rafmacalaba/data-use-mentions-extended huggingface dataset:
1967 1971 1975 1979 1983 1987 1991 1995<br>Year<br><!-- End of picture text --> _Source:_ IMF's International Financial Statistics The collapse at Seattle of the negotiations to launch a new round provides time and perspective for India to reassess more carefully its attitude toward the multilateral trade system. The failed talks are both sobering and heartening in their lessons for India. Sobering because of what failure signals about the external political and negotiating environment. The hardening of partner country attitudes toward opening their own

**1** The other prong was to seek preferential access for one's own exports in industrial country markets.

3
A1). Moreover, vulnerability is mainly risk-induced and not poverty-induced, with a ratio of 1.1 (see Figure A2). Still, this ratio is considerably lower than in the case without measurement errors (a ratio of 1.7). Additionally, contrary to our previous results, poverty-induced vulnerability outweighs risk-induced vulnerability in rural areas, where this same ratio is 0.9 (compared to 1.4 in the case without measurement errors). Idiosyncratic vulnerability remains more persistent than covariate vulnerability, but the difference is smaller than in the case without measurement error (see Figure A3). In addition, the large variation with respect to vulnerability across departments persists (see Figure A4).

**Assuming that 50 percent of the variation stems from measurement errors is assuming a substantial measurement error.** Therefore, we alternatively assume that 25 percent of the observed variation is due to measurement errors in our income data. The vulnerability rate, in this case, is 0.21 and poverty-induced vulnerability plays a lower role in overall vulnerability than risk-induced vulnerability (see Annex 2 for the detailed results). This applies to rural as well as urban areas. The vulnerability rate is much larger in rural (0.38) than urban areas (0.12), similarly to the case without measurement error. Idiosyncratic vulnerability persists to be relatively more critical than covariate vulnerability. In the case of one-third of variation stemming from measurement errors, the vulnerability rate is 0.20. Risk-induced vulnerability is still the main driver, both for rural and urban areas. Idiosyncratic vulnerability plays a more significant role than covariate vulnerability.

**Given the above results, our main findings are robust to measurement errors.** While the vulnerability rate decreases when accounting for measurement errors, our main messages persist across the different specifications. Thus, risk-induced vulnerability remains the main driver behind vulnerability, and idiosyncratic vulnerability plays a more significant role than covariate vulnerability. Additionally, there is substantial variation in vulnerability across departments. While
# **Table 2. Summary Statistics: Capital Ratios**

Banks in our sample operate in 12 different OECD countries (see Table A1 for the list of countries and distribution of banks across countries). The sample period for the measures of capital (lagged one period in the regression) is 2005 to 2008. The yearly data are obtained from Bankscope. RWRt is the total capital adequacy ratio under the Basle rules. It measures regulatory capital divided by risk-weighted assets and off balance sheet risks. RWRt1 is the Tier 1 risk-weighted capital ratio, defined as shareholder funds plus perpetual, non-cumulative preference shares plus retained earnings, as a percentage of risk weighted assets and off balance sheet risks measured under Basel rules. RWRt2 is the Tier 2 capital ratio, defined as subordinated debt, hybrid capital, loan loss reserves, and valuation reserves divided by risk-weighted assets and off balance sheet risks measured under Basle rules. LRt is the leverage ratio defined as regulatory capital divided by total assets. LRt1 is the Tier 1 leverage ratio and LRt2 is the Tier 2 leverage ratio. Common equity is shareholder funds, and other capital is regulatory capital minus common equity scaled either by risk-weighted assets (RWA) or un-weighted total assets (TA). Summary statistics are reported for the whole sample and the sample of large banks. Large banks are defined as banks with total assets above 50 $ billion (the 20th percentile of assets).

||No. ofObserv.|Mean|Std.Dev.|25thpercentile|50thpercentile|75thpercentile|95thpercentile|
|---|---|---|---|---|---|---|---|
|**Whole sample**||||||||
|RWRt|4254|12.6|2.8|10.7|11.9|13.7|19.5|
|RWRt1|4073|10.2|2.8|8.1
B7, are used to impute a malaria risk for each patient. Appendix Figure B2 shows that the distribution of predicted malaria risk by treatment group is very similar, and Table 2 finds no significant difference in average predicted risk across treatment groups.

