rafmacalaba/datause-encoder-data huggingface dataset:
now introduce asymmetry in trade costs across sectors. We know that at the disaggregate product level, trade costs vary substantially across products due to certain product characteristics. Trade cost differences across products are likely to be much larger than within a certain product category.<sup>22</sup> Owing to the simplicity of our basic model, the above-mentioned results can be re-interpreted. The product (sector) with high trade costs displays high average export prices; whereas the product (sector) with low trade costs allows more firms to enter export markets and thus has lower average export prices.

**Result 5: In a multi-sector framework, products (sectors) with high trade costs have higher average export prices (qualities) compared to products with low trade costs. With increasing distance between trading partners, a higher share of products with low trade costs enter the export bundle.**

# **3. Empirical Evidence**

# **3.1 Data**

In order to test our model empirically, data on the quality level of trade flows are needed. However, estimating the quality level of trade flows is not an easy task. The most commonly used measurement of quality is via unit values of trade flows (e.g. Hummels and Skiba, 2004). This measurement of quality performs better the higher the digit level at which trade data are recorded. International trade data is typically only available up to the six-digit level. Some countries collect data on higher digit levels which allow for a thorough analysis of quality issue. Schott (2004) uses this type of data to study the quality level of US imports. However, for the majority of countries only trade data at the six-digit level is recorded. The main data set used in this paper is at the eight-digit level and thus our measurement of quality might not be perfect, however, at the moment, we do not dispose of a more accurate method.

In order to test our first two results empirically, not only detailed international trade data are needed, but also data on intra-national trade flows. Data for our first sample come from the

> 22 For example, Hummels and Skiba (2004) find substantial trade cost differences between different product categories.

20
_Table 6: Percentage of displaced individuals deprived in selected indicators by gender_

||Ethi|opia|South|Sudan|Su|dan|
|---|---|---|---|---|---|---|
||Male|Female|Male|Female|Male|Female|
|Years of schooling|55|78<sup>***</sup>|36|63<sup>***</sup>|32|46<sup>***</sup>|
|School attendance|16|19<sup>**</sup>|21|29|23|23|
|Early marriage|3|13<sup>***</sup>|8|75<sup>***</sup>|6|50<sup>***</sup>|
|Unemployment|7|5<sup>***</sup>|2|0<sup>*</sup>|3|3|
|Legal id|45|46|48|74<sup>***</sup>|10|10|

Source: Authors’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). Asterisks indicate statistical significance of mean differences between male and female at 1% ***, 5% ** and 10% * levels.

Returning to patterns of household headship, Figure 5 breaks down the variation in censored headcount ratios among refugee households in Ethiopia, depending on the gender of the household head. The largest differences between households headed by men and women is for early marriage (42.3 p.p.), waste disposal (37.2 p.p.), and housing (37.1 p.p.), whereas the pregnancy care and school attendance have the smallest differences (3.3 p.p and 3.0 p.p.). Considering that girls and women of reproductive age are more likely than their male counterparts to live in income-poor households below the international poverty line (Boudet et al. 2018), the interaction of multidimensional poverty, gender of the household head, and displacement status in Ethiopia cannot be neglected. The observed gender difference disadvantaging female-headed households in terms of the MPI and incidence in Ethiopia derives from the difference among refugees. The breakdown of indicators here opens up questions for policy makers to ask how and why these differences in waste disposal and housing are occurring for households with different genders in headship, given that both contemporaneous features of displacement rather than indicators such as early marriage that denote deprivation which may have occurred before or as part of displacement. Thus, to see this interaction further, we extend our analysis by type of female heads and household earnings profile.

_Figure 5. Censored headcount ratios of refugee households in Ethiopia, by sex of the household head_ 21
considered accurate and authoritative, but which has not been hitherto systematically analysed.<sup>2</sup> While various aspects of the TPR could be strengthened, (Keesing, 1998), There is little dispute over the accuracy of the information they contain. For the countries for which only a GATT period TPR was available (i.e. before 1995) however, it has been necessary to update the TPR information based on recent country notifications, e.g. regarding tariffs, which contain URA commitments. In others, such as antidumping measures or LDC problems, different and more complete data exist in the WTO and have been used to supplement TPR based infornation. In still others, for example market access issues, additional outside sources have been used, mainly from the OECD.

