rafmacalaba/datause-encoder-data huggingface dataset:
**Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS2442 A Firms's-Eye View of Policy and<br>Fiscal Reforms in Cameroon|Bernard Gauthier<br>Isidro Soloaga<br>James Tybout|September 2000|L. Tabada<br>36896|
|WPS2443 The Politics of Economic Policy<br>Reform in Developing Countries|Richard H. Adams Jr.|September 2000|M. Coleridge-Taylor<br>33704|
|WPS2444Seize the State, Seize the Day:<br>State Capture, Corruption, and<br>Influence in Transition|Joel S. Hellman<br>Geraint Jones<br>Daniel Kaufmann|September 2000|D. Billups<br>35818|
|WPS2445 Subsidies in Chilean Public Utilities|Pablo Serra|September 2000|G. Chenet-Smith<br>36370|
|WPS2446 Forecasting the Demand for<br>Privatized Transport: What Economic<br>Regulators Should Know, and Why|Lourdes Trujillo<br>Emile Quinet<br>Antonio Estache|September 2000|G. Chenet-Smith<br>36370|
|WPS2447 Attrition in Longitudinal Household<br>Survey Data: Some Tests for Three<br>Developing-Country Samples|Harold Alderman<br>Jere R. Behrman<br>Hans-Peter Kohler<br>John A. Maluccio<br>Susan Cotts Watkins|September 2000|P. Sader<br>33902|
|WPS2448 On "Good' Politicians and**"Bad"**<br>Policies: Social Cohesion,<br>Institutions, and Growth|Jo Ritzen<br>William Easterly<br>Michael Woolcock|September 2000|A. Kibutu<br>34047|
|WPS2449 Pricing Irrigation Water: A Literature<br>Survey|Robert C. Johansson|September 2000|M. Williams<br>87297|
|WPS2450 Which Firms Do Foreigners Buy?<br>Evidence from the Republic of<br>Korea|Caroline Freund<br>Simeon Gjankov|September 2000|R. Vo<br>33722|

<!-- Start of picture text --> 9D/<br><!-- End of picture text -->
<!-- Start of picture text --> Figure 5. Progressivity and Redistributive Effect of Taxes<br>Figure A. Direct Taxes<br>0.70 0.035<br>0.60 0.030<br>0.50 0.025<br>0.40 0.020<br>0.30 0.015<br>0.20 0.010<br>0.10 0.005<br>0.00 0.000<br>Kakwani Marginal contribution<br>Figure B. Indirect Taxes<br>0.10 0.010<br>0.05<br>0.005<br>0.00<br>0.000<br>-0.05<br>-0.005<br>-0.10<br>-0.010<br>-0.15<br>-0.015<br>-0.20<br>-0.25 -0.020<br>Kakwani Marginal contribution<br>Kakwani Index<br>(change in Gini points)<br>Marginal contribution to equity<br>Regressive <------------------> Progressive<br>(change in Gini points)<br>Kakwani Index Marginal contribution to equity<br>Regressive <------------------> Progressive<br><!-- End of picture text --> Source: Source: Armenia: Younger et al (2016); Bolivia: Paz Arauco et al (2014); Brazil: Higgins & Pereira (2014); Chile: Ruiz-Tagle & Contreras (2014); Colombia: Melendez (2014); El Salvador: Beneke et al., (2014); Georgia: Cancho & Bondarenko (2016); Mexico: Scott (2014); Peru: Jaramillo (2013); Russia: Lopez-Calvo et al (2016); Sri Lanka: Arunatilake et al (2016); South Africa: Inchauste et al (2016); Uruguay: Bucheli et al (2014); Poland: own estimates based on the 2014 HBS.

