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35
Table 4: Difference-in-differences estimate of the effect of the Boko Haram insurgency on Intimate Partner Violence in Nigeria

|Intimate Partner Violence in Nigeria<br>**Variables**<br><br><br>|**Past year physical or sexual IPV**|
|---|---|
|PPP<br>y<sup>*****PBB</sup><br>l|0.037**|
||(0.015)|
|**Individual characteristics**||
|Type of earnings (reference category: no earnings)<br>||
|_Cash only_|-0.017<br>|
||(0.018)|
|_Cash and kind_|-0.031<br>|
||(0.022)|
|_Kind only_|-0.047*<br>|
||(0.027)|
|Total number of children born|0.003**<br>|
||(0.001)|
|Married as a child (before 18 years of age)|0.026***|
||(0.008)|
|**Partner characteristics**||
|Sometimes or often drunk|0.146***|
||(0.013)|
|**Household characteristics**||
|Polygamous household|0.017**|
||(0.008)|
|**Attitudes and experiences of IPV**||
|Father beat mother|0.124***|
||(0.017)|
|Condones wife beating|0.044***|
||(0.009)|
|**Nigeria specific factors**||
|Ethnicity (reference category: Yoruba)||
|_Igbo_|0.003|
||(0.020)|
|_Hausa_|-0.053**|
||(0.021)|
|_Fulani_|-0.052*|
||(0.030)|
|_Other ethnic group_|0.006|
||(0.017)|
|Religion (reference category: Christianity)||
|_Islam_|0.004|
||(0.011)|
|_Traditional African religion_|-0.002|
||(0.033)|
|_Other religion_|<br>-0.037|
||(0.163)|
|Before BH (Non-BH Area)|<br>9585|
|Before BH (BH Area)|1126|
|During BH (Non-BH Area)|11715|
|During BH (BH Area)|1444|
|Total<br>|23870|
|R-squared|0.11|

Note: Variables used to estimate the kernel propensity scores are dropped State fixed effects are included. The kernel density function is Epanechnikov with a bandwidth of 0.06. Probit estimation is used for the propensity score in the first stage. ***Significant at the 1% level, **Significant at the 5% level, *Significant at the 10% level. Robust standard errors in parentheses.

18
(domestic) migration in pooling risk within extended families includes Lucas and Stark (1985), Rosenzweig and Stark (1989), and Paulson (2003).

Yang and Choi (2007) and Yang (2008a) emphasize credible identification of the effect of negative shocks on international remittances. Existing studies of the impact of household income on remittance receipts use cross-sectional data, and so are subject to potentially severe biases in directions that are not obvious a priori. Reverse causation is a major concern: productive investments funded by migrant remittances can raise household income, leading to positive correlations between household income and remittances. Alternately, remittances may reduce households' need to find alternative income sources, leading to a negative relationship between remittances and domestic-source income. Even if reverse causation from remittances to income in migrants' source households was not a problem, it would be difficult to separate the cross-sectional relationship between income and remittances from the influence of unobserved third factors affecting both income and remittances (for example, the entrepreneurial spirit of household members).

Yang and Choi (2007) resolve these identification problems by focusing on income changes for the migrant-sending family due to shocks—changes in local rainfall—that are credibly exogenous, so that bias due to reverse causation is not a concern. Among households in the Philippines with members who are overseas migrants, they find that changes in income from domestic sources lead to changes in remittances in the opposite direction of the income change: remittances fall when income rises, and remittances rise when income falls. In such households, the amount of insurance is large: roughly 60% of exogenous declines in income are replaced by remittance inflows from overseas. In a similar vein, Clarke and Wallsten (2004) find, using panel data from Jamaica, that remittances from overseas replaced 25% of damages from Hurricane Gilbert in 1992. Yang (2008a) examines the impact of hurricanes on international financial flows using country-level panel data and finds that, for the poorest developing countries, hurricane damage leads to large inflows of migrants’ remittances, amounting to 20% of experienced damages. Strikingly, the remittance response to hurricanes for these countries is large in magnitude: roughly one-quarter as large as the response of foreign aid.

13
|TableIXDistri|butionofUnemploymentSp|ellsbyDuration|
|---|---|---|
|Duration ofspell(inmonths)|Numberofspells|Percentofallspells|
|Lessthan3|1,196|48.60|
|4to6|681|27.67|
|6to12|458|18.61|
|12to24|96|3.90|
|Morethan24|30|1.22|
|Total|2,465|100.00|

Source: Labor Force Survey 1996:1-1996:4, 1999:1-1999:4, and 2000:1-2000:2.

|TableXUnemploym|ntDurationbyUnemploymentIn|srance(UI)Recipiency|
|---|---|---|
|Durationofspell<br>(inmonths)|%ofallspellswithUI<br>.|%ofallspellswithoutUI|
|Lessthan3|21.40|48.35|
|4to6|21.02|16.19|
|**6**to12|25.90|14.38|
|12to24|19.92|11.17|
|Morethan24|11.77|9.92|

Source: Unemployment Registry, 1990-2000.

