rafmacalaba/datause-encoder-data huggingface dataset:
# 4 RESEARCH DESIGN

## _4.1 Data_

DatafortheprojectcomesfromasurveyofruralhouseholdsinColombiafieldedbetweenMarch and June of 2017. The survey was commissioned as part of the performance evaluation<sup>14</sup> of USAID’s Land and Rural Development Program (LRDP), a program designed to improve institutional capacity in relation to land formalization, land restitution, and broader rural development outcomes. The survey was fielded at the halfway point of the LRDP’s lifespan, part of a broader effort to measure the program’s strengths and weaknesses up to that point in time.

The survey captured almost 1,500 responses across fifty rural and conflict-affected municipalities,25ofwhichwereselectedbecauseoftheirinvolvementinorrelevancetotheLRDPand an additional 25 which were selected through matching on observables to serve as comparable municipalities that did not experience any LRDP programming (Figure A.1). Survey responses were collected on tablets and in-person, in or near the respondent’s home. More detailed information on data collection, sampling, implementation, and ethical considerations of sampling in violent contexts are available in the Appendix.

This survey data has a number of strengths for the study of displacement. First, the sampling frame explicitly over-sampled areas and communities that have experienced a lot of displacement and as a result, produced a sample where approximately half of respondents have been displaced (sampling characteristics can be seen in Figure A.2). Second, and relatedly, the survey contains long modules both on displacement experiences as well as on a variety of postdisplacement outcomes that can be used to explore the effects of displacement. Finally, the sample is otherwise relatively homogeneous, capturing rural areas with a relatively high degree of exposure to conflict. This means that – following statistical adjustment to control for potential confounds in who is displaced – there is good coverage for comparing respondents whohaveexperienceddisplacementagainstthosewhohavenotexperienceddisplacement. An important limitation, however, is that the sample is not representative of the universe of IDPs in Colombia.<sup>15</sup> In terms of which outcomes to focus on with respect to displacement effects, we consider a broad range, which we group into six categories. Given prior work on the welfare-diminishing

> 14Generally, _performance_ evaluations differ from _impact_ evaluations in that the latter tend to have built-in randomized treatment and control groups for subsequent estimation of effects, while the former tend not to or are often more descriptive in their evaluation goals.

> 15While the distribution of respondent characteristics in the household survey largely resemble comparable respondent characteristics from the recent (nationally representative) rural waves of LAPOP in Colombia (see Figure A.3), we cannot strictly generalize our findings outside of these areas.

10
23 Information on the countries of origin of immigrants comes from two principal sources: (1) data on country of last residence of permanent settlers (Table 9), and (2) data on country of birth of foreign-born persons from the 1986 census (Table 10). The latter tab,le gives information which is more relevant to net migration, since immigrants who emigrated before the time of the 1986 census were not enumerated in the census. The major sources of settler arrivals in 1986-1987 were the United Kingdom (17.6 percent), New Zealand (14.1 percent), the Philippines (5.7 percent), South Africa (4.6 percent), Hong Kong (4.5 percent), and Malaysia (4.4 percent). The other major sending countries were widely spread throughout parts of Europe, Asia, North and South America, the Middle East, and the Pacific. Information from the 1986 census on the foreign-born population of Australia shows that the largest numbers of such persons who have been in Australia for less thar five years are from England (68,877) and New Zealand (66,268). Other important source countries are Vietnam, Malaysia, the Philippines, the United States, Poland, Hong Kong, China, and South Africa.

In addition to permanent settlers, Australia also grants resident status to persons who are already in the country but who do not have resident status. There were 10,107 such immigrants in the year ending June 30, 1987 and 13,494 such immigrants in the year ending June 30, 1988 (see Table 11). Overall, the migrant intake was 123,416 in 1986-1987 and 156,984 in 1987-1988. Therefore, immigration has continued strong in the last two years and there are no indications that it will drop precipitously in the next few years.

In addition to the migrant intake, another component of gross immigration is the return to Australia of persons who previously emigrated, but I do not have separate statistics on this group. Gross emigration, on the other hand, consists of foreign-born and Australia-born emigrants. Statistics on permanent departures of foreign-born persons and all persons are shown in Table 8. Since 1980/1981, permanent departures have been fairly stable, ranging between 18,100 and 24,830 and averaging 21,133 emigrants per year. The departure rate of settlers is relatively high for the U.S., Canada and Greece, and relatively low for Poland and most countries in Africa and Asia (see Table 12). There is only a small difference in the depart"re rates for males (12.2 percent) and females (12.6 percent).