The probit is estimated off the selected sample of individuals who consented to a home-based test. If this selection is partly based on unobservables that are informative about malaria status, the malaria risk of tested patients may be different, conditional on recorded symptoms, from that of the average patient. However, our most important tests do not require estimated risk to be unbiased; rather we require a proxy that is strongly correlated with true risk. The regression assigns positive weight to symptoms known in the medical literature to indicate malaria, like fever, chills, nausea, and headache (CDC, 2015), which suggests that imputed malaria risk is correlated with true risk. Consistent with this, predicted risk is highly correlated with malaria treatment outcomes. Figure 3 uses data from the Control group to plot local linear regressions, where predicted malaria risk is the running variable. The share of individuals prescribed an antimalarial steadily increases with risk; lower risk patients are more likely to receive simple treatment, while higher risk patients are more likely to receive severe. As a proportion of prescriptions, the share of patients who decline the prescribed treatment is decreasing in predicted malaria risk for both simple and severe treatment. Appendix Figure B3 shows that these patterns hold for those with valid home-based RDT tests and those without.

# **4 Main Results**

## **4.1 Empirical Approach**

We use equation 1 as our core specification for estimating treatment effects. To improve precision and address any concerns of patient selection on PV and DV days, we augment equation 1 to control for additional clinic and patient-level characteristics. To avoid specification searching, we select these additional covariates using double lasso (Belloni, Chernozhukov and Hansen, 2014).<sup>27</sup> Appendix Tables B8
# **_Loan Recovery Rate_**

Another dimension of the performance of a Village Fund is the proportion of loans that are successfully repaid on time. The “loan recovery rate” as reported by VFs in the May 2010 survey was just 73%, but is only a weak approximation of the true rate, because it measures repayments as of December 2009 divided by the loans extended as of May 2010. Data collected from households in the socio-economic surveys of 2004, 2009, and 2010 show an on-time loan recovery rate of about 93% (see Table 2); when late payments are factored in, 97-98% of the principal lent appears to have been recovered.

|**Table 19. Estimation Results of Model of the Det**|**erminants of**<br>Robust OLS|**Loan Recover**<br>Endogen|**y Rate**<br>ous SwitchingReg|ression|
|---|---|---|---|---|
||||No|Probit,|
||Full sample|Onlending|onlending|switch|
|Supply: Fund Capital|||||
|Initial VF capital||-0.017|0.035|0.356***|
|||_(0.091)_|_(0.026)_|_(0.101)_|
|Supply: Characteristics of VF Committee|||||
|Accounts are computerized (yes=1)|0.070***|0.030|0.067**|0.116|
||_(0.022)_|_(0.067)_|_(0.027)_|_(0.121)_|
|Demand: Household Characteristics|||||
|Proportion of income generated by wages|-0.023<br>_
of sovereign risk|_k_1|-|Speed of scaling up plan|
|_αN_|0.45|Labor income share in nontraded sector|_k_2|-|Degree of frontloading|
|_αT_|0.60|Labor income share in traded sector|_ρδ_|0.80|Persistence of depreciation rate of public capital|
|_δN_|0.10|Depreciation rate of _kN,t_|_φ_|1|Severity of public capital depreciation|
|_δT_|0.10|Depreciation rate of _kT,t_|_ςε_|50|Severity of absorptive capacity constraints|
|_ρyT_|0.10|Learning by doing in traded sector| ̄_γ_<sup>_GI_</sup>|0.60|Thresholds of absorptive capacity constraints|
|_ρzT_|0.10|Persistence in TFP in traded sector|_ψH, ψE_|0.60|Concavity parameters in human capital investment|
|_κN_|25|Investment adjustment cost, nontraded sector|_γH, γE_|0.19|Efciency of human capital investment|
|_κT_|25|Investment adjustment cost, traded sector|_jE_|5|Time to build lag for education stock|
|_ψ_|10|Inverse of Frisch labor elasticity|_jH_|1|Time to build lag for health status|
|_σ_|2.94|Inverse of intertemporal elasticity of substitution|_εE_|0.1|Output elasticity to education investment|
|_ρ_|1|Intratemporal substitution elasticity of labor|_εH_|0.03|Output elasticity to health investment|

