rafmacalaba/datause-encoder-data huggingface dataset:
**Figure A.2:** Electoral Data Checks: Digit Distribution in Vote Tallies

<!-- Start of picture text --> (a) Vote tallies: first digit (b) Vote tallies: second digit<br>0 2 4 6 8 10 0 2 4 6 8 10<br>First digit Second digit<br>Villages won by incumbent Villages won by incumbent<br>Villages lost by incumbent Villages lost by incumbent<br>kernel = epanechnikov, bandwidth = 0.6046 kernel = epanechnikov, bandwidth = 0.7338<br>Kolmogorov-Smirnov p-value: 0.311 Kolmogorov-Smirnov p-value: 0.466<br>(c) Vote tallies: third digit (d) Vote tallies: last digit<br>0 2 4 6 8 10 0 2 4 6 8 10<br>Third digit Last digit<br>Villages won by incumbent Villages won by incumbent<br>Villages lost by incumbent Villages lost by incumbent<br>kernel = epanechnikov, bandwidth = 0.7392 kernel = epanechnikov, bandwidth = 0.7432<br>Kolmogorov-Smirnov p-value: 1.000 Kolmogorov-Smirnov p-value: 0.999<br>.25 .2<br>.2<br>.15<br>.15<br>.1<br>Density .1 Density<br>.05<br>.05<br>0 0<br>.25 .25<br>.2 .2<br>.15 .15<br>Density .1 Density .1<br>.05 .05<br>0 0<br><!-- End of picture text --> _Notes_ : This figure plots the distribution of the first, second, third, and last digits of candidate vote tallies, separately for villages won and villages lost by the incumbent. At the bottom of each panel, we report the p-value from a KolmogorovSmirnov test of equality of distributions across the two types of villages.

42
Glewwe, P. and Jacoby, H. (2000). “Recommendations for collecting panel data”, in M. Grosh and P. Glewwe (eds.), Designing Household Survey Questionnaires for Developing Countries: Lessons from 15 years of the Living Standards Measurement Study. Washington D.C., World Bank.

Horvitz, D.G. and D.L. Thompson. (1952). A generalisation of sampling without replacement from finite-universe. J. Amer. Statist. Assoc., 47,663-685.

Kish, L. (1965), Survey Sampling, New York: Wiley. ISBN 978-0471109495.

Lavallée, P. ( 2007). Indirect Sampling. Springer: Ottawa.

Lynn, P., (2009), Methodology of Panel Surveys, Wyley. Wiley and Sons, Ltd, Publication.

Mecatti, F. ( 2007). A single frame multiplicity estimator for multiple frame surveys. Survey Methodology, 33(2): 151-157.

Narain, R.D. 1951. On sampling without replacement with varying probabilities. J. Ind. Soc. Agril. Statist., 3,169-174.

Särndal C.E., and Lundström S. (2015). Estimation in Surveys with Nonresponse. Wiley. New York.

Singh, A.C. & Mecatti, F. (2011). Generalized Multiplicity-Adjusted Horvitz-Thompson Estimation as a Unified Approach to Multiple Frame Surveys. Journal of Official Statistics, 27(4): 633–650.

Uganda Bureau of Statistics (2011), The Uganda National Panel Survey (UNPS) 2009/10 – Main Report, Kampala, Uganda.

Uganda Bureau of Statistics (2017), The National Population and Housing Census 2014 – Education in the Thematic Report Series, Kampala, Uganda.

Uganda Bureau of Statistics (2016), The National Population and Housing Census 2014 – Main Report, Kampala, Uganda.

