rafmacalaba/data-use-mentions-extended huggingface dataset:
# 4.1 The Cox Proportional Hazard Model

We estimate the hazard rate for our trade relationships data using a Cox Proportional Hazard (PH) model (Cox 1972). The Cox PH model is broadly applicable and represents the most widely used method for survival analysis. The hazard function for a given product k exported from country c with covariates X = fx1; x2; :::xj; ::xng, is de...ned as the product of a baseline hazard function h0(t), common to all observations, and a parametrized function exp (X:�) with a vector of parameters �: The form of the baseline hazard function characterizes how the hazard changes as a function of time. The covariates X a¤ect the hazard rate independently of time. The model o¤ers some convenient features. It makes no assumptions about the form of the underlying baseline function. Additionally, the relationship between the covariates and the hazard rate is log-linear, allowing for a straightforward interpretation of the parameters. Increasing xj by 1, all other covariates held constant, a¤ects the hazard function by a factor of exp (�j) at all points in time. Thus, it shifts all points of the baseline hazard function by the same factor. Parameter estimates in the Cox PH model are obtained by maximizing the partial likelihood as opposed to the likelihood for an entirely speci...ed parametric hazard model (Cox 1972). The resulting estimates are less e¢ cient than maximum-likelihood estimates. However, the model makes no arbitrary, and possibly incorrect, assumptions about the form of the baseline hazard function.

# 4.2 Empirical Speci...cations

We use the Cox Proportional Hazard model to analyze the export duration of product k from country c to the USA. This enables us to investigate whether the competitive US market and the banking sector in exporting countries shape export survival according to the idea of comparative advantage. The empirical 15
13.5 cm long (Figure C1b).

# **III Empirical strategy**

In this section, I describe how I estimate the total causal effect of the temporary spatial closures policy on juvenile catch. Potential closures are the unit of observation. I estimate the effect of the policy across potential closures, comparing juvenile catch inside and near treated potential closures—those that the regulator declare as actual closures—to juvenile catch inside and near control potential closures—those that the regulator does not close.

I detail the construction of potential closures in Section III.A. In Section III.B I discuss identifying variation and define the variables in my estimating equation. I describe how I estimate spillover effects in Section III.C. I present my estimating equation in Section III.D. In Section III.E I demonstrate balance of treated and control potential closures on pre-period juvenile catch and observable measures of fishing productivity.

## **A Potential closures**

As detailed in Section I, the regulator uses real-time electronic logbook data to determine closures. When a government official wants to create a closure, they draw a rectangle around a group of sets that occurred near each other during the same time period. A computer algorithm then calculates the exact boundaries and number of days the resulting closure will last. I develop an algorithm that mimics the first stage of the closure rule, when the official selects a group of sets on their computer. I cluster sets in the electronic logbook data that occur near each other and record the bounding box around each cluster. The resulting rectangles are “potential closures”. Unlike the regulator, I create potential closures from _every_ cluster of sets. I do not attempt to reproduce the second stage of the closure rule, when the computer algorithm determines the exact boundaries and time length of a closure,

> when a vessel has reached its quota for the season.

17
household head is either Hindu or Muslim, which covers between 86% and 92% of surveyed households in each data set.

The Indian Human Development Survey (IHDS) collected data from 41,500 households across 384 districts in 33 states and union territories from 2004 to 2005 (Desai, Vanneman and NCAER, 2005). These data cover three sanitation practices: latrine ownership, handwashing, and observed fecal material.

The third wave of the District Level Household and Facility Survey (DLHS-3) collected data from 720,000 households across 601 districts in 34 states and union territories from 2007 to 2008 (IIPS, 2010). DLHS-3 includes data on whether the household reports owning a latrine but does not report data on other sanitation practices.

The third wave of the National Family Health Survey (NFHS-3) collected data from 109,000 households in 29 states and union territories from 2005 to 2006 (IIPS, 2007). NFHS-3 includes data on whether the household reports typically using a latrine but does not report data on other sanitation practices.