The group of developing countries on which the study is based includes thirteen developing economies from Latin America and the Caribbean, thirteen from Asia and the Pacific, eleven from Sub-Sahara Africa and five from Europe, Middle-East and North Africa. A detailed list of the countries can be found in the Appendix Table A- 1. They account for the bulk of the trade of developing members of the WTO (see below). The main limitation of the group of developing countries on which the study is primarily based is that it contains only four least developed countries (Bangladesh, Benin, Uganda and Zambia) out of a total of 29 which are members of the WTO. On the other hand, the study has used information regarding the challenges least developed countries face in integrating into the multilateral system developed in the context of the assessments of trade-related technical assistance needs prepared for 38 LDCs in 1997-1998 (WT/COMTD/IFI-38, 1997-1998). Also, the analysis is limited to merchandise trade only, excluding services, partly in order to make the scope of the project more manageable and partly because the TPRs did not cover services before the establishment of the WTO.

> [By the end of 1998, TPRs had been prepared for 47 developing economies, members of the WTO. Five of these (Burkina Paso, Jamaica, Mali, Trinidad and Tobago and SolomonI **s** lands) are not included in the study, as the TPR was prepared after the data base for the study was completed.

> 2 While several aspects of the TPRs in principle could be strengthened, (see Keesing ,1998), it is importan **t** o recall that there are serious limits to what can be done in that respect: There are inherent limitation **s** pelled out in the terms of reference for the TPRs, e.g. regarding their use in developing information on the consistency of

3
**Table 4. Discrete time hazard analysis of the effects of household level shocks on first‐migration for men and women separately, including significance test of interaction with female from both‐genders model,  Albania LSMS 2005 (control variables not shown)**

|||**Male**<br>|||**Female**<br>||**Testing**<br>**Difference**<br>**in Joint**<br>**Model**<br>|
|---|---|---|---|---|---|---|---|
|**First Migration**|**OR**|**SE(b)**|**P>|z|**|**OR**|**SE(b)**|**P>|z|**|**P>|z|**|
|**Job Loss**|0.934|0.068|0.346|0.932|0.095|0.491|0.140|
|**Illness**|0.981|0.081|0.813|0.669|0.087|0.002|0.006|
|**Property Loss**|1.016|0.155|0.917|1.669|0.336|0.011|0.077|
|**Pyramid Crisis**|1.120|0.184|0.489|1.084|0.233|0.707|0.366|
|**Number of Cases**||**27028**|||**36134**||**63162**|
|**Log Pseudo likelihood**||**‐6188.2**|||**‐3478.6**||**‐9768.9**|

26
# **Table 5:  Parametric Hazard Regression: Cohort Analysis**

||(1|)|(2)|
|---|---|---|---|
|VARIABLES|Cohort1|Cohort2|Cohort1<br>Cohort2|
|**Micro factors**||||
|IFI involved|0.0366|-0.246***|-0.00178<br>-0.343***|
||(0.0725)|(0.0690)|(0.0799)<br>(0.0789)|
|Priv. share>80%|0.0232|0.265***|-0.254***<br>0.217***|
||(0.0700)|(0.0531)|(0.0854)<br>(0.0600)|
|OGM sector|0.345***|-0.421***|0.472***<br>-0.428***|
||(0.122)|(0.0771)|(0.128)<br>(0.0795)|
|Energy sector|0.118**|0.364***|0.173***<br>0.249***|
||(0.0578)|(0.0443)|(0.0621)<br>(0.0531)|
|**Macro factors**||||
|Democracy|-0.211**|-0.198***|0.0483<br>-0.0473|
||(0.0962)|(0.0768)|(0.104)<br>(0.0885)|
|Change in TOT|-0.349***|-0.707***||
||(0.0907)|(0.109)||
|Increase in input cost (avg)|||20.42***<br>-0.485|
||||(1.523)<br>(0.692)|
|Commodity windfall revenue (avg)|||0.209<br>-4.759***|
||||(0.286)<br>(0.542)|
|Constant|-41.78***|-11.92***|-58.51***<br>-10.97**|
||(4.023)|(3.615)|(5.585)<br>(4.677)|
|Observations|1,841|2,541|1,482<br>1,906|
|Ln(α)|1.872***|0.605***|1.907***<br>0.869***|
||(0.0174)|(0.0164)|(0.0198)<br>(0.0187)|

*** p<0.01, ** p<0.05, * p<0.1; Standard errors in parentheses.  Sample excludes government guaranteed contracts.  Log of income per capita in 2000 and the square term, as well as change in real GDP per capita growth are controlled.