University of Essex . (2016), British Household Panel Survey, <u>https://www.iser.essex.ac.uk/bhps</u> <mark>Witoelar, Firman. (2011). T</mark> racking in Longitudinal Household Surveys. <mark>World Bank’s Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA). World Bank.</mark> World Bank . (2020), Basic Information Document - The Uganda National Panel Survey (UNPS) - - - 2015/2016. <u>http://surveys.worldbank.org/lsms/programs/integrated surveys agriculture</u> - <u>ISA/uganda#bootstrap panel.</u> Zardetto, D . (2015). ReGenesees: An advanced R system for calibration, estimation and sampling error assessment in complex sample surveys. Journal of Official Statistics, 31 (2), 177.

35
distance measures for individual points inside and outside of park boundaries permit creation of boundary zones of arbitrary size. For this exercise, we use a zonal width of 10 km.<sup>6</sup>

# **3.2  Zonal deforestation rates**

We compute mean annual Hansen pixel counts per grid cell, within 10 km zones inside and outside of park boundaries. Mean counts are equivalent to deforestation rates, since the Hansen grid cells have a uniform size (.0243 decimal degrees). In the final step, we compute the ratio of outside to inside mean pixel counts.

A complication is introduced by the possibility of differential representation for inside and outside cells. If all protected areas were separated by distances greater than 10 km, this would not be a problem because each section of the within-park zone would have a corresponding section in the outside zone. However, some parks abut or are separated by fewer than 10 km, so intersection of their outside zones prevents full outside representation for either park. Even where proximity restricts outside representation, or where inside and outside grid cells are limited by small park size, our computations draw on huge samples because each grid cell summarizes the information in 8,100 30 m Hansen pixels.

# **4.  Statistical Analysis**

Using the methodology described in Section 3, we compute annual outside/inside deforestation ratios for 4,028 parks in 64 countries: 20 in Africa, 23 in LAC, and 21 in Asia/Pacific. Table 1 tabulates our sample parks by region and country.

> 6 We recognize that the area inside a protected area/ park and its surrounding area do not always have similar environmental conditions as pointed out by Mas 2005; however thorough investigation of all relevant characteristics of 4,028 parks in 64 countries and their surrounding areas was beyond the scope of our analysis. Instead, we used 10km area surrounding park boundaries for testing the effectiveness of the parks .Use of 10-km buffer is a common practice in the literature.  For example, see Pfeifer et al. 2012, Gaveau et al. 2009, Bruner et al. 2001.

6
# **3. Migration and Remittances in Nepal: Descriptive Analysis**

The history of foreign employment in Nepal dates back almost 200 years, when Britain began recruiting men from the hillsides of Nepal, known as Gorkhas into the British armed forces. After India’s independence in 1947, the Indian military also began enlisting Nepali men. Currently, about 3,500 Nepali solders serve in the British army and more than 50,000 Nepalese are enlisted in the Indian military. India was the first country to attract civilian migrants from Nepal. The inflow of working migrants to India has increased sharply since the 1950s and 1960s, and India now represents the largest market for migration to foreign countries in Nepal (Sheddon 2005).<sup>3</sup> The Foreign Employment Act of 1985 was the first legislative document to officially recognize the benefits of international migration (Jha 1999). Around that time, foreign labor migration from Nepal extended from India to the countries of the Southeast and Far East, and later to Arab Gulf States. The total number of Nepali migrants working abroad reached 750,000 in 1997, contributing about NPR 35 billion to the country’s economy in form of remittances (Sheddon, Gurung, and Adhikari 2000). The reform of the administrative system during 2000 and 2001 resulted in a significant boost in both domestic and international migration. Before the reforms, passports could only be obtained in the country’s capital, but under the new regulations, district offices were given the authority to issue passports and other travel documents (McKenzie 2006).

Domestic migration has increased in Nepal since the success of government’s efforts to control endemic malaria in the Terai in early 1950s. The inter-district migration constitutes 13.2 percent of domestic migration (Central Bureau of Statistics 2003), while rural–urban migration represents 25.5 percent; rural-to-rural migration, however, is significantly higher, at 68.2 percent. The poor rural regions of the mid- and far-west underwent a net out-migration, with migrants moving from the mountainous and hillside areas to the plains and urban areas. These regions were also the most affected by the Maoists insurgency over the past 10 years (Do and Iyer 2007). In the Katmandu valley and other urban areas, and in the Terai region, inflows of migrants surpass outflows.