The analysis explores the cross-sectional relationship between religion and sanitation practices, and so it uses three data sets from a similar time period that each provide similar but complementary data on sanitation practices (latrine ownership, latrine use, observed fecal material, and handwashing).<sup>7</sup> The data sets each include information about households, which are used to define a set of household characteristics: caste of household head, occupation of household head, education of household head, household size, age of survey respondent, number of female members of the household, whether the household has piped water, and proxies for household wealth (an indicator variable for whether the household owns their house; indicator variables for the presence of electricity, a cell phone, television, bicycle, car, and motorcycle; and distance to

> respondent about the type of latrine that her family “has,” while the NFHS-3 asks the respondent about the type of latrine that her household “uses.
# **6. Other transactional insurance motives**

An alternative explanation to the observed lack of reciprocity could be that migrants insure non-migrants in exchange for other benefits. By concentrating on consumption differences we have considered only current pay-offs from any risk-sharing arrangement. It is quite possible that the benefits are still to accrue to the migrant in the more distant future. Lucas and Stark (1985) mention that there could be exchange motives for insurance provision relating to the desire for non-migrants to look after local assets, the intention to return home and the aspiration to inherit. In a context that lacks technology to allow future income to be consumed now, we could confuse unilateral insurance with postponed reciprocity. Fortunately, the KHDS questionnaire is particularly rich and we are thus able to explore some of these issues.

The questionnaire asks each migrant about asset holdings in the baseline village. As our outcome variable is consumption growth we cannot use these asset holdings as explanatory variables: current wealth is surely endogenous to growth in wealth. We attempt to circumvent this problem by looking at the share of assets in the current portfolio that are located in the village. While it remains possible that portfolio composition is endogenous to consumption growth, we believe the results are informative enough to report.

About 28 percent of migrants have assets in the baseline village and 25 percent of migrants own land in the baseline village. For land we have exact area measurements, but not monetary values. If migrants engage in risk sharing with those who remain at home for the purpose of maintaining land and ensuring their continued entitlement to the land (which is important in a country with few formal land deeds), then we would expect more responsiveness to network shocks from people with a larger share of their land holdings in the baseline village. The first column of Table 9 explores this. As before, the dependent variable is logged per capita consumption growth. We interact the non-migrant network shocks with a variable measuring the share of the land in the baseline village. The coefficient on this
finally broke the $350/ton barrier.

Limiting losses is reflected in U.S. Trade Adjustment Assistance program as well. For example, the extent of assistance provided to farmers is based on the difference between the current price and the average price of a given product over the last five years, i.e. the industry losses. This is rather different from the estimated cost of adjusting to a more competitive environment, which is the original motivation behind the program.

In sum, the main forms of protection in use today are initiated by price declines and appear to be guided by a desire to maintain domestic prices above some reference

> 19 See Tharakan (1996) for a survey of the evidence. 20

> About half of the more than 200 antidumping orders currently in force are on imported steel.

26
|CONTEN|TS|PAGE|
|---|---|---|
|SUM|MARY|3|
|**1.**<br>INT|RODUCTION|5|
|2.<br>INV|ESTIGATION OF PATTERNS AND DETERMINANTS<br>OF HEALTH|E|
|2.1|Existing health information<br>systems in developing<br>|7|
||countries||
|2.2<br>|TheUseof surveys to investigate<br>patterns of health<br> <br> <br>|8<br>|
|2.3|Integration<br>of survey investigations<br>into existing<br>ttitil<br>t|10|
||sasca<br>sysems||
|2.4|The design of country health studies|11|
|2.5|Investigations<br>of the determinants<br>of health|13|
|3. MEA|SURING THE IMPACT OF INVESTMENTS<br>IN HEALTH|14|
|3.1|Economic evaluation|14|
|3.2|Costs|15|
|3.3<br>|Consequences<br>|16<br>|
|3.4|General problems of economic evaluation|20|
|3.5|Economic evaluation and the World Health Survey|21|
|4. SC|OPE AND LIMITATIONS<br>OF INTERVIEW-BASED<br>SURVEYS|23|
|4.1|Health interview<br>surveys- an introduction|23|
|4.2|Mortality- well established<br>methods|24|
|4.3|General morbidity, cause-specific<br>morbidity and<br>|26|
||use of services||
|4.4|Nutritional status indicators|31|
|4.5|Environmental<br>contamination|33|
|4.6
mated caloric intake using the 7-day recall period. The alternative estimates of caloric intake in column 3 (using the 30-day recall) are also lower than TEE in the later period, but display a similar decline between 2005-2012, such that TEE could “explain” about half of the decline in caloric intake estimates during this period. In column 7, average TEE for adults is reported to once again underscore that, were it not for demographic change, changes in estimated TEE for adults would be enough to “explain” a decrease in caloric intake of 34 calories per day between 1983-2005 (about 20% of the 150 calorie decline in the official estimates) or 111 calorie per day between 1983-2012 (over 100% of the 102 calorie decline in the official estimates).

