rafmacalaba/datause-encoder-data huggingface dataset:
for displaced women, in particular, should have built-in guidelines for the protection of women and should engage men in promoting more gender-equitable relationships (Heilman & Barker, 2018; van der Gaag et al., 2019). Given the long-term nature of displacement in Colombia, it is important to build capacity for both displaced women and men to access economic opportunities, which can eventually replace social assistance. Program interventions should identify occupations and sectors where they could work given their skills and include support services, such as flexible working hours and childcare to address constraints related to domestic responsibilities.

Finally, the risk of sexual and GBV in situations of displacement has devastating impacts on the individual and on the household. This is a complex issue and requires a battery of interventions for prevention and response. Interventions that have worked in non-displaced settings such as strategies for addressing norms that condone violence against women, designing effective facilities and services for survivors, and engaging men and boys in prevention and response could be piloted in situations of displacement (see Jewkes et al., [2015] and Ellsberg et al., [2015]).

This study has limitations. Large-scale household surveys rarely include questions to identify and measure a norm. Moreover, the choice of proxy indicators for attitudes and behaviors is driven by data availability and some of them could arguably be mapped onto multiple spheres or domains of gender norms. Given data limitations, mixed methods studies can offer more insights as the economics literature on the gender-differentiated effects of conflict-induced displacement evolves. The limited time span covered by the migration question might pose a challenge to capturing changes in gender norms. Nonetheless, the theoretical framework argues that displacement can accelerate change by, for instance, opening up economic opportunities for women in urban areas and reducing exposure to traditional structures that reinforce gender norms (Cislaghi & Heise, 2020; Harper et al., 2020; Marcus & Harper, 2015; Muñoz Boudet et al., 2013). The lack of data for men is another limitation. Gender norms are produced and reproduced by women and men; hence, the analysis omits part of the story. On the other hand, focusing on women’s views and their own decision-making power within the household provides an overview of intra-household dynamics that could hint at men’s and other household members’ gender norms.

30
Annex C _Epert AssesementB_ _77_ Adopting the recommended rates in full for the other regions leads to a substantial rise in the world mortality rate from polio from .5 to 1.6 per 100,000, and an increase in the number of deaths worldwide from 25 to 77 thousand.

The inaease involves three regions with little data: Sub-Saharan Africa, the Middle East and North Africa, and Asia. Recommended rates at ages 0 and 14, the age groups most affected, range from 12 to 43 in these regions. Most of the reported rates for 1970 (most countries reported some deaths) are at or below the bottom of this range. For 1985, there are virtually no useful data-this cause is missing for all the Latin American countries due to reporting procedures adopted in the region for TCD-9, and includes no deaths everywhere else except in three countries, for which rates per 100,000 are as follows:

||Age 0|Age 14|
|---|---|---|
|Korea, Rep. of|15.2|15.9|
|Sri Lanka|1.3|1.6|
|Thailand|0.8|0.2|

Therefore, reports of deaths to WHO are well below the expert assessments.

_Yellow fever, dengue, and encephalitis_ The recommendations on this category, largely judgmental, were these: (a) that yellow fever deaths in Sub-Saharan Africa be estimated at 15,000 under age 15 and 5,000 at age 15 and over, out of total numbers of cases of 75,000 and 25,000 respectively; (b) that deaths in Latin America and the Caribbean be estimated at 10 under 15 and 70 at 15 and over, out of total cases of 11,000 and 7,000 respectively; and (c) that malefemale ratios equal one, and cases at 0 and 14 be proportional to dte size of the age groups.

The equations for yellow fever were not satisfactory, and produced an irregular age pattern (not visible in preceding tables because rates are so low). The first step in revision was to smooth the rates, redistributing deaths across age-sex groups without altering the total, to give the same rate for all those under 15 and a second rate for all those 15 and over.

Next, rates were adjusted for Sub-Saharan Africa to give the recommended number of deaths, which is nine times the regression estimate. A rate of 7.2 per 100,000 for those under 15 resulted. Rates of this order were in fact reported to WHO for children under five in 1970 (for Portugal and Uruguay); in 1985, however, virtually no deaths from this cause were reported.

