rafmacalaba/datause-encoder-data huggingface dataset:
# _3.5 Aggregate insurance participation_

Summary statistics for insurance takeup in 2003 and 2004 are presented in Table 3. In 2003, rainfall insurance was sold to 148 farmers in two villages, mostly members of borewell users associations. This increased to 315 farmers across 43 pilot villages in 2004. Policies sold covered 570 acres of crop, insuring a total sum of Rs. 3,409,200; equivalent to Rs. 10,822 per farmer (USD $240, based on an exchange rate of $1US = Rs. 45).

# **Table 3: Insurance Participation, 2003-2004**

Table below presents data on insurance purchases for mandals where survey villages are located. Dataset used in this paper includes information on 267 of the 315 buyers in 2004.

||**Num**<br>**buy**|**ber of**<br>**ers**|**Num**<br>**which**<br>**BA**<br>**clie**|**ber of**<br>**were**<br>**SIX**<br>**nts**|**Num**<br>**ac**<br>**cov**|**ber of**<br>**res**<br>**ered**|**To**<br>**insu**|**tal sum**<br>**red (Rs)**|**Num**<br>**vill**|**ber of**<br>**ages**|
|---|---|---|---|---|---|---|---|---|---|---|
||**2003**|**2004**|**2003**|**2004**|**2003**|**2004**|**2003**|**2004**|**2003**|**2004**|
|||_Rai_<br>|_n gauge_|_s of Ma_<br>|_haboob_|_nagar di_<br>|_strict_||||
|Atmakur|56|32||27||83||498,000|1|4|
|Mahaboobnagar||75||26||128||768,000||12|
|Narayanpet|92|125||90||199||1,183,200|1|12|
|||<br>|_Rain ga_|_uge of A_<br>|_nantap_|_ur distri_<br>|_ct_||||
|Hindupur||83||50||160||960,000||15|
|**Total**|**148**|**315**||**193**||**570**||**3,409,200**|**2**|**43**|

# **4. Determinants of Insurance Participation: Theoretical Predictions**

What does economic theory predict regarding the determinants of insurance market participation? In a simple setting without asymmetric information, a household’s willingness-to-pay for an insurance contract will be (i) increasing in risk aversion, (ii) increasing in the expected insurance payout, (iii) increasing in the size of the insured risk, and (iv) decreasing in basis risk (in other words, increasing in the correlation between the insurance payout and the risk to be insured, or 10
working in the future. As such, there are no incentives for individuals to remain in school if they do not expect to work in the future.

# <u>The decision to start working</u>

Equation (9) provides some intuitive information about when individuals choose to join the labor market. According the system of equations presented above, ��� can become positive for a few reasons:

- a) When the potential future gains from working become more substantial than the benefits from studying one extra hour: when the gains in human capital acquired by staying in school diminish and the gains acquired in the labor market increase, ���� < ���� ; ���<sup>�</sup> ���<sup>�</sup>

- b) When the immediate gains from working at age � increase. If �� becomes attractive enough so that the utility brought by the consumption is larger than the disutility of an extra hour of work;

- c) When the present restrictions require the individual to enter the labor market. If �� becomes insufficient to pay for the minimum consumption and care.

Note that, according to the circumstances the individual faces, the outcome _work-and-study_ can fall at different points of the engagement spectrum. When the job provides excellent opportunities to acquire skills and complements those learned in school, the individual is closer to the full engagement trajectory. However, when individuals enter the labor market out of necessity, to a job that does not provide adequate opportunities for skills-development, they are abnegating potential human capital gains from an extra hour in school to work. When comparing time allocation statistics, these two types of individuals can have the same allocation, but completely different outcomes and implications for the society’s productivity.