Table 1: Baseline calibration real return of the Norwegian Government Pension Fund from 1997 to 2011 (Gros and Mayer, 2012), the annual real return on international financial assets in the resource fund ( _R_<sup>_RF_</sup> _−_ 1) is set at 2 _._ 7 percent.

- _Private production_ . Consistent with the evidence on Sub-Saharan Africa (SSA) surveyed in Buffie _et al._ (2012)
)|
||Private formal|31<br>**48**<br>16<br>3<br>2|21% (22%)|
|Post-|Public formal|6<br>7<br>**84**<br>0<br>3|41% (44%)|
|secondary|Unemployed|40<br>31<br>14<br>**8**<br>7|7% (3%)|
||Out of labor force|36<br>22<br>22<br>10<br>**10**|4% (4%)|
||Private info|rmal<br>Private formal<br>Public formal<br>Un-employed|OLF|

_Notes_ . This figure relies on panel data from the Egypt Labor Market Panel Survey in 2012 and 2018. We restrict our analysis to men who were interviewed in both rounds. We focus on those aged at least 20 years old in 2012 and at most 59 years old in 2018. We use transition matrices to examine individuals’ transition between their work status/sector in 2012 and their work status/sector in 2018, by educational levels. We rely on the market definition of work status, search required (reference 1 week). The percentages reported at the end of each row show the shares of individuals in each employment status/sector in 2012, with the shares in 2018 reported in brackets. Panel weights are used.

12
same as that using real GDP growth. While the change in the domestic unemployment rate variable is the preferred measure of the domestic macroeconomic shock in the analysis of high-income economies of Bown and Crowley (2013a), the lack of good unemployment rate data for China and India in particular means that those countries are excluded from any analysis using the unemployment rate; furthermore, it shortens the available time series of data for other countries. For this reason, we generally emphasize the results which use real GDP growth as the measure of the domestic economy.

Columns (3) through (7) demonstrate the robustness of our baseline estimates for 1995-2010 to various other checks. Column (3) replaces the set of bilateral importer-exporter indicators with a set of importer indicators and a separate set of exporter indicators. The IRRs for the variables of interest exhibit little qualitative change from the baseline specification. Nevertheless, this approach also allows us to report the IRR on the indicator that the trading partner is China. The estimated IRR of 9.09 indicates that, controlling for other factors, China’s exporters are roughly nine times more likely than the omitted exporting country (defined as the median targeted exporter in the sample) to face import protection through TTBs.<sup>18</sup> In column (4), we omit bilateral import growth in order to examine the possibility that identifying foreign-induced shocks through inclusion of both foreign real GDP growth and import growth may be collinear. While omission of imports does increase the size of the effect for the estimated IRR on the foreign real GDP growth slightly, the estimated IRRs on the other variables of interest are virtually unchanged.

Column (5) presents an alternative characterization of the dependent variable by narrowing it to consider only the bilateral count of products subject to the antidumping policy alone. Specifically, we redefine the dependent variable to be the bilateral count of products subject to new antidumping investigations that result in imposed import restrictions, thereby leaving out the other TTB policies of safeguards and countervailing duties.
shocks, technology shocks, and cost changes—with workers’ living standards and need for stable jobs and incomes. New digital technologies behind the ”gig economy” may enhance firms’ demand for flexibility, but they also impose new challenges for employment protection legislation, including how to protect gig workers along with whose tasks may be replaced by digital technologies (Almeida et al., 2018).