Further evidence that changes in TEE may have contributed to lower caloric intake between 2005-2012 but are unlikely to have done so for the 1983-2005 period comes from an analysis of state-level changes. Figure 2 plots the change in state caloric intake against the change in state TEE for the 1983-2005 period and the 2005-2012 period. In the earlier period, although there are some states that appear to line up along the 45 degree line, most lie well below, indicating that measured caloric intake relative to TEE declined. The association is also very weak and not statistically significant. In the later period, the relationship is considerably stronger, with most states lining up along the 45 degree line and a statistically significant slope of 0.666 (with associated _R_<sup>2</sup> of 0.08). Using the full state panel, TEE estimates are significantly correlated with changes in caloric intake within states over time.<sup>8</sup> Given that the finding that population TEE rose during the 1983-2005 period of falling caloric intake considered by (Deaton and Dr`eze 2009), it is worth considering the best estimates of the potential effect of the disease environment on caloric intake from (Duh and Spears 2017). They use a
as foreign direct investment and bilateral financial interconnection indexes. In all cases, the main message emerging from the analysis does not change and cross-country production linkages, as proxied by trade in intermediate input, is associated with higher GDP co-movement, especially for all country-pairs that include a high-income country.

- Finally, based on a product characterization from Martin et al. (2018) that separates “sticky” from “non-sticky” products based on the duration of buyer-seller relationship,

> 4This finding shows that when considering cross-country production linkages, it could be misleading to only focus on the imported exports _that are re-exported further_ . Indeed, since exports typically are a small part of total GDP, the impact of imported input on domestic production goes far beyond the impact on exports.

3
, by using the relative literacy rate of adult females (ages 15 and above), divided by the sum of literacy rates of adult males and females–which can be viewed as a measure of the relative human capital of women. As noted earlier, this corresponds to the main determinant of bargaining power.<sup>22</sup> Using WDI data for 2009, this gives κ = 902(902 + 898) = 0501. By this measure, therefore, there is not much evidence of gender bias in the family in Brazil, although some other measures (based, in particular, on control of household assets) would suggest otherwise. Expression (38) can therefore be solved ̄ ̄ backward for the parameter κ, thereby giving κ = 0395.

Survival probabilities <sup></sup> and <sup></sup> are calibrated as follows. In 2010 life expectancy at birth in Brazil was 697 for men and 767 years for women, with an average of 731 years.<sup>23</sup> This gives an annual death rate of 1697 = 0014 for men and 1767 = 0013

> 22See the references provided earlier.

> 23In principle, the adult survival probability in the model should be calculated on the basis of life expectancy at middle age. However, no such data are available.

20
paper is structured as follows. Section II provides details on the country context, discussion of the messaging campaign, and a description of the data used in the study. Section III briefly describes the pooled ordinary least squares (OLS), and fixed effects estimators used to measure differences in reporting behaviors between message-receiving and non-receiving respondents. Section IV includes of the results, and section V a discussion of the study’s implications.

