rafmacalaba/datause-encoder-data huggingface dataset:
Table 6: Program impacts on self control skills, time preferences, and values

|||||I|TT regres|sion (N= 94|7)||
|---|---|---|---|---|---|---|---|---|
||||Ther|apy only|Cas|h only||Both|
|Outcome, z-score|Round|Control|ITT|Std. Err.|ITT|Std. Err.|ITT|Std. Err.|
|||mean|||||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Self-control skills (_θ_)<sup>_†_</sup>|2–5w|-0.037|0.085|[.098]|-0.147|[.104]|0.037|[.096]|
||12–13m|-0.070|0.159|[.090]*|-0.025|[.095]|0.244|[.095]**|
|Impulsiveness|2–5w|-0.010|-0.011|[.101]|0.180|[.108]*|0.104|[.095]|
||12–13m|0.082|-0.178|[.096]*|0.006|[.098]|-0.212|[.099]**|
|Conscientiousness|2–5w|-0.077|0.109|[.105]|0.046|[.106]|0.163|[.105]|
||12–13m|0.018|-0.065|[.097]|-0.028|[.100]|0.044|[.097]|
|Perseverance/GRIT|2–5w|-0.035|0.027|[.099]|-0.130|[.105]|0.042|[.104]|
||12–13m|-0.037|0.116|[.099]|0.057|[.099]|0.105|[.103]|
|Reward responsiveness|2–5w|-0.010|-0.071|[.106]|0.107|[.107]|0.013|[.105]|
||12–13m|0.072|-0.165|[.102]|0.084|[.100]|-0.242|[.102]**|
|Forward-looking time preferences (_δ, β_)|2–5w|-0.202|0.179|[.098]*|0.071|[.099]|0.318|[.099]***|
||12–13m|-0.149|0.149|[.102]|0.105|[.102]|0.209|[.105]**|
|Patience|2–5w|-0.093|0.187|[.073]**|0.116|[.073]|0.267|[.074]***|
||12–13m|-0.240|0.170|[.103]*|0.145|[.096]|0.258|[.099]***|
|Time inconsistency|2–5w|0.008|-0.063|[.074]|-0.009|[.076]|-0.138|[.075]*|
||12–13m|0.129|-0.072|[.083]|0.018|[.087]|-0.059|[.084]|
|Anticriminal & antiviolent values (_σ_)<sup>_†_</sup>|2–5w|0.100|-0.206|[.094]**|-0.187|[.096]*|-0.180|[.097]*|
||12–13m|0.070|-0.076|[.088]|0.026|[.088]|-0.177|[.086]**|
|Attitudes toward use of violence|2–5w|0.021|-0.141|[.105]|-0.201|[.106]*|-0.057|[.107]|
||12–13m|0.051|0.019|[.108]|0.080|[.109]|-0.046|[.109]|
|Attitudes toward criminality|2–5w|0.139|-0.177|[.107]*|-0.154|[.112]|-0.242|[.110]**|
||12–13m|0.044|-0.062|[.102]|-0.041|[.100]|-0.244|[.101]**|
|Attitudes toward political violence|2–5w|0.111|-0.224|[.109]**|-0.136|[.108]|-0.173|[.113]|
||12–13m|0.096|-0.119|[.105]|0.012|[.105]|-0.167|[.106]|

_Notes:_ The table reports intent to treat estimates of the effect of each treatment arm in the short and long run, controlling for baseline covariates and block fixed effects. We focus on pre-defined composite measures, typically defined by survey module. The overall summary indexes are the standardized mean of its composite outcomes, standardized. Heterosketastic robust standard errors are reported in brackets. *** p<0.01, ** p<0.05, * p<0.1 25
Table 12: Civil Society Leader’s Information and Information Exchange

||Mean Control|Effect of VH<br>Training|Effect of VH<br>+ CL Training|CL Effect||
|---|---|---|---|---|---|
|CL’s knowledge test score (0-1)|0.693|-0.018|0.064|0.082*|196|
|(CL survey)|(0.214)|(0.038)|(0.039)|(0.044)||
|No. of times exchange information|1.758|-0.268|0.433|0.701*|189|
|btwn CL and VH per week (CL survey)|(1.991)|(0.352)|(0.356)|(0.407)||

Table displays standard errors in parentheses. *, ** and *** indicate significance at the 90, 95 and 99 percent confidence levels respectively.