# <u>The decision to quit school</u>

Still regarding equation (9), individuals will choose to leave school and stay only in the labor market, that is ��� �0, if ��<sup>��</sup> � ���� ���<sup>�</sup> �� �� � �� � ���� � for ∀ ��� ∈�0, ��. ���<sup>�</sup> ���<sup>�</sup> ���� ���<sup>�</sup> ~~�~~ ���<sup>�</sup> This can be a result of the following situations: 9
# **Table 2a Plan to Return (Marginal Effects reported)**

|Variables|Model (1)|Model (2)|Model (3)|
|---|---|---|---|
|Probability of being employed in Romania|-0.269|-0.266||
|Predicted earnings in Romania|-0.000|||
|Predicted earnings in Romania (Logs)||0.017||
|Actual Earnings-Predicted Earnings (Logs)|||-0.103***|
|Agriculture|-0.049|-0.048|-0.071|
|Hotel|-0.071|-0.072|-0.072*|
|Manufactured|-0.039|-0.040|-0.030|
|Transport|-0.097*|-0.097*|-0.081|
|Health|-0.171**|-0.173**|-0.135**|
|Trade|-0.106*|-0.106*|-0.104*|
|Domestic|0.055|0.055|0.024|
|Other|-0.144***|-0.145***|-0.126***|
|Age|0.027|0.025|-0.004|
|Age-Squared|-0.000|-0.000|0.000|
|Female|-0.131**|-0.122*|-0.087***|
|Household Size|-0.063***|-0.062***|-0.057***|
|Rural|0.044*|0.044*|0.037|
|Married|-0.008|-0.008|0.030|
|Children|-0.060*|-0.061*|-0.064*|
|No education|-0.276|-0.261|-0.140|
|Prime education|0.128|0.142|0.142|
|General education|-0.027|-0.016|0.025|
|Vocational education|0.025|0.031|0.044*|
|Higher education|-0.001|-0.020|-0.055|
|Northern Europe|-0.058**|-0.059**|-0.035|
|Non-Rom South Europe|0.050|0.049|0.026|
|Eastern Europe|-0.427***|-0.431***|-0.397***|
|Non-European Anglophone|-0.082|-0.081|-0.074|
|Other countries|0.293*|0.288|0.259|
|Observations|2,011|2,011|1,990|

Notes: Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1. Reference groups are not fluent and no language training abroad, worse occupation, construction, high-school education, Southern Europe.

19
Thus far, we have assumed one observation per position on a channel, but our actual data are stacked at each channel-position. For example, in the case of DPR, there are multiple years of data at both head and tail of each channel. To account for this, we cluster our standard errors at the _channel-position_ level.

# **5.1.3 Estimating the lobbying influence function**

Letting _Npc_ be the number of irrigators (or, rather, families) at position _p_ of channel _c_ , we posit an aggregate lobbying influence function for coalition _Cp_ of the form where _Lipc_ is landholdings of irrigator _i_ = 1 _, ..., Npc_ at position _p_ of channel _c_ and _Iipc_ is an indicator for whether that same irrigator holds an influential office. The parameter _γ_ reflects the importance of influential office-holding and the aggregator function _G_ can be the mean ̄ operator _G_ ( _x_ 1 _, ..., xk_ ) = _x_ or a percentile operator. In the former case, we have _ιpc_ (0) = _L_<sup> ̄</sup> _pc_ , which is mean landownership at position _p_ of channel _c_ (see Figure 4). Here, of course, each irrigator contributes to _ιpc_ in proportion to their landholdings. For _γ >_ 0, irrigators with large landholdings, to the extent that they hold influential positions, contribute to _ιpc_ more than in proportion to their landholdings. Conversely, an office-holder contributes little to the aggregate influence of his coalition unless he also has a significant economic stake in the outcome of the lobbying contest in the form of large landholdings.

Since _γ_ is an estimated parameter, equation (14) falls under the class of regression models analyzed by Hansen (1996) in which the nuisance parameter, in this case _γ_ , is not identified under the null _H_ 0 : _βH_ = _βT_ = 0 _._ Following Hansen (1999), estimation is straightforward: For any given value of _γ_ , estimate [ _β_ 0( _γ_ ) _, βH_ ( _γ_ ) _, βT_ ( _γ_ ) _, λ_<sup>_′_</sup> ( _γ_ ) _, μc_ ( _γ_ )] using a conventional fixed effects regression and form the sum of squared residuals _S_ ( _γ_ ) =<sup>�</sup><sup>_N_</sup> _c_ =1<sup>_C_(ˆ</sup><sup>_ε_</sup> _Hc_<sup>2+ ˆ</sup><sup>_ε_2</sup> _Tc_<sup>)</sup><sup>_._</sup> 25
**<mark>Figure 8. Changes in per capita household income between 2019 and 2020 by quintiles of the</mark> initial income distribution using different employment data. In percentages**