# II – Context, Messaging, and Data

About 93 percent of Tajikistan’s territory is occupied by mountains and only about 6 percent of land is arable. About 70 percent of the population lives in rural areas and most reside in the valleys in the western half of the country. There are numerous small communities scattered in difficult-to-reach mountainous regions. Due in part to this challenging terrain, authorities often struggle to communicate with and provide public services to people living in remote areas where long travel times are common. National surveys, including the main data source for this study, find that large majorities of respondents see improving basic public services (such as health and education) as high priority issues, 3
**Appendix 1: 15 Indicators of the Survey on Monitoring the Paris Declaration**

||**_Indicators_**|**_Partner_**<br>**_country_**<br>**_performanc_**<br>**_e_**|**_Developme_**<br>**_nt partner_**<br>**_performanc_**<br>**_e_**|**_How measured (including_**<br>**_source of information)_**|**_2010 target_**|
|---|---|---|---|---|---|
|1|Operational national<br>development<br>strategies<br>|<br>||Bank‘s assessment based on<br>information from partner<br>countries|**75%**of partner countries<br>have operational national<br>development strategies|
|2a|Reliable public<br>financial<br>management (PFM)<br>systems|||Country Policy and<br>Institutional Assessment<br>(CPIA) Indicator 13|Half of partner countries<br>move up at least one<br>measure|
|2b|Reliable<br>procurement<br>systems|||Self-assessment using<br>Methodology for Assessing<br>Procurement Systems (MAPS)<br>|No target (due to the small<br>number of countries<br>conductingMAPSin 2010)|
|3|Aid flows are aligned<br>with national<br>priorities (aid on<br>budget)|||Aid flows reported in the<br>annual budget by<br>government/aid flows actually<br>disbursed by development<br>partners(%)|Halve the gap (at least<br>
,t + β 18Federal Banki,t + β 19Foreign Banki,t + ε i,t

> 36 The high negative outliers in the case of ROE are also explained by the following adjustment we make in the case of negative equity: we set equity at 1% of assets to avoid that a bank with negative equity and losses shows a positive ROE.

> 37 Note that these are quarterly data.  For annualized data, these would have to be multiplied by four.  Since we use a linear estimation model, there is only a scaling effect.

> 38 Similar models are employed by Berger et al. (2005) and Beck et al. (2005).

17
representative at the national level and residence areas: Ndjamena (capital city), rural areas, and other urban areas. So far, one round of the survey has been published.

We first present a few employment and income-related characteristics of female-headed households in Chad, making these households uniquely vulnerable to shocks. For this purpose, we use data from the full sample of ECOSIT 4. Next, to make inferences about COVID-19’s economic impacts, we concentrate on the four modules in the HFPS related to employment and revenue, loss of income, shocks and coping strategy, and COVID-19 impact. We use simple descriptive statistics and test of proportions (prtest) to identify the most affected income channels and coping strategies adopted by female-headed households and use probit model regressions to identify outcomes significantly different from male-headed households. We use weights associated with households in the survey so that the reported results are representative at the national level. The weights also correct for any selection bias associated with not interviewing households that do not own mobile phones or that cannot be reached despite repeated call attempts.

The probit model, which is well-suited to model dichotomous or binary outcome variables, allows us to determine if differences in binary outcomes between female and male-headed households observed through descriptive statistics and proportion tests are significant. It allows us to methodically document trends and impact instead of relying on anecdotes and assumptions. However, our method and survey data do not tell us about the underlying mechanisms, i.e., why there are differences. We draw inferences from the existing literature to explain possible reasons for these differences.

The main binary outcome that we examine through the probit regression is the reduction in household income from various sources in the past 12 months and the adoption of different coping strategies. For household income reduction, we constructed binary variables for the decline in household income from any source (value one if “yes” and zero if unchanged or increased) and for each income source: wage employment, non-farm enterprise income, farm income, remittance. The
2). The GMM systems estimator, developed by Arellano and

> 19 The Dollar’s index of overvaluation gives an idea to what extent the real exchange rate is distorted away from its free-trade level by the trade regime.