Table XI Distribution of Spells b -Actual- Duration of Benefits **(both** in months)

|Benefits|0-3|4-6|7-9|10-12|
|---|---|---|---|---|
|**Sp **ell_<br>|||_<br>_<br>_<br>_<br>_<br>_<br>_|_<br>_<br>_<br>_<br>_<br>_<br>_|
|Oto3|61.05||||
|**4**to6|15.06|31.10|_||
|6to12|13.98|29.24|49.47|23.80|
|12to24|6.25|23.83|33.50|55.40|
|Morethan24|3.66|15.83|17.03|20.80|
|Numberofspells|4,790|6,903|1,417|204|

Source: Unemployment Registry, 1990-2000.

52
the expected returns to malaria prevention behaviors such as ITN use<sup>29</sup> . It is also important to report that, during the 6 months preceding the survey, respondents in treatment villages did not receive significantly more information on ITNs, early seeking behavior and environmental management, compared to those in the control group. These variables are not statistically different in treatment and control groups, either when we look at them individually or jointly. Any changes in information and knowledge are likely to be a direct consequence of the IRS campaign.

Viewed from the perfect information model, and in light of the fact that ITN and IRS are much more likely to be substitutes than complements (if IRS kills the mosquitoes inside the house, individuals will feel less compelled to sleep under a net), it would be difficult to explain our result that ITN ownership and use increased in treated villages. Our results are consistent with the model we developed in Section 4. In response to the introduction of IRS in a community, its inhabitants experience an increase in awareness and concern about malaria (especially about the danger of mosquito bites), which affects their ownership and use of ITNs. More generally, by introducing a program in a community, be it a health, education, or other type of program, a government potentially provides information about its knowledge of the problem addressed by the program, or it just makes the problem more salient in the minds of community members. When individuals have imperfect information and face uncertainty about the importance of the particular problem at hand, such revelation of information may lead individuals to update their beliefs and, as a result, change their behaviors. These changes are generally not expected by those designing the program, while this section shows that they can be quite important. We recognize that our results on information can be seen as a bit tentative, but they are certainly suggestive of the possible importance of the mechanism we emphasize.

In addition to using bed nets, in response to the IRS campaign, individuals can engage in other preventive behaviors to reduce the risk of malaria infection. For example, they can keep any cattle away from home, cover any stored water and participate in environmental management campaigns, among others. Table 6 focuses on participation in LHM 29The treatment effect is unaffected by introducing controls on average past exposure of malaria, proxied by the 2000-2009 average Normalized Difference Vegetation Index (NDVI) across sub-zones. See Appendix B.2.

17
-'32-

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specification, which indicates that, ceteris paribus, intake probability of animal-source foods increases rapidly after the first months of life and then passes an inflection point toward an asymptote around 24 months. Our monthly results are consistent with significantly-expanded fish supplies in the period September - December (per De Graaf (2003)). Controlling for monthly effects, we find that fish supply was particularly ample in 2004 and somewhat greater than the 2000 supply in the other survey years.

# **3.3 Temporal Impacts:  Mother’s Education and Economic Status**

We use the probit results to predict monthly animal-source food intake probabilities for 23-monthold children in 2014. Figure 2 presents monthly results by mother’s education group for a household in the lowest asset quintile. Figure 3 presents monthly results by asset quintile for a mother with zero years of education.

The figures highlight two particularly-striking features of the results. First, as previous research has suggested, both mother’s education and household economic status have large impacts on the probability of child intake of animal-source foods. For households in the lowest asset quintile (Figure 2), the median difference in intake probability of animal-source foods for children of the most highly-educated and leasteducated mothers is 24%.<sup>4</sup> For mothers with no education (Figure 3), the median difference in intake probability of animal-source foods for children in the lowest and highest asset quintiles is 16%.<sup>5</sup> Table 5 provides evidence on the joint impact of mothers’ education and economic status. In January, child animal-source food intake probability for mothers with no education in the lowest asset quintile is 38%. In contrast, the probability is 78% for mothers with post-secondary education in the highest asset quintile - - an increase of 40%.

> 4 This is the median probability difference over 12 months for mothers with no education and those with post-secondary education.

> 5 This is the median probability difference over 12 months for mothers with no education in the lowest-asset quintile and those in the highest-asset quintile.