<!-- Start of picture text -->
-14.0% -12.0% -10.0% -8.0% -6.0% -4.0% -2.0% 0.0% 2.0%<br>Actual data<br>Elasticities<br>Actual data<br>Elasticities<br>Actual data<br>Elasticities<br>Actual data<br>Elasticities<br>Q1 Q2 Q3 Q4 Q5<br>Turkey<br>Philippines<br>South Africa<br>Brazil<br><!-- End of picture text -->

Notes: Projections following the methodology detailed in section 3.1. Estimates based on reported employment data. Estimates do not include changes in non-labor income.

<mark>Figure 9 compares changes in poverty, vulnerability, and the middle class between 2019 and 2020 when using the methodology of Section 3 and when using employment projections based on employment elasticities as an input in the macro-micro simulation model. The results indicate that the use of employment elasticities leads to: (i) smaller poverty increases and even poverty reductions in some cases (for instance, the share of people below the 6.85 USD-a-day poverty line in the Türkiye); (ii) smaller reductions in the share of the vulnerable population and increases in some cases (Philippines, South Africa and Brazil); and (iii) smaller reductions in the size of the middle class in some cases (Philippines, South Africa and Brazil) and a an increase in some other</mark> (Türkiye).

<mark>For Brazil specifically, the differences are substantial. Using 2020 employment data, our simulations project poverty increases ranging between 3 and 5 percentage points depending on the poverty line used. When using employment elasticities to project the employment level of 2020, the expected poverty increases are below 2 percentage point (between 25% and 40% of the poverty increases using actual employment data). The large differences in Brazil, however, are in line with the earlier findings that the employment-GDP elasticities were underestimating the magnitude of</mark> 22
Edwards, Sebastian (1995); "Why are Savings rates so different across countries?";<sup>NBER</sup> Working Paper No. 5097.

European Bank for Reconstruction and Development (EBRD) Transition Report, 1994 and 1995.

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13
# **Table 16: Composition analysis – Murnane & Ganimian’s (2014) sample of information provision studies**

||**Conn 2014**|**Glewwe et**<br>**al. 2014**|**Kremer, Brannen, &**<br>**Glennerster 2013**|**Krishnaratne, White,**<br>**& Carpenter 2013**|**McEwan 2014**|
|---|---|---|---|---|---|
|Andrabi et al. (2009) - Pakistan|-|-|-|Providing<br>information|Informational<br>treatments|
|Camargo et al. (2011) - Brazil|-|-|-|-|-|
|Dinkelman and Martínez (2013) -<br>Chile|-|-|-|-|-|
|Hicks et al. (2013) - Kenya|-|-|-|-|-|
|Jensen (2010) - Dominican Republic|-|-|Providing earnings<br>information|Providing<br>information|-|
|Jensen (2010a) - India|-|-<br>|-|-|-<br>Informational|
|Loyalka et al. (2013) - China|-|-|-|-|treatments|
|Mizala and Urquiola (2013) - Chile|-|-|-|-|-|
||Information|-|Providing earnings|Providing|Informational|
|Nguyen(2009)- Madagascar|provision||information|information|treatments|
|Percentage of studies included in<br>review|11%|0%|11%|33%|33%|
|Percentage of RCT studies included<br>in review|11%|0%|11%|33%|33%|

37
donors for procurement of works, goods and services. Mean use of procurement systems in the main sample of observations is 37.7%.

The PDMS data form an unbalanced three-dimensional panel: surveys were conducted in 2006, 2008 and 2011, and were designed to obtain information on aid flows for the previous calendar year (2005, 2007 and 2010, respectively) for each donor-recipient pair. In each successive survey round, the comprehensiveness and quality of data were improved. The number of recipient countries choosing to participate in the survey increased from 34 in 2006, to 54 in 2008, and 78 in 2011. These 78 recipients account for about three-fourths of total aid worldwide (OECD, 2011a). Guidance on definitions was strengthened and comparability of data reported by donors and by recipients was enhanced in later rounds (OECD, 2011a; OECD 2008).