Rates for Latin America were nzt adjusted, because they would have to be lower than those for developed countries to give the recommended numbers of deaths. The regression estimates were intermediate between developed countries, on one hand, and all other developing regions, on the other, and these were maintained.
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Needless to say, poverty does not affect people and regions evenly throughout the country. In 2001, the more one moves from the Atlantic coast and southern Cameroon towards the interior and the north, the larger the share of people living below the poverty line.

# **1.2 Trends in Cameroon’s Trade**

Cameroon’s trade has grown considerably during the three last decades. In domestic currency, imports and exports have increased at average annual rates of 5.49 percent and 6.53 percent, respectively, between 1983 and 2003.<sup>3</sup> This expansion is greatest after 1994, i.e. in the post-devaluation<sup>4</sup> and new economic recovery era. In fact, Cameroon’s external trade actually declined during the 1986-1993 economic crisis, with imports and exports falling by an average of 9.7 and 6.0 percent per year, respectively. But the economic recovery beginning in 1994 has been characterized by a significant resurgence of external trade, beyond the devaluation’s immediate mechanical effect.

Imports have grown faster than exports during this post-devaluation period. From 1995 to 2003, the current value of imports have increased by a factor of three with a 13.2 percent annual average growth rate, while the current value of exports has increased by a factor of only 1.7 with an average growth rate of 7.0 percent per annum during the same period. This has resulted in a continuous fall in rate of coverage of imports by exports<sup>5</sup> from 164 percent in 1995 to 104 percent in 2003, with a low of 95 percent in 2001. Indeed, 2001 and 2002 are the only year for which Cameroon has recorded a trade deficit since 1988.

However, the net-of-oil trade balance has been in deficit continuously since 1997, reflecting the country’s dependency on oil exports. Cameroon has produced crude oil since 1977

> 3 If not indicated precisely, statistics presented in this subsection have been processed from Cameroon National Institute of Statistics (2004).

> 4 Cameroon and other CFA franc countries experienced a 50 percent devaluation in January 1994.

> 5 The coverage rate, that is the ratio of exports over the imports, may be seen as the ability of a country to pay its imports using its export revenues.

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quality. A large number of studies seek to assess the magnitude of such effects (e.g., Pritchett and Viarengo, 2015; Sass et al., 2012; Hanushek and Rivkin, 2006), in some cases adjusting for family background. However, studies of this sort are rare for low-income contexts,<sup>9</sup> and even fewer attempt provide a multi-country analysis, as we do here. Furthermore, just a handful explicitly estimate the variance contributions of both schools and households (e.g., Carneiro, 2008), and since most such studies are school-based they generally rely on a relatively limited set of observed proxies for family background.<sup>10</sup> For instance, Freeman and Viarengo (2014) use PISA data to investigate the (sources of) variance in school effects. They report that a regression of test scores on school dummies (only) explains around two-thirds of the variation in the data, while a limited set of observed family background variables accounts for just one-third, after controlling for school effects (also via dummies).

The general point emerging from the above (albeit brief) review is that existing studies have generally focused on estimation of either the contribution of households or of schools to variation in educational outcomes. These are of substantive interest, but such estimates correspond to special cases where the contribution of any covariance (sorting) between these effects is effectively absorbed into the main factor under consideration. Furthermore, even in cases in which such upper-bound estimates are tightened by introducing additional controls, such as proxies for one of the sets of effects, past studies have not reported the full variance decomposition incorporating the implied covariance between households and schools. However, it is precisely this covariance that can be of critical interest: it tells us about the extent to which schools – or educational policies more generally – exacerbate or compensate for differences deriving from given family background. In light of this gap, the next section outlines how a more complete decomposition can be elaborated.

# **2.2    Decomposition methods**

Assuming that the household and school factors are not fully observed, decomposing their variance contributions is non-trivial. However, as hinted above, estimation of the upper bounds for each factor is straightforward and can be derived simply by treating each factor (separately) either as a fixed or as a random effect. Variants of this approach have been applied extensively (e.g., Raaum et al., 2006; Lindahl, 2011; Gibbons et al., 2014) and, under additional assumptions, also can be used to identify approximate lower bounds on the variance contribution of the second factor. Similarly, random effects (mixed-linear)

> 9 See Behrman and Birdsall (1983) and Dang and Glewwe (2018) for two studies that respectively investigate school quality in Brazil and Vietnam.