30
Table A5: List of Replicated Papers

|Authors|Title|Journal|Year|Clustered<br>trial|No. of<br>Outcomes|No. of<br>Treatments|No. controls<br>inputted|No. of<br>Estimates|
|---|---|---|---|---|---|---|---|---|
|Wheeler et al|LinkedIn (to) Job Opportunities: Experimental Evidence from Job<br>Readiness Training|AEJ:<br>Applied|2022|Yes|3|1|16|3|
|Lopez et al|Does Patient Demand Contribute to the Overuse of Prescription<br>Drugs|AEJ:<br>Applied|2020|Yes|11|2|331|42|
|Abel et al|The Value of Reference Letters: Experimental Evidence from South<br>Africa|AEJ:<br>Applied|2020|Yes|3|1|28|9|
|Bertrand & Cr ́epon|Teaching Labor Laws: Evidence from a Randomized Control Trial in<br>South Africa|AEJ:<br>Applied|2021|No|12|1|115|12|
|Hussam et al (a)|The Psychosocial Value of Employment: Evidence from a Refugee<br>Camp|AER|2022|Yes|14|2|6|35|
|Hussam et al (b)|Targeting High Ability Entrepreneurs Using Community Information:<br>Mechanism Design in the Field|AER|2022|Yes|4|1|91|8|
|Armand et al|Does Information Break the Political Resource Curse? Experimental<br>Evidence from Mozambique|AER|2020|Yes|19|2|171|38|
|Andrabi et al<br>4|Upping the Ante: The Equilibrium Efects of Unconditional Grants<br>to Private Schools|AER|2020|Yes|13|1|284|48|
|Alsan et al<br>1|Does Diversity Matter for Health? Experimental Evidence from Oak-<br>land|AER|2019|Yes|21|3|12|63|
|Carneiro et al|The Impacts of a Multifaceted Prenatal Intervention on Human Cap-<br>ital Accumulation in Early Life|AER|2021|Yes|27|1|3340|54|
|Garbiras-D ́ıaz<br>&<br>Montenegro|All Eyes on Them: A Field Experiment on Citizen Oversight and<br>Electoral Integrity|AER|2022|Yes|9|3|39|96|
|Dhar et al|Reshaping Adolescents’ Gender Attitudes: Evidence from a School-<br>Based Experiment in India|AER|2022|Yes|3|1|61|10|
|Augsburg et al|When nature calls back: Sustaining behavioral change in rural Pak-<br>istan|JDE|2022|Yes|2|1|183|6|
|Heß et al|Environmental efects of development programs: Experimental evi-<br>dence from West African dryland forests|JDE|2021|Yes|3|1|648|6|
|Islam et al|The Efects of Chess Instruction on Academic and Non-cognitive Out-<br>comes: Field Experimental Evidence from a Developing Country|JDE|2021|Yes|30|1|14|30|
|Magnan et al|Information, technology, and market rewards: Incentivizing afatoxin<br>control in Ghana|JDE|2021|Yes|24|3|80|84|
|McIntosh & Zeitlin|Using household grants to benchmark the cost efectiveness of a US-<br>AID workforce readiness program|JDE|2022|Yes|18|2|332|189|
|Fernando|Seeking the treated: The impact of mobile extension on farmer infor-<br>mation exchange in India|JDE|2021|No|21|2|409|47|
# **V. Concluding Discussion**

We conducted a cluster randomized home based HIV testing and counseling intervention among young females in Malawi to disentangle its effects on risky sexual behavior and human capital investments. While HIV testing plays an unambiguously important role as a gateway to ART and, hence, would have indirect effects on HIV transmission through suppressed infectiousness after the start of therapy, its direct behavioral effects appear to be complex and not uniformly protective. Our main finding is that while HTC has no overall effect on STI risk or achievement test scores, it does have, consistent with economic theory, heterogeneous effects in subgroups. Among those who discover that they are HIV-positive, the risk of HSV-2 infection at follow-up increases substantially. On the other hand, individuals surprised by HIV-negative test results experienced a significant improvement in achievement test scores.