> 20 As a result of the differencing the country specific term is eliminated from forecasting model (5). 21 The 5-year panel dataset contains 78 countries – fifty five of them developing – and for each of them, a minimum of 3 and a maximum of 8 non-overlapping 5-year observations (the sample is unbalanced). A minimum of 3 observations per country is required to implement the instrumental –variable methodology for the GMM estimators. The total number of observations equals 350.

17
# **8. References**

- <mark>Ahmad, Junaid K., Shantayanan Devarajan, Stuti Khemani, and Shekhar Shah. 2005. “Decentralization and Service Delivery.” Policy Research Working Paper 3603, World Bank, Washington, DC.</mark>

- <mark>Akai, Nobuo, and Masayo Sakata. 2002. “Fiscal Decentralization Contributes to Economic Growth: Evidence from State-Level Cross-Section Data for the United States.”</mark> _<mark>Journal of Urban Economics</mark>_ <mark>52 (1): 93–108.</mark>

- <mark>Alatas, Vivi, Abhijit Banerjee, Rema Hanna, Benjamin A. Olken, Ririn Purnamasari, and Matthew Wai-Poi. 2013. “Does Elite Capture Matter? Local Elites and Targeted Welfare Programs in Indonesia.” NBER Working Paper 18798 (February), National Bureau of Economic Research, Cambridge, MA.</mark>

- <mark>Alderman, Harold. 2002. “Do Local Ofcials Know Something We Don’t? Decentralization of Targeted Transfers in Albania.”</mark> _<mark>Journal of Public Economics</mark>_ <mark>83 (3): 375–404.</mark>

- <mark>Altunbaş, Yener, and John Thornton. 2012. “Fiscal Decentralization and Governance.”</mark> _<mark>Public Finance Review</mark>_ <mark>40 (1): 66–85.</mark>

- <mark>Avendaño Ramos, Eréndira. 2012. “Evaluación del fondo de aportaciones para la educación básica (FAEB).”</mark> _<mark>Economía UNAM</mark>_ <mark>9 (26): 82–95.</mark>

- <mark>Araujo, María Caridad, Francisco H. G. Ferreira, Peter Lanjouw, and Berk Özler. 2008. “
<!-- Start of picture text --> Food Deprivation - by State in Food Deprivation - by State in Food Deprivation - by State in<br>North Central - MICS North East - MICS North West- MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>child 0-4 child 0-4 child 0-4<br>Benue FCT Abuja Kogi Kwara Adamawa Bauchi Borno Jigawa Kaduna Kano Katsina<br>Nasarawa Niger Plateau Gombe Taraba Yobe Kebbi Sokoto Zamfara<br>d) e) f)<br>Food Deprivation - by State in Food Deprivation - by State in Food Deprivation - by State in<br>South East - MICS South South MICS South West- MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>child 0-4 child 0-4 child 0-4<br>Abia Anambra Ebonyi Enugu Imo Akwa IbomDelta BayelsaEdo Cross RiverRivers Ekiti Lagos Ogun Ondo Osun Oyo<br><!-- End of picture text --> Source: Author’s estimates based on MICS 2016/17 Note: Individuals with missing age were dropped from the analysis. Total refers to all individuals with known age.

Figure 20 displays a further breakdown of the state level estimates by gender and sector. While evidence from Bangladesh and India (
mix on installed capacity, 2014, percent of total**

<!-- Start of picture text --> 1% 0% 4% 4% 0%<br>6%<br>4% 0%<br>Hydropower<br>Gas<br>21%<br>47GW 47% Diesel and HFO 47GW<br>(excluding South (South Africa<br>Africa) Coal only)<br>Other renewables<br>26% 82%<br>Nuclear<br><!-- End of picture text --> _Source_ : World Bank staff calculations taking data from utility annual reports and other documents.

With a population of almost one billion people, installed capacity in all 48 countries in SSA in 2014 was 94 gigawatts (GW), up from 68 GW in 2005 (Eberhard et al 2011). The total regional capacity is less than that in Spain, whereas Spain has a total population of 50 million, against 962 million in SSA. South Africa with 47 GW accounted for half the capacity in SSA. Fourteen countries had power systems larger than 1 23