Note: Old-age pensions are treated as deferred income for all countries shown. Marginal contributions are the <u>difference between the consumable income Gini coefficient with and without indirect taxes.</u> 23
|**TABLE 18: NURSES, BASIC**|**CLINICAL EQUIPMENT A**<br>(1)|**ND MEDICINES AV**<br>(2)|**AILABLE AT HEALTH**<br>(3)|**FACILITIES†**<br>(4)|
|---|---|---|---|---|
||Number of nurses|Basic clinical|Vaccination|Delivery|
||present|equipment|equipment|equipment|
|Post indicator|0.191|0.030|0.102**|0.016|
||[0.344]|[0.035]|[0.046]|[0.047]|
|PBF/Post interact|1.222***|0.100**|-0.013|0.209***|
||[0.468]|[0.043]|[0.063]|[0.064]|
|Control 1/Post interact|0.738|0.125***|0.021|0.189***|
||[0.475]|[0.043]|[0.060]|[0.060]|
|Control 2/Post interact|-0.172|0.024|-0.037|0.082|
||[0.475]|[0.044]|[0.070]|[0.070]|
|p-value PBF vs. C1|0.291|0.488|0.563|0.729|
|p-value PBF vs. C2|0.003|0.043|0.724|0.061|
|p-value PBF vs. C3|0.010|0.021|0.842|0.001|
|Baseline mean C3|2.725|0.679|0.702|0.535|
|N|369|370|370|370|
||(5)|(6)|(7)|(8)|
||General medicines|Family planning<br>methods|Malaria treatment<br>medicines|Vaccines available|
|Post indicator|0.045|-0.105*|-0.048|-0.116**|
||[0.039]|[0.063]|[0.063]|[0.057]|
|PBF/Post interact|0.064|0.168*|-0.014|0.131|
||[0.058]|[0.091]|[0.080]|[0.088]|
|Control 1/Post interact|0.089|0.078|-0.028|0.113|
||[0.063]|[0.097]|[0.083]|[0.091]|
|Control 2/Post interact|0.050|0.100|0.021|0.053|
||[0.064]|[0.097]|[0.093]|[0.093]|
|p-value PBF vs. C1|0.701|0.361|0.844|0.850|
|p-value PBF vs. C2|0.830|0.491|0.672|0.432|
|p-value PBF vs. C3|0.270|0.067|0.864|0.136|
|Baseline mean C3|0.768|0.482|0 .646|0.530|
|N<br>* = p < 0.10, ** p < 0.05, *** p<br>†Results from difference-in-di|370<br>< 0.01<br>fferences regression mode|370<br>ls examining the effe|370<br>ct of PBF on the numb|370<br>er of nurses present on|

<mark>† Results from difference-in-differences regression models examining the effect of PBF on the number of nurses present on the day of the survey, basic clinical equipment and medicines available at the health facility</mark> . <mark>Regression model adjusted for facility-level control variables (type of health facility public/private/religious, urban/rural). Standard errors were clustered at the health facility level.</mark> 51
# **Appendix 3b.  Sample of a mVAM Questionnaire**

# **_Introduction:_**

Hello, my name is [name enumerator/operator]. I am calling on behalf of the United Nations World Food Programme. WFP is conducting nationwide phone surveys in Yemen to learn about its population’s food security. If you agree to participate in this survey, we will ask you some questions about the household’s food consumption and coping. Your participation in this survey is voluntary. Each survey will take maximum 10-15 minutes of your time. All your answers will remain confidential.

**<u>OPERATOR:</u>** What is your year of birth? _____ If respondent’s age is less than 18, end survey

**<u>OPERATOR:</u>** What is the sex of the head of households?  _____

# **<u>Section 1: Geographic and Demographic info</u>**

**Question 1: OPERATOR** In which Governorate has your Household been living for the past 3 months? ............ [List of Governorates] (drop-down list)

**Question 2: OPERATOR** In which District has your Household been living for the past 3 months? ............ [List of District] (drop-down list)

**Question 3: OPERATOR** How many members living in the household for the past 3 months? .... ( report the number)

# **<u>Section 2: Food Assistance</u>**

**Question 4:** **_<u>OPERATOR:</u>_** When did you receive food assistance last time?

- Never received food assistance

- Less than a month ago

- Between 1 and 3 months ago

- More than 3 months ago

**Question 5:** **_<u>OPERATOR:</u>_** <u>If yes, in which form did you receive it? ......... [Select One]</u>

- in-kind

- Cash

- Vouchers

Was this assistance from WFP? ......... [Report Yes/No/I don’t know]

# **<u>Section 3: Food Consumption Score (FCS) Section</u>**

**Question 6: OPERATOR** How many members of your household were home in the last 7 days?

- ..................................................................................................  [Report the number of people]

# **Question 7: (Staples)**

**_<u>OPERATOR:</u>_** In the past 7 days, how many days did your household eat **main staple starches** , including **cereals, grains, tubers and/or roots** (such as potatoes, rice, pasta, bread, wheat flour, or other grains/cereals)? ..................................................................................... [Report the number of days, from 0-7]

# **Question 8: (Pulses)**

**_<u>OPERATOR:</u>_** <u>In the past 7 days, how many days did your household eat</u> **pulses, nuts, and/or seeds** ? (Including beans, fava bean, peas, peanuts, lentils or others)?

