rafmacalaba/datause-encoder-data huggingface dataset:
among men, those from male-headed households are less likely to be married by 2004 (significant at 10.6%).

Tables 3 and 4 explore the effects of orphanhood and the death of a prime age household member by specific individual and household characteristics for the samples of women and men, respectively. The additional variables include an indicator for whether the child was in school at the time of becoming an orphan, and interactions of orphanhood with two household characteristics from baseline: the head was employed in non-farming and the household had per capita consumption above the median.<sup>11</sup> Because of the low frequency of our variables of interest (mortality shock), we are careful in our interpretation of the interactions where means are, of course, smaller.

The results indicate that the significant positive effect of paternal orphanhood on the probability of marriage among young women only holds for those who were not at school at the time of paternal death (Table 3 column 1).<sup>12</sup> In column 2, results show that the impact of paternal mortality on probability of marriage also varies depending on the occupational sector of the head of the household. We find that orphanhood has a significant impact on transition into marriage among women who, at a young age, were living in households headed by individuals employed in farming as opposed to nonfarming sectors (mainly non-farm enterprise activities rather than wage employment). Finally, the impact of paternal orphanhood on propensity to marry among girls is concentrated among poorer girls (column 3). Girls in rich households are protected from earlier marriage if their father dies relative to poor girls.

For men, we find no impact of paternal or maternal orphanhood on probability of marriage regardless of schooling status when orphaned (Table 4 column 1). In contrast, while paternal death has no significant impact on the probability of marriage among men who were raised in farming households, it does significantly reduce the probability of marriage among those raised in households headed by individuals in the non-farming sector (Table 4 column 2). Non-farm households are largely engaged in non-farm

> 11 The indicator for being in school at baseline is motivated specifically by findings in Beegle _et al_ . (2006c) which found that orphanhood was associated with lower schooling among those children not in school when orphaned.

> 12 Although the mitigating effect of being in school and being a paternal orphan is not significant, as noted above, we are careful in our interpretation since events are infrequent. We compare all our interaction results by re-estimating the specifications in Tables 3 and 4 using the opposite set of interactions: _not_ being in school, being a _farm_ household, and having per capita expenditure _below_ the median.

9
This study used monthly nightlight composites in conjunction with a wind field model to estimate the impact of tropical cyclones on local economic activity in the Dominican Republic. The econometric results show that the negative impact lasts up to 15 months after the strike, with the largest effect observed 9 months after the storm strikes. The 19 damaging storms observed over the 22-year sample period resulted on average in a reduction in nightlight intensity of about 2.1 percent, whereas the most damaging storm, Hurricane Georges, caused brightness to reduce by 31 percent. Translating the reduction in nightlight intensity into monetary losses by relating it to quarterly GDP suggests that on average the storms reduced GDP by about USD 1.1 billion (4.5 percent of GDP 2000 and 1.5 percent of GDP 2016), with Hurricane Georges in 1998 causing a reduction in activity by about USD 14.7 billion (69.4 percent of GDP 1998 and 20 percent of GDP 2016) and more than 5 times the direct and indirect losses reported by CEPAL.<sup>16</sup> Finally one may want to note that one of the weaknesses of our analysis is that by using nightlights we were probably not very good at capturing the direct impact on agriculture. This clearly could add to the overall costs.

> 16 CEPAL, 1998, “ _Dominican Republic Post-disaster needs assessment of Hurricane Georges, 1998_ ”, CEPAL Mexico (in Spanish).

16
(11) _~~~~CAAR(T,,.T,) (var(CAAR(I;,T, )))_<sup>_2_</sup> As pointed by MacKinlay (1997, pp. 24), this distributional result is asymptotic with respect to the number of securities N and the length of estimation window _L._ In the next section, we present results obtained from using the single-index model (constant mean return model).'<sup>2</sup>

# IV. **Empirical Results**

We apply the event-study methodology to the environmental events collected in each of the country over the period 1990-94. While various subsets of firms can be presented (e.g. by countries, by industrial sectors, etc.), each of those subsets contains a relatively small number of firms, and results in each subset are typically driven by changes in the market values of a limited number of firms. Hence, for the purpose of the analysis, we first present the results obtained at the most disaggregated level, i.e. the firm level. This is more likely to indicate the nature of the events to which capital markets

> **12** The single-index model is a particular case of the market model described above. Where market retuns were available, we also obtained results using the market model. Results were similar to those presented here. In fact, Henderson (1990) points out that the three estimating methodologies yield results of similar nature.