> 10 School-based studies are frequently limited to one or two grades and therefore do not contain data on multiple siblings. However, such studies do tend to provide rich data on school-specific characteristics.

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with greater use of export restrictions. These findings seem to be driven by measures that were still in effect at the end of the sample period (i.e. that were not removed within the seven-month period under consideration) (Table 2). Indeed, conditional on measures not having been removed during the period there is some suggestion that larger countries may also pursue import liberalization, i.e., to operate on both margins, presumably with the aim to expand domestic availability of medical products. This finding is consistent with terms-of-trade models of trade policy.

**Table 1: Number of export and import measures targeting medical products**

|**Variables**|(1)<br>**lib_m**|(2)<br>**res_m**|(3)<br>**lib_x**|(4)<br>**res_x**|
|---|---|---|---|---|
|Ln(Total_stepsj)|2.20**|6.28**|2.89**|1.35|
||(1.08)|(3.02)|(1.47)|(1.22)|
|Ln(Total_timej)|0.39|1.34|-0.60|1.72**|
||(0.31)|(0.91)|(0.76)|(0.71)|
|Eprocj|0.03|-0.84*|0.47|0.30*|
||(0.11)|(0.46)|(0.34)|(0.18)|
|Ln(Num_DPAj)|-0.31**|-1.97***||-0.04|
||(0.15)|(0.63)||(0.16)|
|GPAj|0.72**|0.56|2.68***|0.67*|
||(0.33)|(0.79)|(0.85)|(0.38)|
|Ln(POPj)|0.09|0.16|0.08|0.40***|
||(0.07)|(0.17)|(0.17)|(0.13)|
|Ln(MPj)|-0.08|0.38|-0.24**|0.23**|
||(0.07)|(0.27)|(0.11)|(0.12)|
|GEj|0.34*|2.06**|-0.62|0.75**|
||(0.18)|(0.87)|(0.38)|(0.35)|
|Mshj|1.76|3.91|7.32|-4.03|
||(3.34)|(11.62)|(10.88)|(5.97)|
|RCAj|0.18|1.50***|1.72**|-0.07|
||(0.23)|(0.56)|(0.82)|(0.53)|
|Ln(1+Tarj)|0.44**|0.23|0.44|-0.46|
||(0.22)|(0.48)|(0.46)|(0.32)|
|Ln(Covid_casesj)|0.05|0.37*|-0.49|-0.09|
||(0.10)|(0.21)|(0.32)|(0.17)|
|Ln(Covid_deathsj)|0.12|0.06|0.32|0.06|
||(0.10)|(0.23)|(0.29)|(0.14)|
|Constant|-10.15**|-32.18***|-6.67|-16.60***|
||(3.98)|(7.22)|(9.12)|(6.25)|
|Observations|108|108|108|108|
|R-squared|0.54|0.84|0.65|0.45|

**_Note_ :** Robust standard errors, clustered by country, in parentheses. Levels of significance: *10%, **5%, ***1%. **_Legend_ :** lib=liberalizing; m=import policy; res=restrictive; x=export policy; GE: government effectiveness; DPA: deep procurement agreement; GPA: WTO Government Procurement Agreement; RCA: revealed comparative advantage; POP: population; MP: measure of geographic distance to global markets.

12
> <sup>20</sup>

allocations that MPs had at their disposal in 1999 and additional yearly allocations from 1999 on. As discussed in previous sections, this is our measure of the effort exerted by MPs to bring local infrastructure to their constituents.

The variable _PartyStronghold_ equals one if the party won every election in constituency _d_ in state _s_ in the 1990s (in the 1991, 1996, 1998, and 1999 elections) and zero otherwise. This was a period of substantial electoral volatility and emerging incumbency disadvantage, supporting our interpretation of the _PartyStronghold_ indicator variable as identifying those constituencies where voters are particularly attached to political parties.