The nature of these restrictions is quite similar in all countries, but the actual processes and the overall degree of stringency vary considerably.<sup>1718</sup> While the US, New Zealand, Canada, the UK, and Hungary have fairly unrestrictive individual dismissal regulations (the US being the least restrictive); regulations in the Czechia, Portugal, France, the Netherlands, and Germany are far

> 15The terms “employment protection rules” or “job protection legislation” are used synonymously.

> 16Legislation about fair labor conditions might also be considered to fall under the umbrella of employment protection legislation. For instance, Abman et al. (2023) and Lakdawala et al. (2023) study the effects of implementing and expanding workers’ rights to child workers. However, legislation around fair labor conditions, including child labor conditions, falls outside the scope of our review.

> 17See the OECD indexes for reference.

> 18While some aspects of employment protection rules, like the length of advance notices and the dimension of severance payments, can be measured with precision, other important features, such as the willingness of labor courts to entertain appeals by fired workers and how judges interpret what is a “just cause” for termination, are much more difficult to quantify.

50
paper by Ambler et al. (2017), using the 2011-12 Bangladesh Integrated Household Survey (BIHS), finds that measures of women’s well-being across health, employment, and participation in outside programs improve significantly when both spouses agree that decision-making is joint.

The patterns identified in the studies cited above have not been mapped back to a specific theory of how power comes about and influences outcomes. Within the intra-household bargaining power literature, the case of agreement corresponds most closely to the unitary model, which assumes either perfectly aligned preferences or a dictator with complete power and enforcement (Samuelson, 1956; Becker, 1973). The other predominant framework is that of bargaining models, where threat points determine outcomes (Browning & Chiappori, 1998; Manser & Brown, 1980; McElroy & Horney, 1981) and include both cooperative and non-cooperative models. One important distinction between the two is that the bargaining outcome in co-operative models is assumed to be stable in the absence of shifts in spouses’ relative bargaining power, while noncooperative equilibria may change based on new information about the other spouse’s power. However, none of these models allows for a state of continued disequilibrium, where partners do not recognize the power of the other in decision-making and have persistently different perceptions.

Yet empirically, a growing number of studies are finding substantial disagreement in reported decision-making roles across spouses, or other household members (Twyman et al., 2015; Seymour & Peterman, 2018). Anderson et al. (2017) conduct a study in rural Tanzania where husbands and wives are asked to depict their relative influence in different domains of decisionmaking using visual aids, to our knowledge the only study to examine this pattern in Sub-Saharan 7
coefficient. In (A106), the variables _CFtcoal_ , _CFtoil_ , _CFtgas_ denote the quantity of fuel consumed in million tons or million m<sup>3</sup> . Here, _CF_<sup>_f_</sup> is calculated as the sum of commodity output ( _QC_ ) and imports ( _M)_ less exports ( _X_ ), multiplied by the fuel conversion coefficient (  _meanf_ ) to convert the constant yuan of fuel consumed into the quantity of fuel consumed (in million tons or million m<sup>3</sup> ).

Second, in consumption-based accounting, we also calculate national energy consumption by adding over each industry, using industry specific information about the consumption of coal, coke, liquid _f_<sup></sup> _j_ fuels, etc. We first define consumption coefficients ( ) by taking the data on fuel actually used (in million tons, million m<sup>3</sup> , or billion kWh) from the China Statistical Year book (CSY 2012, Table 7-9 “Consumption of Energy by Sector”) and dividing by the value of energy purchases given in the Input-Output table.

To disambiguate, the fuel conversion coefficient (  _meanf_ ), presented in equation (A101), is computed using the production data at the aggregate level: the _total_ quantity of the commodity output divided by the _f total_ value of the commodity output. In contrast, the consumption coefficient (<sup></sup> _j_<sup>), presented here, is</sup> computed using the consumption data at the industry level: industry _j_ ’s consumption of fuel _f_ (in million tons, million m<sup>3</sup> , or billion kWh) divided by the value of industry _j_ ’s purchases of fuel _f_ (in billion yuan).