16
Table 3: Tests for independence of peer variables and class assignment and classroom resources (following AP2009).

|**Family**|**Chi-square test**|**Teacher’s characteristics**|
|---|---|---|
|**background**|**% school passing test**|**F-test**|
|_Number of books_|||
|All|87.4|0.213|
|Public|87.0|0.421|
|Private|88.8|0.484|
|_Parental education_|||
|All|94.1|0.577|
|Public|93.7|0.673|
|Private|95.3|0.947|

Note: The first column corresponds to the proportion of schools for which the Pearson’s _χ_<sup>2</sup> test cannot reject the null hypothesis of independence between the number of books (parental education) and class assigned within the school. The second column of the table reports the p-value of a joint test from a regression of number of books (parental education in years) on a set of teachers’ characteristics. These include teachers’ gender, degree obtained and years of experience. Source: ONE 2000, Argentina.

more tenure in a school might be able to influence their assignation into classes based on, for instance, past performance of pupils so that the more experienced teacher is met with the best performing class. If this were the case, the estimates of the peer effects will be biased. Once again, I follow Ammermueller and Pischke to test this by regressing children socioeconomic characteristics (number of books and years of parental education) on a set of teachers’ characteristics, with school fixed effects. The explanatory variables included in the model are teachers’ gender, degree obtained and years of experience. The p-values of the F-test of joint significance is reported in the right side of table 4. In all cases, there is no evidence of allocation of teachers based on students’ characteristics.

A separate issue worth mentioning is the well-known problem of identifying separately endogenous and exogenous effects. This concern is known in the literature of social interactions as the _reflection problem_ and has been introduced in the area by Manski (1993). The standard formulation of peer effects is a linear-in-means model, where the student’s achievement is determined by the average classmates’ achievement and average predetermined characteristics. The estimated coefficient on the first term defines the endogenous peer effect and the coefficient on the second is the contextual effect.

19
7 tax revenue will always be considered as own source revenue. Finally, more detailed information about intergovernmental grants is also given in OECD data, allowing their subdivision as either general-purpose or specific grants. General-purpose grants are ones that can be seen as own revenue when they are provided based on objective criteria. But their allocation may as well be made at the central government’s discretion. Specific grants are earmarked for certain purposes. Their allocation may be conditional across subnational governments as well as unconditional, which gives more autonomy.

A comparison presented in Table 2 of the GFS and OECD aggregate data for subnational share of government expenditure and revenue shows little differences. However, a more detailed analysis shown in Table 3 of OECD data reveals that in most countries, subnational governments do not have a significant control over their revenues. On average for this sample, about 50 % of subnational governments revenues came from tax sharing in 1999. This share ranges from 14% for Poland to 91% for Lithuania.
||Other services|0.776|0.003|227.650|0.000|0.769|0.783|
|---|---|---|---|---|---|---|---|
||Urban|0.650|0.002|299.440|0.000|0.645|0.654|
||Constante|2.049|0.003|602.210|0.000|2.042|2.056|
|Quintile 5||||||||
||taille|-0.317|0.000|-1357.080|0.000|-0.318|-0.317|
||Female|-0.662|0.003|-248.400|0.000|-0.667|-0.657|
||Agriculture|-0.398|0.003|-141.330|0.000|-0.404|-0.393|
||Industry|0.523|0.004|128.330|0.000|0.515|0.531|
||Commerce|0.416|0.004|117.690|0.000|0.409|0.423|
||Transport|-0.212|0.006|-36.980|0.000|-0.223|-0.200|
||Other services|0.599|0.004|164.960|0.000|0.591|0.606|
||Urban|1.283|0.002|543.400|0.000|1.279|1.288|
||Constante|3.380|0.004|900.740|0.000|3.373|3.387|

Source: ESAM2001, ESPS2005, ESPS2011 and authors’ estimates The estimates suggest that people engaged in “other services,” which is dominated by ICT, are most likely to belong to the fourth wealth quintile than to the first quintile, based on the ranking of the coefficients of the “other service” dummy variable, which reached the highest value of 0.776 in the fourth quintile. The next quintile in the rank is the fifth (0.599), implying that individuals engaged in the sector are likely to end up in the top two income quintiles. By similar ranking, individuals engaged in commerce are most likely to end up in the third and fourth quintiles where the coefficients are highest at 0.834 and 0.693, respectively. Agriculture workers sort into the second and third quintiles. Workers in the transport sector are the most likely to be in the first quintile. However, as Katz and Koutroumpis (2012) demonstrate, the indirect employment effects of ICT deepening outsize the direct effects by a ratio of 20:1. Given the likelihood of indirect employment diversified across sectors, it is expected that the welfare effects of ICT development will be more broad-based.

One of the six pillars of the government’s development strategy, contained in the Plan Senegal Emergent (PSE), focuses on increasing the scope and sophistication of information and communications technology (ICT). In particular, the PSE aims to improve the quality of ICT infrastructure and services, facilitate the growth of business process outsourcing (BPO), and catalyze the formation of ICT-specific human capital through implementation of large-scale ICT projects in key sectors such as education and health care.