The variable _CandidateStronghold_ equals 1 if the same person has been elected into office in constituency _d_ in state _s_ in every election between, and including, 1991 and 1999, irrespective of his/her party affiliation. We include this variable to test whether a party could have an electoral lock on a constituency for reasons other than the citizens’ intrinsic preference for the party. The most important is that parties might be dominant because they have been fortunate in finding a candidate whose personal characteristics are highly valued by voters.<sup>19</sup> Of the 483 constituencies in our analysis, 97 (20 percent) are party strongholds, and of these the party switched the nominated candidate in 61 districts, retaining the same candidate in the remaining 36 districts. We test whether the effect of party stronghold is different in constituencies where a party switched its candidate than in constituencies where there was no such switch. That is, we estimate the following specification: (2)

> 19 The total number of candidate stronghold constituencies in our sample is 42, of which only 6 are candidate strongholds without being party strongholds (where the dominant candidate switched his/her party affiliation). The Indian data therefore do not suggest that candidates can maintain dominance without a dominant party.
# **Table 8. Stability of Rankings Based on Various Program-Level Outcomes and Value-Added Contributions**

## _Panel A. Correlation of Rankings_

||Grad|uation|Formal E|mployment|
|---|---|---|---|---|
||Outcome|Value Added|Outcome|Value Added|
||(1)|(2)|(3)|(4)|
|Formal employment|-0.00|0.08|||
|Wages (log)|-0.05|0.05|0.54<sup>***</sup>|0.50<sup>***</sup>|

_Panel B. Percent of Programs that Move 3 or More Deciles Across Rankings_

||Grad|uation|Formal E|mployment|
|---|---|---|---|---|
||Outcome|Value Added|Outcome|Value Added|
||(1)|(2)|(3)|(4)|
|Formal employment|57|55|||
|Wages (log)|51|51|34|34|

_Source_ : Authors’ calculations using administrative data.

_Notes_ : This table shows the stability of program rankings based on program-level average outcomes or value added (6 rankings in total). Panel A shows the correlation among rankings based on a given outcome or value added. For instance, the correlation of the rankings based on (log) wages and formal employment is 0.54, whereas the rankings based on (log) wage value added and formal employment value added have a correlation of 0.50. Panel B shows the percent of programs whose decile position differs by at least 3 deciles across rankings. Since the number of programs for which we estimate value added differs across outcomes, we compute only these correlations for the programs that are common across all samples. * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01.

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**Table 4: Ratio of Observed TIIs to Predicted TIIs**

|**Pt**||||**Ex**<br>|**porting**<br>|**Countr**<br>|**y**<br>||||
|---|---|---|---|---|---|---|---|---|---|---|
|**arner**<br>**Country**|**India**<br>||**Paki**<br>|**stan**<br>|**Bangla**<br>|**desh**<br>|**Sri La**<br>|**nka**<br>|**Nep**<br>|**al**<br>|
||**1990**|**1998**|**1990**|**1998**|**1990**|**1998**|**1990**|**1998**|**1990**|**1998**|
|India|-|-|0.41|0.92|0.49|0.76|0.37|0.40|5.61|6.07|
|Pakistan|0.34|0.56|-|-|1.79|1.90|1.93|1.25|0.83|0.17|
|Bangladesh|4.83|5.46|9.60|3.13|-|-|1.14|0.28|1.51|1.27|
|Sri Lanka|2.37|3.34|4.17|3.23|1.76|0.10|-|-|0.41|22.15|
|Nepal|3.84|8.50|0.40|1.08|6.66|7.30|0.06|0.51|-|-|
|Maldives|2.19|0.64|0.86|0.95|N/A|N/A|10.01|11.73|NA|-|
|Bhutan|-|4.03|0.07|0.17|0.58|1.71|N/A|N/A|N/A|1.18|
|US|4.99|1.53|1.46|2.37|3.13|2.59|16.02|2.58|37.31|0.70|
|UK|0.76|0.56|0.81|0.97|0.76|1.23|0.80|1.49|2.05|0.30|

Note: Calculations are based on data from the IMF Direction of Trade Statistics Overall, the trade intensity indices and their movements from pre-reform to post-reform periods do not demonstrate a consistent positive relationship for all countries in the South Asia region. A positive increase in the TII appears only between India and Nepal; in other cases, the bilateral relationships are asymmetric and/or in decline. The evidence also supports the earlier observation that South Asia does not appear to fit the hypothesis that geography is a strong determinant of trade. Instead, those countries with which the South Asian countries demonstrate real intensive relationships (that is, those with TIIs above the regression line) tend to be well dispersed along the distance axis. The evidence of irregular, incoherent “trade intensity”, given the geographic proximity and relative size of trade, cannot be interpreted as a positive sign for a rapid increase in regional trade.

