ernment spending<br>Share of||Child<br>relevant|
|---|---|---|---|---|
|Spending category|ETB<br>(millions)|<br>government<br>spending (%)|Share of<br>GDP (%)|<br>spending<br>considered?|
|Total spending|413,106|100.0|20.8||
|General services|74,660|18.1|3.8||
|Economic development|137,751|33.3|6.9|Yes|
|Agriculture|62,975|15.2|3.2|Yes|
|PSNP<sup>a</sup>|5,690|1.4|0.3|Yes|
|Food security<sup>b</sup>|1,666|0.4|0.1|Yes|
|Urban development and<br>construction|16,094|3.9|0.8||
|Road|41,318|10.0|2.1||
|Other|17,364|4.2|0.9||
|Social development|160,407|38.8|8.1|Yes|
|Education|102,816|24.9|5.2|Yes|
|Health|38,382|9.3|1.9|Yes|
|Labor and social welfare|3,821|0.9|0.2|Yes|
|Other|15,388|3.7|0.8|Yes|
|Indirect subsidies (off-budget)<sup>c</sup>|2,714|0.7|0.9|Yes|
|Others<br>**_Source:_**_Authors’ calculations; Data from_|40,288<br>_the Ministry of Fina_|9.8<br>_nce, the Ethiopian P_|2<br>_etroleum Suppl_|Yes<br>_y Enterprise, the_|

**_Source:_** _Authors’ calculations; Data from the Ministry of Finance, the Ethiopian Petroleum Supply Enterprise, the Ethiopian Trading Businesses Corporation, and ESS._<sup>_a_</sup> _The value of PSNP for 2018/19 is derived from the ESS data, which has information about PSNP transfers for the previous 12 months._<sup>_b_</sup> _Food security value is also estimated from the ESS data._<sup>_c_</sup> _Data on subsidies are from the Ethiopian Petroleum Supply Enterprise and the Ethiopian Trading Businesses Corporation._ **_Note:_** _ETB = Ethiopian Birr._

# **3.2.  Survey data: Consumption, utilization of services and child poverty**

The survey data are from the 2018/19 Ethiopia Socioeconomic Survey (ESS). ESS is a nationally representative survey implemented by the Central Statistics Agency in collaboration with the World Bank under the LSMS-ISA project. The survey interviewed 6,700 households out of which 4,992 households had at least one household member between 0-17 years old at the time of the interview. A total of 13,820 members in this age group are included in the analyses.

Table 4 presents the descriptive statistics of the children included in this study. The profile shows that both boys and girls have similar demographic and socioeconomic characteristics. The average age is about 8.5 years. The household size is over the national average because this sub-sample of households includes only those with children. About one in five children live in urban areas, and the share is slightly higher for girls. The profile, however, differs by place of residence. For example, children in rural areas are way more deprived than those in urban areas. This difference is strongly associated with child deprivations in housing conditions including water and sanitation facilities, access to electricity, number of rooms per household member, and access to information.

11
More recently, the literature has moved to incorporate the work decision and thus analyzing schooling and child labor jointly. Nielsen (1998) analyzes child labor and schooling in Zambia. A gender gap is established, as boys are found more likely to go to school than girls. However, there does not seem to exist any gender related differences in the working decision. Transport costs in the form of walking distance to school affects schooling adversely. Furthermore, supply constraints on secondary schooling in the form of distance are found to negatively affect the demand for primary schooling. This accords with the view in Lavy (1996), who suggests that completion of primary schooling may serve as a ticket to secondary schooling. Nielsen does not find a positive relation between poverty and child labor, and thus raises doubts to the claim of poverty being a main determinant of child labor, see, e.g., Grootaert and Kanbur (1995).