Net Migration. The level of net overseas migration that the Australian Bureau of Statistics uses in its estimates of the resident population is shown in Table 13. The level of net migration has fluctuated considerably, largely in response to annual changes in the level of permanent settlement. Net migration varied between 49,100 in 1983/1984 and 128,100 in 1981/1982. For the entire six-year period covered by the table, net immigration averaged 88,050 annually.
# **Forecasting Local Climate for Policy Analysis: A Pilot Application for Ethiopia**

**Brian Blankespoor Kiran Dev Pandey David Wheeler**

This paper has been fully or partially funded by the Economics of Adaptation to Climate Change study. The study is a partnership of the World Bank which is leading the technical aspects of the study, and the governments of the United Kingdom, the Netherlands and Switzerland who are funding the study. The Economics of Adaptation is a larger effort in the department to make aid more effective in promoting economic development. Papers in this series are also posted on the Web at http://WorldBank.org/Eenvironment/EACC. Questions about the paper can be addressed to Kiran Dev Pandey at kpandey@worldbank.org.
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# **Swaziland**

||**Prevalence amon**<br>|**g females 15-24(%)**|**Spectrum**|**estimates**|
|---|---|---|---|---|
||**National Antenatal Sentinel**<br>**Surveillance Surveys **|**National Demographic and**<br>**Health or AIDS Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|
|**1990**|-|-|0.54|-|
|**1991**|-|-|1.16|-|
|**1992**|-|-|2.12|-|
|**1993**|-|-|3.27|-|
|**1994**|-|-|4.30|-|
|**1995**|-|-|4.92|-|
|**1996**|-|-|5.07|-|
|**1997**|-|-|4.84|-|
|**1998**|-|-|4.45|-|
|**1999**|-|-|4.07|-|
|**2000**|35.4|-|3.75|-|
|**2001**|-|-|3.48|-|
|**2002**|39.4|-|3.36|-|
|**2003**|-|-|3.27|-|
|**2004**|39.4|-|3.29|14|
|**2005**|-|-|3.21|27|
|**2006**|34.6|-|3.03|35|
|**2007**|-|22.7|2.85|42|
|**2008**|38.1|-|2.74|-|
|**2009**|-|-|2.57|64|
|**2010**|34.7|-|2.50|73|
|**2011**|34.0|-|2.47|83|
|**2012**|-|-|2.04|82|

<u>ANC data source: Swaziland Country Report on Monitoring the Political Declaration on HIV/AIDS 2012 National DHS and AIS data source: Swaziland DHS 2006-2007</u>

# **Tanzania**

||**Prevalence amo**<br>|**ng females 15-24(%)**<br>|**Spectrum**<br>|**estimates**<br>|
|---|---|---|---|---|
||**National Antenatal Sentinel**<br>**Surveillance Surveys **|**National Demographic and**<br>**Health or AIDS Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|
|**1990**|-|-|1.25|-|
|**1991**|-|-|1.33|-|
|**1992**|-|-|1.34|-|
|**1993**|-|-|1.29|-|
|**1994**|-|-|1.21|-|
|**1995**|-|-|1.08|-|
|**1996**|-|-|0.97|-|
|**1997**|-|-|0.87|-|
|**1998**|-|-|0.76|-|
|**1999**|-|-|0.71|-|
|**2000**|-|-|0.64|-|
|**2001**|-|-|0.59|-|
|**2002**|-|-|0.55|-|
|**2003**|-|-|0.53|-|
|**2004**|7.4|4.0|0.50|<1|
|**2005**|-|-|0.48|5|
|**2006**|6.8|-|0.46|14|
|**2007**|-|-|0.42|31|
|**2008**|5.3|3.6|0.40|-|
|**2009**|-|-|0.38|32|
|**2010**|-|-|0.34|38|
|**2011**|-|-|0.34|40|
|**2012**|-|-|0.32|61|