10
<!-- Start of picture text --> 0 10 20 3gkm % change in the budget dedicated<br>aX ouYy to% change housing + transport<br>"&a SSRN — Road network<br><!-- End of picture text --> <!-- Start of picture text --> ee = i SOR * Radios with lowest socio-eco level<br>Woe<br><!-- End of picture text --> <!-- Start of picture text --> ge. Al Ran<br><!-- End of picture text -->
<!-- Start of picture text --> Household Income Quintiles by type of Employment<br>In European Regions, 2008-2009<br>Employee with a contract Employee without a contract<br>West East South North West East South North<br>1 2 3 4 5 NA 1 2 3 4 5 NA<br>100 100<br>80 80<br>60 60<br>40 % of workers 40<br>20 20<br>0 0<br><!-- End of picture text --> Regions sorted descending by share of low-income households among workers without a contract Data not weighted by countries' population: A worker from any country is equally likely to be sampled

# **Figure 18 Household income quintiles among formal and informal employees in European regions** _Source:_ Calculation with ESS data.

The results of descriptive analysis are reinforced by ordered probit results (Table 8 and Figure 19), which suggest that after controlling for a rich set of individual characteristics (including parental background, as well as minority/migrant background), informal employees are having the largest financial difficulties among all categories of employed population (yet they fare much better than the unemployed and discouraged).

The situation of informal self-employed varies by country group: in the UK, Ireland, Austria and Netherlands, they are as well off as formal employees; in France, Belgium, Germany, and Switzerland, as well as in the Nordic countries and in Southern Europe, they are better off than otherwise similar formal employees, but not by much; and in Eastern Europe they are substantially better off<sup>23</sup> .

Note that these results are not subject to selection bias because non-response to the question on self-assessment of household‘s financial situation is quite small, and nonemployed population is included in the sample. Yet we cannot claim that the relationship is causal because the same unobserved factors can influence propensity to work informally and

> 23 In this analysis, we do not account for coordinated labor supply decisions within household. This awaits a separate study (there  are, however, some data limitations).

38
The minimum value of the index is 1 when no intermediate inputs are used to produce final goods or services.

A greater length of the GVC means more opportunities for tapping value added for a country like Brazil. Longer chains suggest the GVC sector is more complex, with more stages done in the nation and possibly more opportunities for upgrading. Similarly, an increase in the length of the GVC over time also suggests that this value chain has become more complex. Figure 7 presents the length of the chain across countries in our selected GVC sectors.

For all sectors Brazil has a relatively larger domestic length of the supply chain while internationally it participates in rather short supply chains. Even though automotive, metals and chemicals show stronger foreign value added embodied in Brazil’s gross exports than agribusiness, compared to its peer countries Brazil does not participate in these chains with many international stages. On the other hand, Brazil shows a greater length of the domestic value chain in automotive and agribusiness. These sectors have a higher total average number of production stages domestically, indicating that the GVCs in these sectors are relatively more complex. In all cases, Canada shows a similar length of the value chains compared to Brazil, but a greater share in its international participation as a buyer than Brazil.

Figure 7: Average length of the value chain, by sector and country (2009)* <!-- Start of picture text --> Agribusiness Metals<br>BRA ARG ZAF TUR THA IDN CAN MEX BRA ZAF ARG IDN TUR MEX CAN THA<br>Domestic (NPS) International (NPS) Domestic (NPS) International (NPS)<br>Automotive Chemicals<br>BRA ZAF IDN TUR ARG CAN MEX THA BRA ZAF MEX TUR ARG CAN IDN THA<br>Domestic (NPS) International (NPS) Domestic (NPS) International (NPS)<br>2.5 2.5<br>2 2<br>1.5 1.5<br>1 1<br>.5 .5<br>0 0<br>3 2.5<br>2<br>2<br>1.5<br>1<br>1<br>.5<br>0 0<br><!-- End of picture text --> Source: Authors’ calculations using OECD/WTO TiVA. Note: * No updates for 2011.

12
research and development; number three Chongqing Lifan, a producer of engines and motorcycles; and number four Xianghuoju, a leading maker of spark plugs that turned private in 1993.

<!-- Start of picture text --> Auto Parts Sales 1995-2002<br>30<br>28.16<br>25<br>20<br>17.75<br>15<br>14.12<br>10 10.99<br>10.13<br>8.59<br>7.84<br>5 6.58<br>0<br>1995年1996年1997年1998年1999年2000年2001年2002年<br>Year<br>Billion US doll<br><!-- End of picture text --> Government policy and market pressure have resulted in considerable consolidation among the top producers of motor vehicles and their components. One survey found that the number of parts producers declined by well over half from 1996 to 1998 (5,321 to 2,321) and only increased modestly in the next few years, despite the dramatic increase in output. According to another survey, the number of firms with over USD $600,000 in revenue dropped from a peak of 340 in 1996 to just 213 in 1999, even though the parts market grew by almost 50 percent over that period. The number of large firms barely increased over the next three years (217 firms in 2002), even though industry sales increased another 50 percent. . In other words, economies of scale at the leading assemblers and parts producers increased sharply over the late 1990s and the first few years of the new century (KMPG Transaction Services 2003).

Geographically, auto parts production is concentrated in several regions, mostly along 45