> 3 The “Treaty of Peace and Friendship” signed by the Indian and Nepali governments in 1950 allowed Nepali nationals to enter and work in India without a visa and any job restrictions (Thapliyal 1999).

7
|ransfers<br>(14)|-0.17_⇤⇤⇤_<br>(0.05)<br>-0.04<br>(0.25)<br>0.05<br>(0.22)<br>X|X<br>842<br>0.51|ate. _Credit_<br>pital stock<br>cidence of<br>_ts_ capture<br> logarithm<br> _<_ 0_._1; _⇤⇤_:|
|---|---|---|---|
|HH T<br>(13)|-0.20_⇤⇤⇤_<br>(0.05)<br>-0.09<br>(0.26)<br>0.12<br>(0.22)<br>X|X<br>842<br>0.45<br>2.90<br>0.28|rowth r<br>f the ca<br>es the in<br>_A Benef_<br> natural<br>s: _⇤_: _p _|
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|**increasi**<br>Inf<br>(7)|-6.15_⇤⇤⇤_<br>(2.24)<br>-2.37_⇤_<br>(1.30)<br>8.38_⇤⇤⇤_<br>(2.44)<br>X|X<br>3348<br>0.18<br>8.72<br>8.48|ls. _M2 _<br>_sset Pr_<br>is the in<br>ed part-<br> per cen<br>tandard|
|**equality-**<br> Prices<br>(6)|0.19_⇤_<br>(0.10)<br>0.07<br>(0.11)<br>-0.60_⇤⇤⇤_<br>(0.17)<br>X|X<br>3861<br>0.25|e mode<br>redit. _A_<br>_fation_ <br> employ<br>ent as a<br> GDP. S|
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|**Expla**<br>rowth (%)<br>(4)|0.13_⇤⇤_<br>(0.07)<br>0.10_⇤⇤⇤_<br>(0.03)<br>-0.08_⇤⇤_<br>(0.04)<br>X|X<br>3564<br>0.04|d at the t<br> rate of p<br>mmer (2<br>ortion of <br> general <br>lds as pe|
|Credit G<br>(3)|0.15_⇤⇤_<br>(0.07)<br>0.10_⇤⇤⇤_<br>(0.03)<br>-0.08_⇤⇤_<br>(0.04)<br>X|X<br>3564<br>0.02<br>0.06<br>0.25|is liste<br> growth <br>, and Ti<br>the prop<br> paid by<br>househo|
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|M2 Grow<br>(1)|-14.30_⇤⇤_<br>(6.91)<br>-2.95<br>(2.98)<br>8.65_⇤⇤_<br>(3.76)<br>X|X<br>3550<br>0.08<br>19.48<br>19.31|pendent <br>tures the<br>Feenstra, <br>yment, w<br>rithm of <br>fers recei<br>_<_0_._01.|
||CBI<br>GDP (log)<br>Population (log)<br>Year FE<br>Quadratic Time|Country FE<br>Observations<br>_R_2<br>Mean of DV<br>SD of DV|Table 4: The de<br>_Growth (%)_ cap<br>as proposed in <br>part-time emplo<br>the natural loga<br>of current trans<br>_p <_0_._05; _⇤⇤⇤p _|