Coverage of aid within aid-recipient countries is comprehensive with respect to donors. Most of the “emerging” bilateral donors and a few of the smaller multilateral donors and vertical funds are dropped from the analysis, due to missing data on other donor variables from the DAC database. Dropped donors collectively account for a total of only 1.4% of aid reported in the survey. The 34 donors remaining include the 22 traditional DAC donors, Korea (a new DAC member in 2011), Turkey, 7 multilateral organizations, and 3 vertical funds.<sup>4</sup> The panel is unbalanced in multiple respects. Not all recipients are included in all three years. In addition, in each survey year where aid flows for a donor-recipient pair are 0, the dependent variable is undefined so the observation is treated as missing. These features of the data create potential selection problems, considered in more detail below.

_Independent variables_

> 4 Multilaterals in the sample include the African, Asian and Inter-American Development Banks, the World Bank, IMF, EU and UN.  Vertical funds include the GAVI Alliance, IFAD, and the Global Fund to Fight AIDS, Tuberculosis, and Malaria.  The sample includes all donors with sufficient data for listing in the appendix of the OECD (2011) survey report, plus Greece.

8
Improving the quality of education in developing cou-tries requires improving the effectiveness of the schooling that is offered: increasing the learning that takes place. Both group work and testing contribute substantially to increased science achievement, with students in classes in which these practices are used significantly outperforming students in classes in which these pr&ctices are not used. Frequent laboratory use also contributes to achievement, but not as substantially.

The key to improved efficiency is the comparative effectiveness of testing and group work versus laboratories. Our research showed that the effects of group work and testing were substantially higher than those of laboratories, while the costs of the three are vastly different. One study of construction costs for general science labor4tories reported costs per laboratory ranging from $31,000 in Jamaica to $92,000 in Jordan; equipment costs ranged from $11,700 in Botswana to $34,600 in Jamaica, with per student costs averaging about $65 (Mundangephuphu, 1985). By comparison, group work and testing are virtually free. The cost-effectiveness (i.e. efficiency) of group work and testing, therefore, will be much greater than the costeffectiveness of laboratories.

Two other teaching practices -- frequent use of practical activities and frequent use of science textbooks -- were unrelated to student achievement. The failure to find an effect for textbooks is not surprising given the successful efforts of the Philippine government to infuse science classrooms with textbooks. The Philippine Textbook Project produced and distributed 97 million books covering all subject areas from first grade 39
which it is intended." The sharing formula was to be revised by Congress every five years. For municipalities, the 1991 constitution broadened the base of the existing revenue-sharing system from the IVA to all government current revenues and committed the national government to increase the municipal share from 14 percent in 1993 to a minimum of 22 percent by 2002. Thus the 1991 constitution and Law 60 committed the national government to sharing nearly half of all its current revenues with territorial govermments and entities by 2002. (The proportion in 1998 was already 42.5 percent.) The 1991-94 reforms also reduced the national government's discretion in the distribution of transfers. Prior to Law 60, the _situado_ was paid directly to teachers and health workers under the ministries of education and health. Law 60 changed this system to one in which the _situado_ was transferred directly to each departnental government on the basis of a formula.<sup>3</sup> The distribution of revenue sharing among municipalities-the _participaciones municipales_ and the share of the value added tax-was also formula driven: 60 percent was to be distributed in proportion to the number of habitants with unsatisfied basic needs and relative level of poverty (as determined by the Central Statistical Agency), with the remaining 40 percent distributed according to population, administrative efficiency, and improvements in quality of life (all quantitatively defined in legislation). As a transition measure in 1994-98, each municipality received, as a minimum, the amount of IVA transferred in 1992, in constant prices. Also Law 60 specified that part of the transfer would be distributed equally to all municipalities, which meant that small municipalities got much more per capita and that there was a lot of incentive to form new (small) municipalities. The latter happened a lot at first, as in Brazil and Mexico, but then the problem was brought under some control by the rules for certification of municipalities. Even though this sort of

> 3 According to Law 60, 15 percent of the _situado_ is to be uniformly distributed to each department and district. The distribution of the remaining 85 percent is to be based on a formula taking into account the current number of students enrolled, the number of school-age children not attending school, the number of patients seen by health units, and the number of potential patients based on population.