Table 13: Civil Society Leader Effects by Human Resource Levels

||Know|ledge of VH|(0-1)|Procedural|Compliance o|f VH(st. index)|
|---|---|---|---|---|---|---|
|Measure of human resource|CL Effect|CL Effect|Difference|CL Effect|CL Effect|Difference|
|level|High HR|Low HR||High HR|Low HR||
|Whether VH completed|0.072|0.076|0.004|0.793***|-0.137|-0.930**|
|primary education|(0.050)|(0.070)|(0.088)|(0.238)|(0.334)|(0.417)|
|Whether VH above 65|0.055|0.099|0.044|0.463*|0.490*|0.027|
||(0.057)|(0.061)|(0.086)|(0.273)|(0.289)|(0.408)|
|VH has large court|0.126*|0.028|-0.098|0.722|0.263|-0.459|
||(0.065)|(0.058)|(0.088)|(0.297)|(0.266)|(0.401)|

Table displays standard errors in parentheses. *, ** and *** indicate significance at the 90, 95 and 99 percent confidence levels respectively.

32
14 non contraceptive users with low parity users. There was some relationship between the expectations of old age support and contraceptive use, but in some cases the differences were small and in several cases were reversed. (Bulatao, 1979).

The major cross country studies that have attempted to estimate the effects of pension programs on fertility are that by Hohm in 1975, reanalysis of his data by Kelly et al in 1976, reanalysis by Hohm himself in 1976 and a more recent effort by Entwisle and Winegarden in 1984. Hohm attempted to test two hyp- aeses; (1) The coverage of a country's old age or retirement, invalidity, and survivorship programs will vary inversely with that country's subsequent fertility, and (2) The benefit level of old age, nvalidity and survivorship programs will vary inversely with subsequent levels of fertility. Data (partly generated) from 67 countries showed strong zero order correlations between coverage and benefit levels and total fertility rates. Multiple regressions controlling for infant mortality, newspaper circulation and per capita domestic product, showed only marginally significant coefficients for coverage and no significance for level of benefits. In smaller random subsamples the coverage and benefits measure were significant in one of 5 cases. Despite these rather weak findings, the author stated that since the other determinants of fertility were even weaker, social security programs should be considered to reduce fertility.

Kelly et al took issue with Hohm's findings and showed that if a modernization index composed of 5 variables was used, it eliminated the significance of the coverage and benefit level variables. An expanded analysis by Hohm in a reply used a simpler measure of old-age support, the percentage of GNP going to social security, and found that this measure was
they receive, or the average temperature that occurs. As a result, there are places that have higher and in some cases much higher exogenous exposure to droughts and floods.

This result, that parts of the world experience differential exposure to climate variability and damagecausing climate extremes, has important implications for our understanding of how climate affects human welfare and economic growth. If climate variability was distributed equally throughout the world, then one could surmise that evidence of climate effects on welfare or economic growth would be due to endogenous factors within the states where measured, such as the quality of the institutions or the infrastructure. One may then assume that the key climate differences between countries would be limited to their mean conditions, i.e., some places are hotter or dryer than others. When it is clear that climate variability is not equally distributed but in fact is higher in some regions than in others, then one might surmise that this greater degree of variability could cause conditions that were disadvantageous to human welfare and economic growth. In fact, one could conceive of a state where the effects of repeated droughts or floods are so deleterious that it hinders the ability of that state to make the investments necessary to mitigate the effects of climate variability, investments such as infrastructure and weather observation and forecast systems, and so the effect of climate variability relative to its effect in other states is amplified by the variability itself.

In this analysis we attempt to assess whether climate variability is an impediment to economic growth and a contributor to poverty levels in countries where exposure to variability is high. The analysis makes use of a new rainfall statistic that captures climate extremes in rainfall and their spatial extent. The econometric model is applied to national level growth and poverty statistics representing most countries of the world. It builds on a previous study using a similar approach that identified drought as a significant and negative factor influencing poverty levels and GDP growth in Sub-Saharan Africa (Brown et al., 2010). The results of this study will have significant implications for evaluating the costs of climate change and the continuing dialogue on the best means for adapting to climate change. Typically, climate change impact analyses have focused on changes in mean conditions, effectively ignoring the damages or 3
However, contrary to conventional wisdom, the distribution of public capital investment that generates the largest value-added impact is Scenario C, in which the entire investment is targeted to Region III, the one with the lowest PCVA. This also is the scenario that results in the strongest convergence in PCVA across regions, and the greatest national PCVA value (see table 6).