21
**Table 4: Mining and Assessment of Public Services: the role of corruption (bribe payment) and trust in local governments**

||(1-LPM)|(2-LPM)|(3-LPM)|(4-LPM)|(5-LPM)|(6-LPM)|
|---|---|---|---|---|---|---|
|||||Living|Public||
|_Dependent Variables_|Livingstandards|Public Services|Optimism|standards|Services|Optimism|
|Active 50 km|0.024**|0.028***|-0.022*|0.054**|0.024|0.035*|
||(0.010)|(0.009)|(0.012)|(0.023)|(0.020)|(0.018)|
|Inactive 50 km|0.004|-0.003|0.015**|0.048***|0.035***|0.042***|
||(0.006)|(0.005)|(0.007)|(0.010)|(0.008)|(0.010)|
|Bribe|0.007|-0.003|-0.085***||||
||(0.015)|(0.013)|(0.016)||||
|_Active 50 km * Bribe_|-0.246***|-0.265***|-0.090*||||
||(0.039)|(0.034)|(0.049)||||
|_Inactive 50 km * Bribe_|-0.006|0.012|-0.180***||||
||(0.023)|(0.019)|(0.026)||||
|Local Government Distrust||||-0.392***|-0.415***|-0.177***|
|||||(0.012)|(0.010)|(0.013)|
|_Active 50 km * Local Government Distrust_||||-0.122***|-0.065*|-0.162***|
|||||(0.039)|(0.035)|(0.037)|
|_Inactive 50 km * Local Government Distrust_||||-0.089***|-0.071***|-0.144***|
|||||(0.020)|(0.017)|(0.020)|
|Natural Resources Rents (ln)|-0.033***|-0.121***|-0.022***|-0.013*|-0.100***|-0.011*|
||(0.007)|(0.006)|(0.007)|(0.007)|(0.006)|(0.007)|
|Control of Corruption|0.156***|0.203***|0.292***|0.080***|0.124***|0.272***|
||(0.014)|(0.012)|(0.012)|(0.014)|(0.012)|(0.012)|
|Constant|1.455***|2.061***|1.836***|1.599***|2.209***|1.885***|
||(0.119)|(0.102)|(0.119)|(0.119)|(0.101)|(0.120)|
|R-squared|0.07|0.11|0.12|0.08|0.13|0.12|
|Observations|109,282|105,199|80,553|109,282|105,199|80,553|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Round FE|Yes|Yes|Yes|Yes|Yes|Yes|

**_Notes:_**

1: Significance level: * p<0.10, ** p<0.05, ***p<0.01. Robust standard errors are in parenthesis.

2: Specifications (1) and (4) refers to the respondents' view of how the government is improving the living conditions of the poor. The dependent variable in specification (2) and (5) is a composite indicator constructed through polychoric correlation and which combines all the respondents' views on various types of public services and policies. The dependent variable in specification (3) and (6) refers to the expected living standards of the respondent in 12 months after the survey collection. Individual-level control variables are added in all specifications: age (ln), the square of the logarithm of age, gender, education, residential area (urban), employment status, interest in public affairs and deprivation (poverty level).

20
Simnlarly, usinig cross-section data, Psacharopoulos (1997) finds that school failure (as captured by a discrete variable) is significantly higher for indigenous children compared to their nonindigenous counterparts in Bolivia. My objective then is to see whether the determinants of time use in household chores and labor are also different for native boys and girls and whether schooling attainment of natives is worse than that of non-natives after controlling for other factors and unobserved heterogeneity.

Lastly, theoretical time allocation models call for the inclusion of a non-wage income variable on the right-hand side. However, in developing country settings, non-wage income may itself be endogenous for other reasons. Households may receive remittances if they suffer a negative economic shock, thus remittances which are typically included in non-wage income may make that variable endogenous.<sup>24</sup> In order to avoid this problem I use a stock indicatorhousehold wealth. This variable includes the value of consumer durables, self-assessed value of owned housing, the value of other property and equipment (see World Bank, 1998 for details).

# _School Costs_

The costs of schooling can influence not just the demand for children's education, but also the time children allocate to housework and "outside" labor. There are typically two components of costs-direct costs and indirect costs or opportunity costs.