32
<!-- Start of picture text --> Min scores in HS GPA or national entry test is an admission requirement e<br>Research skills are very important when hiring faculty<br>* Percent of faculty working in the industry<br>HEI provides job market information @<br>Somebody is in charge of industry relations @<br>HEI trains students for job interviews e@<br>Test required for graduation @<br>Teaches a foreign language e<br>Collect student satisfaction data more than once per year e@<br>Teaches numerical competencies e<br>-20 0 20 40<br>Percent<br>@ Training and curriculum = @ Link with productive sector Faculty @ Other Practices<br><!-- End of picture text --> <!-- Start of picture text --> Research skills are very important when hiring faculty<br>Students and peers' informal comments are very important in faculty evaluation<br>* Percent of faculty with SCP degree<br>* Percent of <40 years old faculty<br>* Percent of faculty working in the industry<br>* Percent of faculty with 5yrs+ industry experience<br>Classroom observation is very important in faculty evaluation<br>HEI coordinates job interviews with firms @<br>HEI has agreements with private firms to hire graduates .<br>Industry lends or provides equipment to program for student training @<br>HEI trains students for job interviews és<br>HEI provides job market information ©<br>Government scholarships are used by some students €<br>Analyze student performance to solve problems more than once per year &<br>Time assigned to practical training (%) 3<br>Collect student satisfaction data more than once per year @<br>Student feedback is very important to update program @<br>Teaches numerical competencies @<br>Government standards are very important to update program @<br>Number of workshops or labs available for practice<br>-40 -20 0 20 40<br>Percent<br>Infraestructure @ Training and curriculum @ Costs<br>@ Link with productive sector Faculty<br><!-- End of picture text -->
was systematically targeted to areas with low educated parents.<sup>29</sup> Another concern relates to concurrent government programs that might have aected children's educational outcomes and might be spatially correlated with Inpres intensity across districts. Duo (2001) carefully considers such threats to the identifying assumption and includes controls for a water and sanitation program implemented under Inpres. We thus include controls for the water and sanitation program exposure across districts; see below for details.

We provide evidence on the plausibility of the parallel trends assumption using an event study design (please see the two immediately following subsections for details). In addition to the parallel trends assumption, a DiD design requires a second identifying assumption: the no anticipation assumption. The no anticipation in our context implies that the schooling of the comparison cohorts cannot be aected by anticipatory actions of parents or children themselves before the actual school opening in a village. However, it is not likely to be a concern in our application for the following reason. While it is possible that parents may invest time and money (say on books) in anticipation of a primary school opening in the village next year, such investments will be for the children of primary schooling age, not for the cohorts who completed primary schooling and constitute our comparison group. Such parental investments thus do no aect the nal schooling attainment of the comparison cohorts.

# (4.1) Estimating Equation: Linear Model

In this section, we rely on the linear intergenerational educational mobility model. The DiD empirical model in our application can be written as: where _Eikt_ is an indicator of educational attainment of child _i_ and his/her parents (depending on the superscript), superscripts _c_ and _p_ refer to children and parents respectively, _k_ denotes the birth district of child _i_ , _t_ denotes time period (year), _Expt_ is a dummy that takes on the value 1 for the children exposed to the Inpres schools (born between 1968-1972) and zero otherwise (born between 1957-1962). _Inpk_ is a measure of the intensity of the new school

> 29The estimates are available upon request.

12
**<u>Table 2: Millet and sorghum consumption in Mali for different household groups, 2006</u>**