**Table 6: Modified Results**

|||||New<br>|PCVA by Sce<br>|nario<br>|Percen<br>|t Change in<br>|PCVA<br>|
|---|---|---|---|---|---|---|---|---|---|
|Region|Popn|VA|PCVA|A|B|C|A|B|C|
|I|10|450|45.0|51.1|47.6|45.8|13.5%|5.7%|1.7%|
|II|9|308|34.2|34.7|36.8|35.1|1.3%|7.5%|2.5%|
|III|8|258|32.3|32.8|34.9|39.1|1.6%|8.1%|21.2%|
|Nation|27|1016|37.6|39.50|39.73|39.98|5.0%|5.6%|6.2%|
||||6.9|10.1|6.8|5.4||||

The lessons that we draw from this exercise can be summarized as follows:

1. The structure of interindustry interregional trade within a nation plays a vital role in determining impacts of policies and programs on each region and on the nation.

2. Investments in one region will have impacts on all regions.

3. Whether a nation must sacrifice efficiency in exchange for increased interregional equity will depend upon the intra-and inter-regional interindustry structure of that national system.

4. Altering the structure of intraregional trade can have dramatic impacts on own-region multipliers and system-wide effects.

To further illustrate this final point, consider the following scenario in which, as a result of import substitution, Region III increases its intraregional dependency, as reflected by an increase in its diagonal value from 0.28 to 0.31. Making this single change while holding all else constant in Scenario C results in a 4.4% increase in both the regional and national value-added impacts values. Likewise, strengthening interregional trade can have substantial impact. Doubling Region I’s relatively small purchases from the other two regions increases the value-added impact by 1.75%.

21
Figure 14. Rate of Demographic Dependency Years 1950 – 2100 <!-- Start of picture text --> 120%<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Child Dependency Rate Older People Dependency Rate<br>Dependency Rate<br>1950 1956 1962 1968 1974 1980 1986 1992 1998 2004 2010 2016 2022 2028 2034 2040 2046 2052 2058 2064 2070 2076 2082 2088 2094 2100<br><!-- End of picture text --> Source: United Nations Population Division In this context, the demographic transition process taking place in Ecuador towards an aged population structure affects directly on the number and sufficiency of transfers made through the pension system. This is a consequence of two factors: i) the demographic transition itself, resulting in a change of size in working population and beneficiary groups as population ages, and ii) the changes in public policies as a result of these demographic trends.

Current policies may affect the number of retirements and public finances in the coming decades. Although the Ecuadorian pension system currently has costing capacity, it would face pressure in the medium term because of these described demographic trends.

In this context, the purpose of this section is to analyze the possible impact of the demographic trends on IESS pension insurance financial sustainability. For that reason, the World Bank PROST ( _Pension Reform Options Simulation Toolkit_ ) was applied as it allows simulating income and expenditure paths in pension schemes and reforms thereof, both parametric as well as structural.<sup>7</sup>

> 7 This simulation toolkit works up pension contribution, pension right, system income and expenditure models in a long term. The model design intends to promote an information-based policy formulation. It is a flexible computer-based toolkit easily customizable to a wide range of circumstances depending on each country.