10
Gerber, Alan S., Donald P. Green, and Ron Shachar. 2003. ―Voting May Be Habit-Forming: Evidence from a Randomized Field Experiment.‖ _American Journal of Political Science_ 47 (3): 540–50.

Green, Donald P., Alan S. Gerber, and David W. Nickerson. 2003. ―Getting Out the Vote in Local Elections: Results from Six Door-to-Door Canvassing Experiments.‖ _Journal of Politics_ 65 (4): 1083–96.

Gerber, Alan S., Donald P. Green and Christopher W. Larimer. 2008. ―Social Pressure and Voter Turnout: Evidence from a Large-scale Field Experiment.‖ _American Political Science Review_ 102 (1): 33-48.

Harbaugh, W. T. 1996. ―If People Vote because they Like to, then Why do so Many of them Lie?‖ Public Choice 89 (October):63–76.

Horiuchi, Yusaku, Kosuke Imai and Naoko Taniguchi. 2007. ―Designing and analyzing randomized experiments: application to a Japanese election survey experiment.‖ _American Journal of Political Science._ 51(3):669–687.

Imai, Kosuke. 2005. "Do Get-Out-the-Vote Calls Reduce Turnout? The Importance of Statistical Methods for Field Experiments." American Political Science Review, 99 (2): 283-300.

Jacoby, Hanan and Ghazala Mansuri, 2011, ―Crossing Boundaries: Gender, Caste and Schooling in Rural Pakistan‖, mimeo Kling, Jeffrey, Jeffrey Liebman, and Larry Katz. 2007. ―Experimental Analysis of Neighborhood Effects‖ _Econometrica_ , 75 (1): 83-119.

Krackhardt, David. 1988. ―Predicting with Networks: Nonparametric Multiple Regression Analysis of Dyadic Data.‖ _Social Networks_ , 10(4): 359–81.

Kremer, Michael and Edward Miguel, 2001, ―Worms: Identifying Impacts On Education And Health In The Presence Of Treatment Externalities,‖ _Econometrica_ , Vol. 72, No. 1, 159–217 Lott, John R. Jr. and Lawrence W. Kenny. 1999. ―Did Women‘s Suffrage Change the Size and Scope of Government?‖ _Journal of Political Economy_ 10 (Dec.): 1163-1198.

Lundberg, Shelly, Pollak, Robert and Wales, Terence, 1997, Do husbands and wives pool their resource? Evidence from the United Kingdom child benefit. Journal of Human Resources, vol. 32(3), pp. 463–80.

Manski, Charles F. 1993. "Identification of endogenous social effects: the reflection problem," _Review of Economic Studies_ 60:531-542 Manski, Charles F. 1995. Identification Problems in the Social Sciences. Harvard University Press, Cambridge).

27
# 2. Data

The OECD’s Programme for International Student Assessment assesses students between ages 15 years and 3 months to 16 years and 2 months in grades 7 or higher in both OECD and non-OECD countries. The assessment occurs every three years and began in 2000. In 2000, five Latin American countries participated; in 2006, six participated.

The primary sampling unit for the PISA survey is the school. School selection occurs within specified strata according to a proportional-to-size method. Within schools, 35 students within the age and grade targets are selected with equal probability. Sampling weights are then constructed reflecting changes in information about the school size, student non-response, and other factors (OECD 2002: 39 – 53).

PISA’s assessment framework consists of questions or test _items_ that are designed to measure the average competency of students from a particular country to “complete tasks relating to real life, depending on a broad understanding of key concepts, rather than limiting the assessment to the understanding of subject-specific knowledge” (OECD 2007: 20). The framework consists of three subject areas: mathematics, reading, and science.

Like other major international assessments, the purpose of PISA is to measure the average competency of students at a national level. Since its purpose is not to measure competency at the individual level, matrix-sampling booklet design is used where 8