Direct costs of schooling-such as tuition fees, uniforms and transport costs-may play a role in the demand for children's education, as well as their decision to work and to do housework.<sup>25</sup> Not including such costs may create a missing variable problem in schooling, labor and housework regressions. While school costs are usually available in household survey data, 23 See Roseriouse (1989) and Mason and Lampietti (1998) for a criticism of the use of this concept in poverty analysis.

24 See among others, Rosenzweig and Stark (1989).

25 In the studies in Grootaert and Patrinos (1999) there is mixed evidence of the effect of school costs and distance to school on child labor.

14
to be ultra-processed and displace healthy foods, rises in low- and middle-income countries (Monteiro et al. 2017).

# **3 Data and methodology**

3.1 Data on food prices For each country, from the National Statistics Office (NSO), we collect the list of food and nonalcoholic beverage items for which data are collected for the Consumer Price Index, along with expenditure weights for each item. At the national level and for a subnational level for which data are available, we collect prices per specified unit of each item for each month over time, preferably since the start of the current reference or base period for the CPI.<sup>11</sup> We have obtained CPI data in sufficient depth for four countries in South Asia: Afghanistan, India, Pakistan, Sri Lanka. We calculate the <mark>Nutritious Food Price Index f</mark> or all four, but the Cost of a Recommended Diet in only three; we cannot calculate CoRD for India because we were not able to obtain raw prices for that country.

# 3.2 Cost of the Recommended Diet (CoRD)

# _Food-Based Dietary Guidelines (FBDGs)_

We reviewed food-based dietary guidelines (FBDGs) for the eight countries in the South Asia Region: Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka. This was done to obtain quantitative dietary guidance and enable calculation of CoRD. The CoRD indicator was developed as part of the Indicators of Affordability of Nutritious Diets in Africa project, funded by an Innovative Methods and Metrics for Agriculture and Nutrition Actions (IMMANA) grant from UKAid, in 2017.

Five countries have their own guidelines or some version of guidelines. Nepal has key messages on what constitutes a nutritious diet but no pictorial food guide. Afghanistan has key messages and a food guide, but no quantitative amounts or guidelines document yet available. Bangladesh, India, and Sri Lanka have quantitative guidelines. Figure 1 shows an example of quantitative guidelines from Sri Lanka.

The five countries’ dietary guidelines were sourced from a repository of food-based dietary guidelines maintained by the Food and Agriculture Organization of the United Nations (FAO).<sup>12</sup> We analyzed these guidelines in two steps: first, an analysis of the key messages, and second, an analysis of the food guide and the full guidelines document if available.

> 11 Consumer Price Index data for India were retrieved from the Ministry of Statistics and Programme Implementation (http://www.mospi.gov.in/). Data for Pakistan came from the Pakistan Bureau of Statistics; those for Afghanistan from the Central Statistics Organization, National Accounts Department, Price Statistics unit; and those for Sri Lanka from the Department of Census and Statistics, Prices and Wages Division.

> 12 Available at: http://www.fao.org/nutrition/education/food-dietary-guidelines/home/en/.