||Percentag|e of households co<br>Auto|nsuming|Average co|nsumption for all<br>Auto|households|Average c|onsumption for house<br>consumption<br>Auto|holds with positive<br>|
|---|---|---|---|---|---|---|---|---|---|
||Purchase|consumption|Total|Purchase|consumption|Total|Purchase|consumption|Total|
|**All**|65.80%|52.90%|91.00%|57,707.51|78,094.34|135,801.86|87,677.81|147,703.45|149,153.08|
|**Residence area**||||||||||
|Urban|79.40%|26.90%|88.40%|68,675.63|21,316.04|89,991.67|86,505.74|79,230.87|101,854.02|
|Rural|57.70%|68.40%|92.70%|51,161.11|111,982.84|163,143.95|88,640.02|163,784.73|176,072.59|
|**Region**||||||||||
|Kayes|51.40%|67.70%|91.00%|47,363.89|122,121.98|169,485.86|92,167.77|180,406.70|186,201.56|
|Koulikoro|58.60%|73.70%|94.80%|62,123.73|134,332.16|196,455.89|106,092.76|182,160.38|207,288.59|
|Sikasso|34.50%|64.30%|79.80%|14,373.50|91,588.62|105,962.12|41,669.61|142,516.41|132,858.56|
|Ségou|64.30%|55.00%|94.00%|44,103.07|80,295.56|124,398.63|68,612.18|145,887.83|132,365.69|
|Mopti|87.60%|51.70%|97.70%|94,695.56|74,001.27|168,696.83|108,125.43|143,248.41|172,620.14|
|Tombouctou|78.30%|39.70%|87.00%|49,946.46|24,439.19|74,385.64|63,820.40|61,624.04|85,498.63|
|Gao|91.80%|26.30%|94.30%|91,119.48|14,043.52|105,163.01|99,297.43|53,335.23|111,536.47|
|Kidal|38.90%|5.70%|42.20%|26,701.72|1,416.54|28,118.26|68,625.52|24,992.43|66,618.44|
|Bamako|86.80%|16.40%|91.00%|82,602.02|7,722.35|90,324.38|95,194.28|47,105.36|99,274.81|
|**Quintile**||||||||||
|Q1|42.60%|73.40%|87.60%|25,911.89|91,306.84|117,218.73|60,764.43|124,468.88|133,836.50|
|Q2|57.20%|70.50%|92.90%|41,725.15|105,180.62|146,905.77|72,919.83|149,173.31|158,192.34|
|Q3|61.20%|64.80%|93.00%|58,529.32|111,379.59|169,908.91|95,575.94|172,013.97|182,710.06|
|Q4|69.20%|48.00%|90.70%|67,669.66|79,811.85|147,481.52|97,773.89|166,220.25|162,646.38|
|Q5|83.60%|26.90%|90.70%|75,461.63|29,760.69|105,222.32|90,282.78|110,452.22|116,073.06|

Source: Authors’ estimation using ELIM 2006.
> **Table 5.  Differences** <u></u> _Uj_   _Rj_ <u></u> **and Expected Gain by Province**

|<br>**Province**|**Difference**<br>**Urban- Rural**|**Expected**<br>**gain**|
|---|---|---|
|Azuay|0.142|0.380|
|Bolívar|-0.027|0.211|
|Cañar|-0.098|0.140|
|Carchi|0.105|0.343|
|Cotopaxi|-0.106|0.132|
|Chimborazo|0.004|0.242|
|El Oro|-0.041|0.197|
|Esmeraldas|-0.017|0.221|
|Guayas|-0.189|0.049|
|Imbabura|0.094|0.332|
|Loja|0.195|0.433|
|Los Ríos|-0.270|-0.032|
|Manabí|-0.169|0.069|
|Morona Santiago|0.621|0.859|
|Napo|0.130|0.368|
|Pastaza|0.250|0.488|
|Tungurahua|0.054|0.292|
|Zamora|0.101|0.339|
|Sucumbíos|0.207|0.445|
|Orellana|0.296|0.534|

_Source_ : Own calculations based on ECV.

To estimate the importance of characteristics and returns obtained from these characteristics in explaining welfare differences between urban and rural areas, we analyze each of these factors separately.

- i) _Geographic profile of living standards_ . This reflects the differentials in returns between urban and rural areas, isolating the structural component, i.e., controlling for all non-geographical attributes. The geographic log welfare ratio is defined as:

Any differences between the log of welfare ratio in (5) and (6), for a given province, are due to differences in the returns to the common characteristics **X** * .

- ii) _Concentration profile of living standards_ . This reflects the spatial concentration of non-geographic characteristics, by isolating the effects of non-geographic attributes controlling for geographic variables. The concentration log welfare ratio is defined as:

log _CONUj_  _E_  log _Wi_ **X** _i_  **X** _Uj_    _N_  **β** ' _N_ **X** _Uj_

> where **X** _Uj_ and **X** _Rj_ represent the mean characteristics of the households living in the province _j_

> for the urban and rural areas, respectively;  _N_ and **β** _N_ are the weighted average parameters:

10
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|---|---|---|---|---|---|---|
|Marketcapitalizationoutoftop<br>Numberof|||Standard|||Weighted|
|<br> <br>10largestcompanies(%)<br>countries ||Average ||Median|| deviation|||average®|
|World<br>46|448|44.8|18.2|3.7|74.2|63.6|
|Bydeveloped/developing economies|||||||
|Developed economies<br>25|42.4|42.6|20.8|37|74.2|64.4|
|Developing economies<br>21|47.6|51.2|14.6|24.6|72.1|60.9|
|Byincome level|||||||
|High income<br>25|42.4|42.6|20.8|3.7|74.2|64.4|
|Upper middle income<br>15|45.7|45.8|14.6|24.6|71.6|59.9|
|Lower middle income<br>6|52.4|54.3|14.6|26.8|72.1|66.7|
|Low income<br>0|||||||
|Byregion|||||||
|High income:OECD<br>20|43.5|43.2|20.2|3.7|74.2|64.9|
|High income: non-OECD<br>5|38.0|39.6|24.8|5.8|65.0|55.2|
|EastAsia&Pacific<br>=|58.2|53.2|8.8|$1.2|71.6|69.6|
|<br><br>Europe & Central Asia<br>2|41.6|41.6|10.4|34.3|49.0|37.4|
|LatinAmerica&Caribbean<br>6|37.0|35.0|11.0|24.6|53.7|42.1|
|<br><br>Middle East& North Africa<br>4|41.8|42.3|15.3|26.8|56.0|44.8|
|South Asia<br>2|63.7|63.7|11.9|55.3|72.1|72.2|
|Sub-SaharanAfrica<br>2|54.7|54.7|18.3|41.8|67.6|68.4|

Source: Global Financial Development Database, 2008-10 data. Note: OECD = Organisation for Economic Co-operation and Development. a. Weighted average by stock market capitalization.
could include experimenting with the housing as in Qiu et al. (2021), social opportunities, and other amenities migrants have at destination, and experimenting with improving job opportunities in the origin region through training programs or other job programs.

Finally, there are other aspects of the migration decision that this simple model in equation 3 does not explicitly incorporate, but where experimentation may also be possible. A notable example is behavioral factors. Some of these (such as attention costs) could be included in _C_ , others (such as loss aversion and impatience) could be included in more general formulation with decision-making over multiple periods and with an alternative to expected utility maximization. This can lead to experiments designed to overcome some of these behavioral constraints. For example, Hainmueller et al. (2018) experiment with behavioral nudges such as reminders and text messages to encourage immigrants who are eligible for a federal fee waiver to take advantage of this.

In addition to the above experiments which change actual migration policies or inputs into the migration decision, there are other types of experiments which just vary perceived characteristics. One example is audit studies, which randomly vary characteristics of individuals on resumes and then measure the impact of this characteristic on interview callbacks. In the migration domain, this has been used to examine whether employers value migration experience of returnees (e.g. Abarcar (2015) and Wah (2018)), and whether they discriminate against immigrants from different countries (e.g. Duguet et al. (2010)).

# **3.2 Some key logistical and methodological issues that arise when conducting migration experiments**

Running any randomized experiment involves many key design issues, data collection issues, and implementation issues. We focus here on some specific issues that are particularly important to consider when conducting migration experiments.

_Migration is usually a rare event._ This is particularly the case when considering international migration. For example, even in a country known for international migration like Mexico, where 870,000 Mexicans moved from Mexico to the U.S. in the five-year period between 2009 and 2014 (Gonzalez-Barrera, 2015), this five-year flow represents less than one percent of Mexico’s population. This means that if researchers start with a random sample of the population in the origin country and implement an experiment designed to change migration decisions, they will need incredibly large samples, or lack statistical power for detecting impacts. This was the case in work we did in the Philippines, in Beam et al. (2016), where we found that few people migrated even after we offered substantial migration assistance. This is still useful for understanding whether certain barriers to movement are binding, but is a challenge for researchers intending to examine migration outcomes. An alternative is to select a sample at much higher risk of migration. For example, Shrestha (2020) recruited subjects who had just finished applying for passports in Nepal. An in8
24 incentive, possibly without any change in economlc behavior. If this were the case, the "incentives" would simply represent a transfer of revenue from the government to the business sector.

Tables 1 and 2 present a summary of fiscal incentives offered in 28 developing countries; the data are from Shah and Toye (1980).9 The primary mechanism for providing investment incentives is the tax holiday, in which the company is exempt from company taxes for a fixed number of years which, in this sample, ranges from 2 to 10. The second most popular incentive is some form of high depreciation allowance, investment credit, or development rebate. The third commonly used scheme is the exemption of imported capital goods from import tariffs.

There are a number of methods for testing whether these incentives are effective.<sup>1 0</sup> Some studies have examined the investment to GNP ratio before and after the tax changes (see references in Shah and Toye (ST)), although it is difficult to attribute changes in this ratio only to the tax changes. Essentially (if there is no trend), there is a 50 percent probability that the investment to GNP ratio will go up after the tax change.

Alternatively, the businessmen can be interviewed to assess

> 9 For an overview of investment incentives in developing countries, also see Usher (1977).

1 0This section draws heavily on Shah and Toye (1980).