Canagarajah and Coulombe (1997) analyze 1991-92 data on child labor in Ghana and find, similar to Nielsen, evidence of a gender gap in schooling, as boys have a higher probability of attending school than girls. Again, there is no substantial differences in the tendency to work. Including expenditure per capita of the household as a proxy for welfare (or an "inverse" measure of poverty), a weak inversely u-shaped relationship with child labor is established. This seems, in accordance with Nielsen's results, to contradict the traditional claim of poverty being a main determinant of child labor. However, a strong positive (negative) relation is found between wealth (poverty) and schooling. Livestock owned by the household is found to affect the working decision positively and the schooling decision negatively, due to the labor intensity of livestock, which increasingly finds use of child labor. Lastly, monetary costs of schooling affect the schooling choice adversely.

Sasaki and Temesgen (1999), analyze child labor in Peru. In accordance with the studies reviewed above, a gender gap related to schooling is established, i.e., females tend to be less likely to be sent to school. However, in addition to the studies previously reviewed, it is further found that girls are more likely to work than boys. Hence, the work of girls may be said to somewhat subsidize the building up of human capital of their brothers. Furthermore, a positive relation between mothers' educational level and the likelihood of the child attending school is established. Lastly, no significant relationship 5
The total burden (line s) of this major part of the total budget has fallen by only 2.5 percent of GDP between the prereform period and 1993. It took a reduction of subsidies by 12.3 percent of GDP (line b, last column) to achieve this reduction of fiscal distortions. The gain Table 10: Summary of Budgetary Changes due to Income Claims

||1987-89<br>-<br>l|1991<br>-<br>.|diff, of<br>1987-89|1992<br>J|diff. of<br>1987-89<br>and1992|1993|diff. of<br>1987-89<br>and 1993|
|---|---|---|---|---|---|---|---|
||______||percentag|<br>e of GDP||||
|a. Profit tax|12|6.5|_-5.5_|<br>4.4|-7.6|4.0|-8.0|
|b. Subsidies (all)|(15)|(3.8)|(-11.3)|(3.4)|(-11.6)|(2.7)|(12.3)|
|c. Net (a-b)|3|+2.8|+5.8|+ 1.0|+4.0|+1.3|+4.3|
|d. FUS contribution|8.5|11.4|+2.9|12.9|+4.4|12.9|+4.4|
|e. Contribution rate|||+1.3||+3.9||+3.9|
|f. Employment|||-1.7||-2.4||-3.0|
|g. Wage (d-e-f)|||+3.3||+2.9||+3.5|
|h. Net FUS pensions|(8.5)|(14.0)|(+5.5)|(13.9)|(+5.4)|(13.3)|(+4.8)|
|i. Entitlements|||_+1.5_||+2.2||+2.5|
|j. Benefit (h-i)|||+4.0 1||+3.2||+2.3|
|k. Net (d-h)|0.0|-2.6|-2.6|-1.0|-1.0|-0.4|-0.4|
|1. FUSRpensions|(0-9)|(1.9)|(+1.0)|(2.1)|(+1.2)|(2.1)|(+1.2)|
|m. Unemployment benefits|(0.0)|<br>(1.6)|(+1.6)|(2.2)|(+2.2)|(2.0)|(+2.0)|
|n. Unemployment contributions|0.0|0.6|+0.6|0.6|+0.6|0.8|+0.8|
|o. Net government wages|(3.5)|(6.0)|(+2.5)|(5.3)|(+1.8)|(5.3)|(+1.8)|
|p. PIT (excl. gov.)|3.5|2.6|-0.9|3.3|-0.2|4.0|+0.5|
|q. Net (p-l-m+n-o)|-0.9|-6.3|-5.4|-5.7|-4.8|-4.6|-3.7|
|**r.**Net**total (c+k+q)**|**-3.9**|**-6.1**|**-2.2**|**-5.7**|**-1.8**|**-3.7**|+0.2|
|s. Total spending|27.9|27.2|-0.7|26.9|-1.0|25.4|-2.5|

Note: Numbers in 0 are expenditure, which enter negatively in sums except in total spending. e=(d*coeffh table 3)-d f=(e/coeff e table 3)-e i =h-(h/coeff b table 4) p =personal income tax excluding taxes on government wages and on pensions.

s= sum of all expenditure (or expenditure changes). It measures total burden.