Senegal ranks among the notable African countries already capturing parts of the global ICT value chain particularly in the areas of information technology outsourcing (ITO) and business process outsourcing (BPO) (Stephenson, 2012). Opportunities exist for expansion of mobile banking, 23
- 24 -

Table 6 Selected macroeconomic indicators (1983-87)

||1983|1984|<br>1985|<br>1986|<br>1987|
|---|---|---|---|---|---|
|Real GDP growth rate(%)|-0.7|6.3|<br>2.4|<br>5.7|<br>5.7|
|Real effective exchange||||||
|rate (1980=**10O)a**|117.5|119.2|150.4|173.0|178.4|
|Terms of trade (1980=100<br>|85.8|80.6|<br>76.0|<br>75.5|<br>81.3|
|Cturrent account/GDP(%)<br>Fiscal deficit(-)|-7.1|-10.7|<br>-8.3|<br>-6.5|<br>-4.3|
|or surplus/GDPb|-2.6|-3.0|<br>-2.4|<br>-1.0|<br>-0.8|
|M2/GDP (%)c<br>|19.3|20.7|<br>20.1|<br>19.8|22.9|
|Infltion (CPI)(3,|23.1|23.0|<br>26.4|<br>17.4|<br>19.9|
|Unemployment(%)|14.6|<br>13.9|<br>12.0|<br>8.8|<br>9.6|

Source: Central Bank Bulletin unless otherwise specified.

- a. Source: Bank staff estimates.

- b. Consolidated central government deficit or surplus.

- c. M2 = Ml + Dp, where Ml = Money in circulation plus drawable money of private sector. Dp = Time deposits at more than 30 days of private sector, excluding time savings.

- d. Source: National Institute of Statistics. For 1985-87, last quarter (October-December) numbers have been used.
main outcomes of analysis, including economic growth, spatial inequality, electoral outcomes, and organized crime. _Xm_<sup>_′_isavectorofbaselinecontrolvariablesbefore2013.Thesepre-</sup> shock municipal characteristics are interacted with year fixed effects to flexibly control for differential municipal trends. The variables in this vector include baseline night-light density measured in 1992; urban coverage, water bodies, and tree-cover loss observed in 2001; and proxies for political repression collected in 2004.<sup>18</sup> Foreigners Share _m,_ 1990 is the share of foreign settlers in municipality _m_ in 1990, constructed as the ratio of foreigners in the municipality over total foreigners in the country. By construction, these shares sum to 100. This variable serves as an allocation weight that distributes total national outflows across municipalities of origin. The numerator of Imputed Outflows _mt_ therefore allocates total outflows to Colombia across municipalities according to their foreign settlement shares, while the denominator scales this count by _Pm,_ 1990 to express the instrument as the share of each municipality’s 1990 baseline population that was displaced. This population normalization operates at the level of the full instrument rather than at the level of the allocation weight, because dividing the share itself by local population would break the adding-up property and prevent the instrument from being interpreted as a municipal displacement rate. Imputed Outflows _mt_ is multiplied by 100 to facilitate interpretation.

_γm_ and _αt_ denote municipality and year fixed effects, respectively, and standard errors are clustered at the municipality level to account for potential serial correlation within municipalities. The coefficient of interest, _β_ 1, measures the change in outcomes associated with a one-percentage-point increase in displacement outflows as a share of the 1990 municipal population. The main results report ordinary least squares (OLS) estimates of equation (2), 2SLS estimates as specified by the equations above, and reduced-form estimates from a regression of _ymt_ on Imputed Outflows _mt_ .

> 18As described in Appendix B, these measures correspond to the municipal count of the individuals who opposed the Ch ́avez regime in an open letter and subsequently received fewer economic opportunities as a result of lower employment and reduced access to the social welfare system. The data come from Hsieh et al. (2011).

23
7 Research and Development Activities 12. Safety and Efficacy. More women use contraceptives than any other category of drug or medical device, and many use them for long periods of time. It is, therefore, particularly important for information on the safety and side-effects of different contraceptives to be as complete and accurate as possible. Much of the existing information has been collected from the most developed countries. Relatively little has been obtained from less developed countries, even though the use of contraceptives in the developing world is increasing on a wide scale. In particular, not much is known about the safety of contraceptives when used by women with diseases which are endemic in developing countries, and whether results from studies conducted in developed coun'ries fre applicable to other parts of the world where patterns of disease may be very different.