<u>ANC data sources: Tanzania Surveillance of HIV and Syphilis Infections Among Antenatal Clinic Attendees 2003/4,</u> 2005/06, 2008 <u>National DHS and AIS data sources: Tanzania AIS 2003-2004, Tanzania AIS/Malaria Indicator Survey 2007-2008 and</u> 2011-2012 63
The first dependent variable proxies the severity of censorship by country-year. In terms of our theoretical hypotheses, this event-specific operationalization allows us to interpret the selective choice of censorship targets, if statistically significant, as signals that the censor may want to send to all similar outlets in terms of lines not to be crossed. We constructed an ordinal variable capturing thirteen categories of censorship actions, grouped by the consequences for targets, and ranging from lawsuits and fines all the way to jailing, death and disappearance (Figure 4). In our data set, intimidation, assault and jail are the most numerous censorship actions. The second dependent variable aggregates all punitive censorship events, in total and by types, by country-year into a panel format. Unlike the number of total censorship events that displays a clear and steep upward trend between 2001 and 2015 (Figure 1), the mean severity of censorship oscillated more sharply, exhibiting a recent increase (Figure 5).

_Independent variables_ To test the hypotheses of censors’ selective control of different media market segments based on the audience size and transaction costs, the independent variables of interest aim at operationalizing several dimensions: (1) whether or not the targeted media outlet is foreign or domestic, stemming from the assumption that domestic constituencies are more important for the survival of the leader, and that there are higher transaction and reputation costs involved in repressing foreign media; (2) whether the media outlet is located in the capital city, in the regions or abroad; (3) whether the media outlet distributes or broadcasts information to a large or narrow audience depending on its type. Empirically, to assign meaningful values to an ordinal scale that accurately capture the size of the audience, we use the most recent Pew Research Center’s _Global Attitudes and Trends_ surveys asking respondents where they get their national and international news primarily from. While there is variation in terms of the audience size reached by each type 29
structures are simulated to estimate effects on GDP. An externality is introduced into the basic Heckscher-Olin framework to represent extraction effects of one country on another country's water endowment. Results imply each country has an incentive to subsidize water use and, thus, lessen water availability to the other courntry. The optimal solution is for a cooperative tax on water use in each country. This simple m:del (similar to a Nash-game) can be used to represent in a stylized manner the case of Israel and Jordan. Other international influences on water reform include loan conditionalities (Krueger et al., 1991) and trade agreements (Dinar, 2000).

# 6. CONCLUDING COMMENTS

As indicated in the executive summary, there is quite a range of issues the touch directly and indirectly on irrigation water pricing. [ have attempted to include and synthesize the most relevant literature under the seven broad section headings. Rather than provide a summary and reference list at the end of each of those sections I do so here. However, for those that do not have unfettered access to these studies, i t is hoped that the discussion provided under the broad section headings will prove useful and relevant to current pricing policy questions.

I would like to first focus on several literature sources essential for this review. Above all, the Natural Resource Management and Policy Series published by Kluwer Academic Publishers<sup>25</sup> as edited by Ariel Dinar and David Zilberman is an excellent source of informnation for irrigation practitioners and policy makers. They review a vast array of recent research and often contain empirical applications and case studies. In this series I have drawn heavily from:

- _Conflict and Cooperation on Trans-Boundary Water Resources,_ edited by Richard Just and Sinaia Netanyahu.

- _Markets for Water: Potential and Performance,_ edited by K. William Easter, Mark W. Rosegrant, and Ariel Dinar.

- _Decentralization and Coordinaton of Water Resource Management,_ edited by Douglas D. Parker and Yacov Tsur.

- _Economics of Water Resources. From Regulation to Privatization,_ edited by Nicolas Spulber and Asghar Sabbaghi.

- _The Economics and Management of Water and Drainage in Agriculture,_ edited by Ariel Dinar and David Zilberman.

In addition to these, the IIMI<sup>6</sup> research report series and the review by Dinar and Subramanian (1997) provide a current overview of th1e prevailing irrigation systems found around the world. Other reviews used extensively in this survey are:

- _Designing Institutions for Env,ronmental and Resource Management,_ edited by Edna Tusak Loehman and D. Marc Kilgour.

> 25 Kluwer Academic Publishers: 101 Philip Drive / Assinippi Park / Norwell, MA 02061 / USA or Distribution Centre / Post Office Box 322 / 3300 AH Dordrecht / The Netherlands.

> 26 International Irrigation Management Institute / P.O. Box 2075 / Colombo, Sri Lanka.

37
4) does not apply to large price changes and 5) is made obsolete by the revealed preferences approach.