Source: Tables in text.

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the DDA and beyond. The current Doha round has the advantage over the Uruguay Round of beginning from the framework of rules and disciplines agreed in that previous Round. In particular, it has the three clearly identified “pillars” of market access, export subsidies, and domestic support on which to focus. True, it took more than three years to agree on a framework for the current negotiations, reached on at the end of July 2004 (WTO 2004), but now that July Framework Agreement is likely to guide the negotiations for some time. It therefore provides a strong basis for undertaking _ex ante_ analysis of various options potentially available to WTO members during the Doha negotiations.

This paper summarizes a recent study (Martin and Anderson 2005) that builds on numerous analyses of the Doha Development Agenda and agricultural trade, including five very helpful books that appeared in 2004. One edited by Aksoy and Beghin (2004) provides details of trends in global agricultural markets and policies, especially as they affect nine commodities of interest to developing countries. Another, edited by Ingco and Winters (2004), includes a wide range of analyses based on papers revised following a conference held just prior to the aborted WTO Trade Ministerial meeting in Seattle in 1999. The third, edited by Ingco and Nash (2004), provides a follow-up to the broad global perspective of the Ingco and Winters volume: it explores a wide range of key issues and options in agricultural trade reform from a developing country perspective. The fourth, edited by Anania, Bohman, Carter and McCalla (2004), is a comprehensive tenth-anniversary retrospective on the Uruguay Round Agreement on Agriculture as well as a look ahead following also numerous unilateral trade and subsidy reforms in developed, transition and developing economies. And the fifth focuses on implications for Latin America (Jank 2004).

All of those 2004 studies were completed well before the July Framework Agreement was reached in the early hours of 1 August 2004, and before the public release in December 2004 of the new Version 6 database of the Global Trade Analysis Project (GTAP) at Purdue University. That Version 6 database is a major improvement over the previous version for several reasons. One is that it includes global trade and protection data as of 2001 (previously 1997). Another is that the new protection data include, for the first time, bound as well as applied tariffs, non-reciprocal as well as reciprocal tariff preferences, the _ad valorem_ equivalents of specific tariffs (which are 5
The summary statistics for each of these variables used in the estimation are provided in the rows of the lower half of table 5.

# **4 Econometric Approach**

## **4.1 Econometric Model**

Our ultimate question of interest is why foreign governments choose to challenge some U.S. trade remedies while not challenging other U.S. measures. To address this question, our sample of data consists of the set of U.S. trade remedy actions imposed after antidumping and countervailing duty investigations initiated between 1992 and 2003. We only focus on those applied against GATT Contracting Parties or WTO members, as formal participation in the GATT/WTO system is an obvious requirement for the foreign country’s use of GATT/WTO dispute settlement procedures. Our empirical investigation will thus examine determinants of a dichotomous dependent variable which takes on a value of 1 if a U.S. trade remedy was challenged by a WTO trade dispute and 0 if it was not. In the absence of any additional econometric concerns, we would simply estimate the probability that a U.S. trade remedy was challenged by assuming that the decision was a function of a number of covariates with a normally distributed error term, and we would thus employ the standard probit model.

However, one concern with examining in isolation the foreign country’s decision of whether or not to file a GATT/WTO dispute over a U.S.-imposed trade remedy is selection bias.<sup>24</sup> In our case, selection bias results from the incidental data truncation problem associated with the fact that we only observe formal trade disputes over U.S. trade remedy investigations that would ultimately result in the trade remedy being applied. For example, one of our key testable hypothesis is that a foreign country is more likely to file a WTO dispute against the U.S. if it has a sufficient retaliation capacity, and thus we presume that this may have a direct effect on our underlying dependent variable of interest. However, given the evidence provided by Blonigen and Bown (2003), we would also expect that the same retaliation capacity may also affect the likelihood that the U.S. imposes a trade remedy against that particular foreign country in the first place, and thus it also has an _indirect_ affect on the probability that this country

> 24 For a further discussion of the selection bias problem in econometrics, see Greene (2000, pp. 926-950).

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