13. An important achievement has neen the publication of the first series of results from the large, multicenter study on oral contraceptive use and cancer. The results obtained do not differ markedly from earlier findings in North America and Europe, and confirm the safety of oral ceontraception for most women. Oral contraceptive use provides some protection against endometrial and ovarian cancer. There is a weak association with a somewhat elevated risk of breast cancer and cervical cancer but it is not determined whether the association is of a causal nature. No link was found between oral contraceptives and liver cancer in developing countries. Studies on cancer and the use of the injectable contraceptive depot-medroxyprogesterone acetate (DMPA), about to be published, have helped to quiet the understandable concern that had arisen over the use of this method of contraception in developing countries when the Food and Drug Administration had withheld approval for its use as a contraceptive in the United States.

14. Several other major multinational safety evaluations are underway. Among them are a 20-center, four-year study on how the use of low dose hormonal contraceptive pills affects the risk of cardiovascular disease. Priority is also being given to studies on: the interaction between different methods of contraception and iron-deficiency anermia-a very common condition among women of reproductive age in a large number of developing countries;
The informal poor face risks in the form of unemployment and overall economic downturns, earn a low and irregular income, own very few assets, and have no insurance against poverty, such as unemployment benefits. At the same time, it should be recognized that since very few people work in the formal labor market, social policies tied to formal employment or unemployment will have only a very limited reach among the poor. Moreover, informal mechanisms of risk sharing in dispersed rural areas are limited because covariate risk (such as climate) is high and thus there is limited potential for informal risk sharing. This places a premium on migration diversification strategies, such as sending a few children to informal urban markets.

The labor category contributing the largest share to overall poverty is that of employees with salary as a percent of sales or production (26 percent are indigent). This contrasts with the self-employed and wage worker categories where 18.7 and 23.2 percent are extremely poor, respectively.

Those who work in agriculture and services are more likely to be poor than workers in industry. This suggests that productivity in agriculture and services is lower than in industry. It also suggests a more competitive wage environment among more highly educated workers in the industrial sector. The P0 is 24.5 percent in agriculture, 25.7 percent among service workers, and 5.2 percent among industrial workers.

In 2003, more than 75 percent of the extreme poor household heads in dispersed rural areas cited agriculture as their primary form of employment. One explanation for the indigent poverty rate in agriculture can be traced to migration out of the sector as the most educated rural dwellers leave, in part due to the structure of land ownership, the quality of land, and lack of credit and other productive inputs. Rural land ownership is characterized by a high degree of concentration of land in a few large establishments and a large number of small farms with an insufficient area to sustain a family by agricultural employment alone.

Land concentration increased over the last 10 years. During 1988–2001, the planted area increased by 8 percent, while the number of farm enterprises decreased by 21 percent (Agricultural Census, 2001). Table 5.3 indicates the land distribution as of 2003 in dispersed rural areas in Argentina. The table shows that nearly half of the landholdings (49 percent) are smaller than 10 hectares and 19 percent are larger than 100 hectares.

|Table 5.3: Land Distribution For Producer Households<br>|
|---|
|Dispersed Rural Areas in Argentina,2003 (percent)|
|Hectares<br>Total<br>Cumulative|
|0–1<br>27.6<br>27.6|
|1.12–10<br>21.7<br>49.3|
|10.1–35<br>17.9<br>67.2|
|35.1–100<br>14.2<br>81.4|
|100.1–250<br>10.8<br>92.2|
|More than 250<br>7.8<br>100.0|

Source: Own calculation based on RHS 2003.

29
seasonal income and consumption. That is, it is possible that a common seasonal shock influences all households to behave in a certain manner, independent of household and village heterogeneity. For example, household consumption may be completely independent of seasonal variations in income and still co-vary with seasonal income, simply because of common season-specific shock.

Introducing seasonal dummies in the regression of (3.1) does not resolve this problem. Nor an introduction of a village fixed effects method can solve this either, as common seasonal effect affects all households equally in a village. This requires us to introduce a common seasonal model which requires seasonal panel data (i.e., repeated observations across seasons). Recall that cross-sectional data of 2000 and 2005 do not form panel either at the household or village level, but they provide a cross-section of households/villages interviewed across seasons over two years. In this case, one constructs a panel of seasons at the thana level if not at the village level with different households interviewed over two seasons. Thana is thus the lowest common sampling units that can help create the panel across seasons. The common seasonal effect at the thana level (  _sj_ ) is therefore an aggregate shock observed at the thana level that affects equally all households living in a particular thana. We propose, therefore, to use a thana level fixed effects method with panel seasonal data to control for the common seasonal shock.<sup>21</sup> In other words, let us rewrite the equation (3.1) as follows:

> where  _sj_ is unobserved thana-level common seasonal shock, _j_ stands for thana instead of village and _t_ is year.  Similarly, equations (3.2) and (3.3) will include a seasonal thana-specific

> 21 The ideal panel could be at the household level, meaning the same households were interviewed in different seasons over two years.

17