23
||`(1.96)`|`(1.82)`|`(0.47)`|`(0.38)`|
|---|---|---|---|---|
|`judi`|`-0.200`|`-0.177`|`0.040`|`0.029`|
||`(1.51)`|`(1.31)`|`(0.70)`|`(0.50)`|
|`judirate`|`-0.084`|`-0.092`|`-0.074`|`-0.081`|
||`(0.46)`|`(0.50)`|`(1.12)`|`(1.21)`|
|`centgov`|`-0.065`|`-0.077`|`0.012`|`0.030`|
||`(0.31)`|`(0.36)`|`(0.16)`|`(0.38)`|
|`parl`|`-0.502`|`-0.490`|`-0.054`|`-0.054`|
||`(2.60)**`|`(2.52)* `|`(0.72)`|`(0.73)`|
|`cbank`|`0.135`|`0.136`|`-0.106`|`-0.094`|
||`(0.74)`|`(0.73)`|`(1.32)`|`(1.16)`|
|`police`|`0.167`|`0.112`|`0.029`|`0.021`|
||`(1.11)`|`(0.73)`|`(0.43)`|`(0.31)`|
|`govine`|`0.014`|`-0.001`|`-0.060`|`-0.061`|
||`(0.10)`|`(0.01)`|`(1.01)`|`(1.02)`|
|`ginter`|`-0.062`|`-0.055`|`-0.038`|`-0.066`|
||`(0.40)`|`(0.34)`|`(0.62)`|`(1.05)`|
|`corobs`|`0.180`|`0.210`|`-0.085`|`-0.083`|
||`(1.04)`|`(1.19)`|`(1.11)`|`(1.08)`|
|`corry`|`-0.017`|`-0.041`|`-0.189`|`-0.206`|
||`(0.11)`|`(0.26)`|`(2.44)* `|`(2.62)**`|
|`corqb `|`-0.045`|`-0.053`|`-0.022`|`-0.023`|
||`(0.24)`|`(0.28)`|`(0.19)`|`(0.20)`|
|`infr`|`-0.022`|`-0.004`|`0.159`|`0.164`|
||`(0.11)`|`(0.02)`|`(1.77)`|`(1.83)`|
|`roads`|`-0.117`|`-0.087`|`0.019`|`0.010`|
||`(0.87)`|`(0.64)`|`(0.31)`|`(0.16)`|
|`post`|`0.110`|`0.070`|`0.211`|`0.212`|
||`(0.60)`|`(0.38)`|`(1.81)`|`(1.80)`|
|`elec`|`-0.039`|`-0.029`|`-0.086`|`-0.078`|
||`(0.23)`|`(0.17)`|`(0.86)`|`(0.78)`|
|`water`|`-0.183`|`-0.158`|`0.031`|`0.029`|
||`(1.10)`|`(0.93)`|`(0.35)`|`(0.33)`|
|`crimest`|`0.249`|`0.267`|`-0.019`|`-0.012`|
||`(1.23)`|`(1.31)`|`(0.24)`|`(0.15)`|
|`crimeorg`|`0.007`|`-0.039`|`0.093`|`0.105`|
||`(0.04)`|`(0.19)`|`(1.07)`|`(1.20)`|
|`sale1`||`0.179`||`0.075`|
|||`(0.52)`||`(0.73)`|
|`sale2`||`0.737`||`0.296`|
|||`(2.02)* `||`(2.93)**`|
|`owind`||`0.057`||`-0.038`|
|||`(0.28)`||`(0.50)`|
|`owfam`||`0.204`||`-0.025`|
|||`(0.87)`||`(0.25)`|
|`owork`||`0.011`||`-0.173`|
|||`(0.05)`||`(1.59)`|
|`owsta`||`-0.200`||`-0.230`|
|||`(0.81)`||`(2.44)* `|
|`Constant`|`0.914`|`0.357`|`0.915`|`0.746`|
||`(2.97)**`|`(0.73)`|`(8.69)**`|`(5.15)**`|
|`Observations`|`532`|`532`|`2560`|`2560`|

``` Source: BEEPS. Absolute value of z statistics in parentheses; * significant at 5%; ** significant at 1% ``` 42
- Nellis, John. 1999. "Time to Rethink privatization?", _International Finance Corporation Discussion Paper,_ January.

- Pinto, Brian, Marek Belka, and Stefan Krajewski. 1993. " Transforming State Enterprises in Poland: Evidence on Adjustment by Manufacturing Firms," _Brookings Papers on Economic Activity,_ 1: 213-270.

- Pistor, Katharina and Andrew Spicer. 1997. "Investment Funds in Mass Privatization: Lessons from Russia and the Czech Republic," _Viewpoint,_ World Bank, No. 110.

- Pohl, Gerhard, Robert E. Anderson, Stijn Claessens and Simeon Djankov. 1997.

- "Privatization and Restructuring in Central and Eastern Europe: Evidence and Policy Options," Technical Paper 368, The World Bank, Washington DC.

- Shleifer, Andrei and Robert Vishny. 1997, "A Survey of Corporate Governance," _Journal of Finance, 52:_ 737-783.

- . 1994. "Politicians and Firms", _Quarterly Journal of_

_Economics,_ CIX: 995-1025.

_. 1986. "Large Shareholders and Corporate

- Control," _Journal of Political Economy_ 94, No.3, 1986.

- Sleuwaegen, Leo and Wim Dehandschutter. 1986. "The Critical Choice Between the

- Concentration Ratio and H-index in Assessing Industry Performance", _Journal of Industrial Economics_ 35, December, 193-208.

- Stiglitz, Joseph. 1998. "Knowledge for Development: Economic Science, Economic Policy and Economic Advice," paper presented at _Annual Bank Conference on Development Economics,_ the World Bank.

- Tandon, Pankaj. 1995. "Welfare Effects of Privatization: Some Evidence from Mexico," _International Law Journal,_ Vol. 13, No. 2.

- Weiss, Andrew and Georgiy Nikitin. 1998. "Performance of Czech Companies by Ownership Structure," Boston University.

- World Bank. 1997. "Between State and Market: Mass Privatization in Transition Economies", eds. Ira Lieberman, Stilpon Nestor and Raj Desai.

40
households with very low access to education, health, fresh water, sanitation, housing, nutrition and assets regardless of the place of residence.

More specifically, a low level of welfare is characterized by a low literacy rate of the household head, a small proportion of members who cannot read and write in the household and a high proportion of members with no diploma in the household. In the field of health, sanitation and drinking water, the MCA shows that the poor usually do not have access to health care for illness or injury and have no medical coverage. They do not have toilets at home and are not connected to the potable water supply or even to a public fountain nearby (the case of rural households). Poor households also fail to meet their basic nutrition needs to the extent that their expenditure on food is below the food poverty line. These households have also children under age of five suffering from malnutrition and stunted growth.