Until very recently, the literature on the effects of HIV testing on subsequent sexual behavior almost uniformly suggested that it caused HIV-positive individuals to become safer, not more risky. However, this study and Gong (2013), which share random assignment of HIV testing and the use of STI biomarkers as primary outcomes, come to the opposite conclusion. The sample in Gong (2013) consists of male and female adults seeking HIV-related services in Kenya and Tanzania in the mid-1990s, while our study examines a random sample of young females in Malawi in 2009. The common findings in these two studies, which utilize independent data sets from different countries, decades, and demographic groups, cast some doubt on the assumption that standard HIV testing interventions have direct prevention benefits through behavior change. We suggest that caution is warranted in interpreting findings from testing studies that do not simultaneously tackle both the endogeneity of testing and the risk of bias from self-reported sexual behavior data.

The study's main finding, i.e. that HIV-positive tests lead to increases in HSV-2 rates suggesting increases in unprotected sex, warrants some caution given that it is based on a small sample size (N=73). One concern is that a small sample size increases the chance of baseline imbalance for our primary outcome measures. Given the study design, whereby baseline STI status is unknown in the control group, it is impossible to demonstrate balance for HIV and HSV-2. However, we note that the main findings are robust to the inclusion of baseline covariates that are prognostic of HSV-2 infection at follow-up. Another concern is that our 20
13 where i is the index for the n individuals in the economy. As is well known, this measure is decomposable across mutually exclusive and exhaustive groups indexed by j with population weight xj: Pα = ∑ xj Pα, j Consider now dividing the economy up into three locations‐‐cities, towns and rural. Further, if we have a panel data set at time t and t+1, we can divide the population up into nine mutually exclusive and exhaustive groups of movers and stayers in each location, and these are the nine groups in the poverty decomposition. Change in national poverty is then simply given by: ∆Pα = ∑ xj ∆Pα, j Thus national change in poverty is a weighted sum of the change in poverty of those who were and stayed in rural areas, those who moved from rural areas to cites, those who moved from rural areas to towns, those who were and stayed in towns, those who moved from towns to rural areas, those who moved from towns to cities, and the corresponding three categories for cities, making nine groups in total.

The contribution of each category to national poverty reduction is: cj = [xj ∆Pα, j]/[ ∆Pα] Consider now a division of the population into stayers (3 groups) and movers (6 groups). The relative contribution of each group to poverty reduction depends both on xj and on ∆Pα, j. As is the case in the Kagera sample, even if ∆Pα, j is relatively small for a particular group of movers (rural to town compared to rural to city), xj can be relatively so large (number of rural to town moves compared to number of rural to city moves), that xj ∆Pα, j for this group can be larger than for another group of movers.

In a purely accounting sense, then, national poverty reduction is greatest when (i) poverty reduction is greatest in that group where the stayers are relatively large in number and (ii) moves are numerically greatest where the location‐destination combination gives the highest reduction in poverty.
WHEN IS THE GOVERNMENT _TRANSFER_ MULTIPLIER LARGE?

21 suggests, _α_ ( _φπ − ρ_ ) _/_ (1 _− ρ_ ) _>_ 0 whenever the Taylor principle holds (i.e. _φπ >_ 1), which ensures that an increase in inflation raises real interest rates and lowers the consumption of the Ricardian household. When the ZLB binds, the Taylor Principle Effect reverses its sign because a rise in inflation _lowers_ real interest rates, which increases consumption demand — from Ricardian HHs (Section 3.3 effectively _φπ_ = 0).