By analogy with national accounts, Harberger (1971) responded to each of the five criticisms but on point 1) further research has shown that the conditions for the CV approach to apply are more restrictive than initially thought. As shown by Chipman and Moore (1977), changes in consumer’s surplus are single valued and ordinarily equivalent to changes in utility under the conditions of utility maximization, homogeneous utility, integrable demand functions and constant marginal utility. In addition, with changes in prices that affect more than one product, the _CS_ approach is “path-dependent”, meaning that the estimation of the welfare change will be different depending on which price changes first. These two critiques have induced scholars to revalue other methods and approximations of welfare change (see Slesnick (1998) for a full critique of the _CS_ method).

The **Compensating Variation (CV)** was first named by Hicks in his “Value and Capital” but it was Henderson (1941) who first clarified the distinction between _CS_ and _CV_ . Hicks (1942)) later accepted this distinction and also introduced the concept of **Equivalent Variation (EV)** to distinguish Henderson’s concept of _CV_ when welfare change is evaluated at final rather than initial prices. The _CV_ is the monetary compensation required to bring the consumer back to the original utility level after the price change. The _EV_ is the monetary change required to obtain the same level of utility after the price change. For changes from _p_<sup>_a_</sup> to _p_<sup>_b_</sup> of one product, these two variations can be represented as<sup>6</sup> where _ν_ and _e_ represent generic indirect utility and expenditure functions.

The **Laspeyers Variation (LV)** is defined as the exact change in income necessary to purchase, after the price variation, the same bundle of goods purchased before the price variation. The **Paasche Variation (PV)** is defined as the exact change in income required to purchase the final bundle of goods at initial prices. Hence, possible representations of the two indexes are the following:

> 6See Layard and Walters (1978) or Dixit and Weller (1979).

9
**Table 4. Incidence** **<u>of Violent Shocks</u>**

|**Type of shocks**|**Percentage**<br>**of**<br>**households**<br>**affected**|**Percentage of**<br>**Communities**<br>**Affected**|
|---|---|---|
|Murder|11.22%|13.19%|
|Cattle Theft|24.88%|31.09%|
|Land Expropriations|0.98%|1.04%|
|Threats by armed groups|4.39%|4.45%|
|Kidnappings|1.46%|1.31%|
|At least one violent shock|36.10%|43.55%|
|N|205|3,754|
|Total number of shocks|74|1,635|

Source: CLS-WILL: first wave – community questionnaire.

**Table 5. Household Income: Descriptive Statistics**

||**Violent**<br>**shock**|**No**<br>**Violent**<br>**Shock**|**Difference**<br>**between**<br>**means (t-test)**<br>**All**|
|---|---|---|---|
|Total Income|2,637,983|1,869,390|***<br>2,200,207|
||(9,286,131)|(6,843,082)|(7,994,481)|
|Agricultural production||||
|income|982,511|1,033,592|1,011,606|
||(3,493,644)|(4,979,059)|(4,401,047)|
|Livestock production income|1,223,497|582,002|***<br>858,114|
||(8,077,275)|(4,052,256)|(6,125,504)|
|Labor Income|312,582|335,226|325,480|
||(494,640)|(536,679)|(519,045)|
|Labor Income as a<br>percentage of total income|0.41|0.52|***<br>0.47|
||(0.40)|(0.40)|(0.40)|

Standard errors in parenthesis. Source: CLS-WILL: first wave – household questionnaire.

38
**<u>Table A.31. The district fixed-effect RDD-DD regressions of households’ income using a ‘donut’ sample</u>**