Secondary fuels are produced by the Petroleum Refining & Coal Products sector which we group as coke, refined liquids, and other petroleum products.
, a 4.4 percentage point difference. The official national poverty rate in 2015 was 27.8 percent, an intermediate value between these two estimates.

_Table A.0: Contrasting 2015 poverty estimates based on welfare aggregates with those based on total expenditures_

|Statistic|National|Metropol.|Lower<br>Urban|Lower<br>Rural|Upper<br>Urban|Upper<br>Rural|Borders|
|---|---|---|---|---|---|---|---|
|% ofpopulation*|100.0%|6.4%|7.7%|29.8%|14.3%|38.6%|3.2%|
|Per capita welfare*|8614|12371|10069|7952|8598|5841|9015|
|Per capita total<br>expenditures*|8599|12088|9884|8075|8479|5745|9177|
|Poverty rate<br>(welfare)*|29.2%|14.1%|11.6%|23.2%|26.9%|58.1%|24.1%|
|Poverty rate (total<br>exp.)*|24.8%|11.0%|9.6%|17.1%|23.9%|54.8%|15.5%|
|Poverty rate<br>(official)**|27.8%|15.1%|9.7%|19.7%|27.4%|56.7%|-|

**Source** : *Authors’ calculations based on HIECS 2015. **CAPMAS (2019).

> 10 Given the fact that homeowners tend to misreport the value of the rent (Ceriani et al. 2019), we
for the three variables used. The question refers to the past two complete weeks of school and we can see that Vietnamese students are less likely to have been late for school, have fewer days of unexcused absence and skip fewer classes.<sup>3</sup> The final set of variables in Table 1 concerns parental background and wealth at the students’ home, including cultural resources and books at home which may work to stimulate cognitive development. The PISA database includes a number of indices to measure aspects such as wealth. These indices are based on underlying data regarding occupations and possessions. The scaling of raw data to indices is described in detail in the PISA technical report [OECD, 2014a]. For HISEI, which describes parental occupation status, the OECD mean is 50 and the OECD standard deviation is 15. Table 1 shows that HISEI for Dev7 parents stands at 40.42 and is thus much higher than 26.60 for Vietnamese parents. MISCED refers to the International Standard Classification of Education (ISCED) developed by UNESCO. Table 1 shows that the average level of mother’s education (MISCED) for Dev7 was just over 3, meaning Upper Secondary education, while for Vietnam the mean was just over 2, meaning Lower Secondary education. The WEALTH index is set for an OECD mean of zero and standard deviation of 1. Dev 7 countries wealth level was -1.5 and Vietnam’s was -2.1, which is consistent with the data regarding occupational classification and mother’s education. These findings indicate the close correlation of these variables with GDP per capita. Another interesting finding concerns the indices CULTPOS, cultural possessions and HEDRES, educational resources at home which have an OECD mean 0 and a standard deviation 1, as well as BOOK ~~N~~ , the number of books in family home. CULTPOS includes classical literature, books of poetry and works of art. HEDRES includes reference books and books to help with school work as well as a study
(1.00)|1,321|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022-2023 phone survey data. Each row is its own dependent variable.

- N. of close Jordanian friends reports the treatment effect on the number of close Jordanian friends the respondent reports. Met close Jordanian friend at ... reports an indicator for where the respondent met the most recent close Jordanian friend they made. Overwhelmed by financial needs of others is a standardized likert response to “Over the past week, I have felt overwhelmed or burdened by the financial needs of people outside my household”. Current Macarthur rung is a standardized version of the following question: “Imagine a ladder with 10 rungs representing society. At the top of the ladder are the people who are the best off, those who have the most money, most education, and best jobs. At the bottom are the people who are the worst off, those who have the least money, least education, worst jobs, or no job. If the top of the ladder is the 10th rung and the bottom is the 1st. Which rung best represents where you think you stand?” Aspired macarthur rung is a standardized version of the 1-10 response on which rung they would like to acheive in their life.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age). In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family xxx in the preanalysis plan.

Table (F11