**Definition 4.** **_Disposable Income (DI) Effect._** The _boost_ to aggregate demand by the HtM household from an increase in inflation is given by (1 _− α_ )(1 _− βρ_ ) _/κ_ , other things (transfers and aggregate output) equal _._ Consumption of the HtM household is driven by its disposable income each period, and along the adjustment path higher inflation increases wages (by reducing markups), thereby boosting labour income of the HtM HH.<sup>26</sup>

**Proposition 2.** **_Sticky price multiplier._** _In the simple model when prices are sticky, monetary policy follows a Taylor rule and Assumption A1 holds, the targeted transfer and purchases multipliers are given by Equation 3.9._

> 26The disposable income effect is derived from the HtM HH’s budget constraint (Equation A3), substituting the firm’s FOC (Equation A9), and using the Phillips curve (Equation A6) plus Lemma 2 to substitute out variation in markups _X_<sup>ˆ</sup> _t_ to yield Equation 3.8.
3 We find that in 1981 high school drop-out rates of cohorts exempt before formal eligibility and while of high school age are significantly lower by 3.6 percentage points than those of similar cohorts from control towns, consistently with the predictions of a standard human capital model; a similar proportion of youths belonging to older exempt cohorts - released from military obligations after high school age - substituted instead army service with labor market participation. Ten years after the quake, in 1991, the first group displays a significantly larger share of high school graduates, by over 2 percentage points; all other observed circumstances of younger and older exempt cohorts five and ten years after the exemption are not statistically different from those of their non-exempt counterparts. In particular, we do not detect differences in their employment rates. The probability of dying over the next ten years, between 1991 and 2001, for the same younger exempt individuals is significantly lower, by 0.35 percentage points, about one fourth less than the figure recorded in comparable non-exempt cohorts. No difference can be detected between older exempt cohorts and comparable non-exempt ones.

The findings imply that the exemption led over 3,000 youths from the whole quake region to complete high school instead of dropping out; over the subsequent decade 1991-2001, about 600 additional youths in their mid-20s also survived into their mid-30s. The effect on life expectancy at age 25 is also substantial. Under the extreme assumption that the mortality rate reduction induced by the exemption is limited to the ten years between age 25 and 35, life expectancy at age 25 increased by about one quarter of a year. To put this change into context, between 1980 and 1990 life expectancy at age 25 increased by about one year.

The implied 2SLS estimate of the effect of a one percentage point increase in the proportion of high school graduates on mortality rates between the mid-20s and mid-30s suggests a statistically significant reduction of 0.1-0.2 percentage points, about one tenth of the baseline probability of death. The main assumption required to interpret this figure correctly as the causal effect of high school
government – for example, by enacting an electricity law giving various powers and duties to a Ministry regulator thereby requiring publicly owned electricity companies to operate in a more commercial way which would, among other things, allow state owned electricity companies to borrow from banks or debt markets on standard, commercial terms.

Investment is encouraged once effective regulation is available to support a workable financial framework. If the electricity industry is in private ownership the owners have the prospect of earning a reasonable return on their investment; if publicly owned, the industry can become independent of tax revenue or continually increasing loans. In addition, the existence of an effective regulatory framework can also encourage the growth of private investment and/or private finance within state systems, as has been happening in recent years in India and China.

These considerations suggest that the presence of an effective regulatory framework should, in general, lead to increased investment in the electricity sector, including the balanced development of generation, transmission, and distribution _ceteris paribus._ Unfortunately, comparable time-series capacity data across countries only exists for generation and it is on this aspect that the present study focuses.

In an unconstrained market economy, per capita generation capacity will adjust to the level of demand, which will depend upon the level of per capita income, the price of electricity, and environmental factors such as climate. The price of electricity will be determined in part by the efficiency of the sector. The latter may depend upon regulatory factors, but also availability of energy sources such as hydro, gas, oil, and coal. (This is most evident in cross-U.S. comparisons of prices.) However, many developing countries with a traditional, vertically integrated and state-owned electricity sector will be constrained not so much by market demand but by the availability of continuing subsidy.

The capacity constraints arise because of either inadequate government revenues for electricity investment or subsidy payments and/or insufficient revenue flow to support viable private investment. A simple diagrammatic version of such a model is shown in Figure 1 below 10