|||||Dependent variables<br>|<br>|||
|---|---|---|---|---|---|---|---|
|Explanatory variables|Log of per<br>capita income|Log of per<br>capita income<br>from wages|Log of per<br>capita income<br>from nonfarm<br>production|Log of per<br>capita income<br>from farm<br>production|Log of per<br>capita<br>remittances|Log of per<br>capita public<br>cash transfers|Log of per<br>capita income<br>from other<br>sources|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Program *Post-program period|0.2085|1.4672|1.0765*|-0.4884*|1.2916*|0.8844*|0.7303|
||(0.1604)|(0.9095)|(0.6333)|(0.2526)|(0.7531)|(0.4953)|(0.4981)|
|(Poverty rate – 50) *Post-program|-0.0179|-0.1060|-0.1307**|0.0534**|-0.1292**|-0.0556*|-0.0698*|
|period|(0.0171)|(0.0762)|(0.0596)|(0.0265)|(0.0589)|(0.0313)|(0.0372)|
|Program * (Poverty rate – 50) *Post-|0.0107|0.0322|0.1311**|-0.0574**|0.0887|0.0433|0.1075**|
|program period|(0.0176)|(0.0819)|(0.0645)|(0.0271)|(0.0651)|(0.0415)|(0.0442)|
|Age of household heads|0.0288***|0.0540***|0.0673***|0.1387***|-0.0421***|-0.0973***|-0.0608***|
||(0.0029)|(0.0163)|(0.0095)|(0.0144)|(0.0091)|(0.0115)|(0.0117)|
|Age squared of household heads|-0.0003***|-0.0008***|-0.0008***|-0.0014***|0.0006***|0.0012***|0.0007***|
||(0.0000)|(0.0002)|(0.0001)|(0.0002)|(0.0001)|(0.0001)|(0.0001)|
|Gender of household head (male=1,|0.0102|0.1407|0.2359***|0.6502***|-0.4488***|-0.1180*|-0.3801***|
|female=0)|(0.0179)|(0.1559)|(0.0755)|(0.0742)|(0.0793)|(0.0628)|(0.0580)|
|Ethnic minorities (yes=1, Kinh=0)|-0.5679***|-0.1670|-1.7312***|0.8275***|-0.5734***|0.5718***|-0.4833***|
||(0.0524)|(0.1819)|(0.1645)|(0.1361)|(0.1275)|(0.1055)|(0.1428)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|8.5472***|2.8292***|1.3281***|3.7045***|4.8671***|1.9666***|3.1140***|
||(0.1037)|(0.4317)|(0.2560)|(0.3473)|(0.2964)|(0.3361)|(0.2909)|
|Observations|36,184|36,184|36,184|36,184|36,184|36,184|36,184|
|R-squared|0.373|0.155|0.106|0.139|0.273|0.139|0.093|

Note: This table reports district fixed-effect RDD-DD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

81
interrupting public transport services seems to have had no effect whatsoever on daily cases. Restrictions on personal liberties such as stay at home or banning meetings and public events would have reduced the rate of daily cases by 5 percentage points. Results also show that the CCMs have been effective in reducing daily death rates. However, in this case, the stress of health systems capacity reflected in the rate of daily COVID-19 cases to doctors and nurses also played a critical role in increasing the number of fatalities.

**The paper is organized in five sections including this introductory one.** Section 2 briefly presents the main global trends of contagion and death rates since the outbreak of the COVID-19 pandemic, highlights regional differences in the evolution of the pandemic, and provides a snapshot of the containment measures adopted by governments to reduce the spread of the pandemic. Section 3 describes the econometric methods used in the identification of the factors influencing the governments’ decision to adopt CCMs, their timing and the effectiveness of the different CCMs. Section 4 describes the main results of the estimations. Finally, section 5 summarizes the findings and underlines their policy implications.

**2.** Global and Regional Trends of COVID-19 and Government Responses

**By end of August 2020, COVID-19 had infected almost 25 million people globally and caused nearly one million deaths since it emerged in late 2019.**<sup>**1**</sup> The outbreak was initially centered in Hubei province in China, but it soon shifted to other epicenters, including the Islamic Republic of Iran; northern Italy; Spain; and New York. Cases worldwide leveled off in April after social distancing measures were put in place in many of the areas with early outbreaks (Figure 1). But as countries began to reopen in May and June, the United States, Latin America and the Caribbean (LAC), and the Middle East and North Africa (MENA) have seen a resurgence of COVID-19 cases (Figure 3).

**The rapid increase in COVID-19 cases has caused a significant loss of life and overwhelmed many health systems.** The daily new cases of COVID-19 deaths peaked globally at 244 per million by the beginning of April, largely driven by Western Europe, LAC and Europe and Central Asia (ECA) (Figure 4). However, as strict lockdowns were adopted, countries managed to contain the mortality rate and, as a result, daily COVID-19 deaths dropped by 50% by the end of August (Figure 3). Most of these deaths could have been caused not by the disease itself, but by the further strain on health care systems. This is particularly true for countries where health systems were unable to cope with the surge in demand due to the lack of health care workers, medical equipment and supplies.

> 1 Data on cases and deaths used in our study, which covers the period January 1 to August 31, 2020, were obtained from Hale et al. (2020).

4