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# I. Introduction

Tropical cyclones (hurricanes, typhoons) have become the icon of climate change with pictures from space parading the front covers of many climate change books and even a new journal on the Economics of Climate Change. The Intergovernmental Panel on Climate Change (IPCC) argues that tropical cyclones and other extreme events are an important reason to support greenhouse gas mitigation efforts. Several scientists report an increase in tropical cyclone intensity over the last 30 years (Emanuel 2005, IPCC 2007a). The IPCC (2001) and Swiss Re (2006) report dramatic increases in tropical cyclone damages over time.

And yet despite these findings, the link between climate change and tropical cyclone damage remains controversial. Partly this is due to the fact that tropical cyclones are rare events and appear to be subject to long term cycles so it is difficult to detect changes in underlying frequencies and severity (Landsea et al. 1999; 2006). There are approximately 50 tropical cyclones a year globally but some years can have very few and others over a hundred. Many things changing over time can influence the damages from storms, most noticeably population and income (Pielke et al. 1998; 2008). Accounting for changes in the vulnerable population and capital in the path of storms, it is not clear that there is any trend in tropical cyclone damages (Pielke et al 1998; 2008). The historic record may simply not be long enough and clear enough to detect how climate may be affecting tropical cyclones.

The average current global damage from tropical cyclones is currently $26 billion/year (EMDAT 2009). Several authors have relied on the general result by Emanuel et al. (2005) that tropical cyclone intensity (wind speed cubed) would increase for the US with warming. Assuming damages increase with the cube of wind speed, US damages would increase by 56 percent (Hallegate 2007). However, most authors find that damages are proportional to a higher order power of wind speed and they predict that tropical cyclone damages in the US would double (Nordhaus 2006; 2010; Pielke 2007). Narita et al. (2008), using the FUND model, estimate that global tropical cyclone damages would also double. These analyses assume that every storm becomes more intense by a constant percentage.

2
27 (although somewhat higher than) the estimates of measurement error from cross coder reliability correlations in table 2 above.

In using this technique, one would like to test whether the assumptions made in obtaining the IV results are valid. Note that since one variable may be endogenous-beneficiary participation-at least one instrument must be used to identify the model<sup>17</sup> . But if one instrument can be unambiguously accepted-that without argument it directly affects only the independent variable-then the validity of any other instruments can be tested. Indeed, the entire set of instruments can be tested.

17 Heuristically the problem is that we need to test the exclusion of the instrument from the performance equation. However, one cannot test the exclusion directly (say by a t-test of the inclusion of the instrument) because in the presence of endogeneity the coefficient on the potentially endogenous variable is inconsistent when not instrumented and hence the t-test on the instrument would be biased. As a simple example say the model is _y_ = P *x + 8 *z + e and x is endogenous. Say there is a single potential instrument z, say x = ,r*z +xj . But z is a valid instrument only if 8 = 0 However, this hypothesis can onlytbe tested if there is a consistent estimate for P . But if z is used as an instrument for x, then the 'instrumented" x is perfectly collinear with z (since the instrumented x is just x projected onto z). But since the 'instrumented" x is collinear it is obvious one cannot use z to both recover a consistent estimate of P and to estimate 8 to test the exclusion restriction because using only z both cannot be identified separately. Therefore sufficient "exclusion restrictions"(such as 8 = 0 ) must be imposed a priori and the 'just identifying" assumptions cannot be tested.
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Figure 6: Regional log Formal Employment - 1992-2010 <!-- Start of picture text --> 7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>-1<br><!-- End of picture text --> Each point reflects an individual regression coefficient where the dependent variable is the change in regional log formal employment from 1991 to the year listed on the x-axis, calculated using RAIS. For all years, the independent variable is the regional tariff change reflecting tariff changes from 1990-1995 (described in the text), with state fixed effects and pre-trend control. Positive estimates imply larger formal employment declines in regions facing larger tariff declines. Vertical bar indicates that liberalization was complete in 1995. Dashed lines show 95 percent confidence intervals. Standard errors adjusted for 112 mesoregion clusters.

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