5
Table 6: ITT Treatment Effect Estimates, Entrepreneurial Spirit OLS

||ITT Treatm<br>Estim|ent Effect<br>ates|Observations|Control<br>group<br>mean|
|---|---|---|---|---|
|Outcome variable||||(std. dev.)|
||(1)|(2)|(3)|(4)|
|PCA entrereneurial sirit index|0.237*|0.223|373|-0.094|
|p p|(0.140)|(0.139)||(1.371)|
|PCA entrepreneurial spirit index w/o components d and e|0.240*|0.208|373|-0.095|
||(0.140)|(0.138)||(1.343)|
|KLK entrereneurial sirit index|0.130|0.128|378|-0.055|
|p p|(0.103)|(0.102)||(0.964)|
|KLK entrereneurial sirit index w/o comonents d and e|0.153|0.140|378|-0.064|
|p p p|(0.107)|(0.105)||(0.961)|
|Index components|||||
|a. I have professional goals.|0.114*|0.112*|378|4.531|
||(0.060)|(0.060)||(0.651)|
|b. I revise my goals periodically.|0.128|0.115|378|4.029|
||(0.085)|(0.082)||(0.840)|
|c. If I don’t reach a goal in the way I wanted to I try again.|-0.033|-0.035|378|4.374|
||(0.077)|(0.077)||(0.683)|
|d. I can’t motivate my business partners.^|0.064|0.055|376|2.277|
||(0.121)|(0.121)||(1.086)|
|e. Everything I need for success lies in myself.|0.074|0.090|378|3.938|
||(0.112)|(0.108)||(1.025)|
|f. I prefer to do routine tasks instead of doing something new in|-0.013|-0.017|376|2.000|
|my work.^|(0.104)|(0.103)||(0.964)|
|g. I think the government should give me opportunities.^|-0.061|-0.075|377|3.545|
||(0.139)|(0.132)||(1.215)|
|h. I have to reach some goals every day to feel satisfied.|0.126|0.114|378|3.897|
||(0.109)|(0.107)||(1.076)|
|Controls for baseline value of outcome|No|Yes||-|

Note: Components marked with ^ are reverse coded in the indices. The PCA index is generated using Principal Components Analysis. The KLK index follows the methodology in Kling, Liebman, and Katz (2007) and is the normalized average of z- scores for all non-missing process measures, using mean and standard deviation in the control group to calculate the z- scores. Each row in Columns 1 and 2 contains the treatment effect point estimates and robust standard errors for separate OLS estimations. All regressions include controls for strata dummies and re-randomization variables, as well as a dummy for having been surveyed in March 2009 (vs. April, May or June) at follow-up. Column 4 contains means and standard deviations for the control group at follow-up. Significance levels: *10 percent, **5 percent, ***1 percent.

28
step was to aggregate the information from individual to household level and merge POF1, POF2, POF3, POF4 and POF5. This procedure resulted in 56,091 observations at the household level of which only 5 households had per capita income equal to zero<sup>55</sup> . From this, we identified 9,263 treated households. After analyzing household incomes per capita we observed some outliers, households classified as beneficiaries with incomes per capita much larger than the overall average income (R$ 828.29). We excluded 114 observations<sup>56</sup> of treated households (1.2% of total treated households) with incomes per capita above R$ 1,000. The resulting data set contains information on 55,976 households (9,149 BFP’s beneficiaries and 46,827 non-beneficiaries).

To check whether our sample is representative, we compared it with the total share of BFP’s treated and the amount of transfer by region available in Figure 1. The data are consistent with our sample on POF 2008-2009, for which 16.3% of total households (Brazil) are treated and the regional distribution follows a similar pattern: 22.0% (North), 27.9% (Northeast); 7.8% (Southeast); 5.6% (South), 7.6% (Center-West).<sup>57</sup>

# **4.3 Descriptive statistics**

Before going into the analysis, it is informative to consider some descriptive statistics. Figure 3 provides a general overview of the expenditure composition of treated and untreated households. It is noticeable that most of the beneficiaries’ expenditures are concentrated on essential goods such as food, housing, hygiene and transport. Moreover, if we compare the difference in expenditure share between treated and untreated households, there are three groups of goods for which the difference is large: food, transport and education. The expenditure share on food is almost twice as large for BFP beneficiaries, while their expenditure share on transport and education is approximately half of the share spent by non-beneficiaries.

> 55The observations with an income per capita equal to zero were excluded from the sample.

> 56In a previous version of the essay we used the full number of observations without dropping outliers: and the results - available upon request - remained unchanged.

> 57Without dropping 114 observations we have the following distribution of treated households: 16.51% (Brazil), 22.5% (North), 28.09% (Northeast); 7.90% (Southeast); 5.76% (South), 7.78% (Center-West)

23