In the field of housing, poor households live in homes characterized by the absence of basic hygienic conditions and comforts. They have no electricity in their homes, use candles and gas lamps or kerosene for lighting. In terms of communication, poor households do not have televisions, satellite dishes and cell phones. The percentage of households that do not have these means of communication is greater in rural than in urban areas. The group of poor households also lacks basic durables such as gas stove, gas oven, refrigerator or freezer. In terms of employment, poor households are those headed by jobless people. There is also a category of non-poor households who may be well off in terms of employment but they are still very poor compared to other dimensions such as education, health, water and communications. Therefore the phenomenon of the employed poor is also clearly visible in these data.

In contrast to poor households, wealthy households are generally those who have access to education, health, drinking water, sanitation, decent housing, food and basic assets. Households that are well connected to the welfare axis are households whose head is literate, who have a high proportion of literate people and where low levels of education are absent. These same households have also access to basic health services in case of illness or injury and are covered by the health insurance system. They have children under five years of age who do not suffer from stunted growth or malnutrition. These households also have homes connected to potable water and sewage systems and have hygienic toilets. They live in decent housing and have electricity and various means of comfort (kitchen and appliances). Affluent households are households that have access to communication (televisions, mobile phones and satellite dishes), whose head is an employed person and where young children aged 15 to 24 do not exercise a professional activity.

10
assumption that the residuals contain a mix of non-negative and normally distributed stochastic terms.

Estimated time-effects associated with the second (1997) and third (2001) surveys are also reported in the table. Because the first-period dummy is suppressed, the time effects can be interpreted as deviations from the 1995 round of surveys. The results suggest that average transfer costs fell between 1995 and 2001. The estimate associated with 1997 is positive, indicating that average transfer costs rose as the economy destabilized; however the associated z-score lies just outside a 10 percent level of significance. In contrast, a test based on the difference between the 1997 and 2002 effects is statistically significant and indicates that average transfer costs fell from 1997 to 2002.<sup>26</sup> As discussed, a concern in any type of test of the efficiency of spatial markets based on price pairs is that third markets influencing the price outcomes have been omitted from the analysis and that this omission potentially leads to inferential errors. Although we cannot compensate for information about markets that are missing, we test the sensitivity of the results to excluded markets in the context of the sample by randomly excluding some markets and repeating the analysis in a bootstrapping exercise. Results based on 200 replications are reported in table 3. The procedure inflates the standard errors of the respective time-effects, but conclusions based on the first set of standard-errors are not reversed.

The model also provides estimates of the idiosyncratic slack variables, which together with information about relative price changes, can be used provide some insight about differences in individual market outcomes among the three periods. Recall from the earlier discussion that food market reforms of the type undertaken by Bulgaria are expected to lead potentially to different patterns of internal trade and to an improved integration of markets. For any given market pair and commodity, this might mean a reversal of trade flows or possibly a cessation of trade. In terms of the model, this might mean that the inequality _p_ ,1( _c_ )  _p_ (,2 _c_ ) reverses or the value of the associated slack variable,  ,2,1( _c_ ) , might switch from zero to a positive value or both changes might occur together. This makes the equilibrium outcome for any particular market pairing unpredictable. However, in general, market reforms are expected to reduce the disparity in spatial market outcomes, which, in turn, implies that the distribution of the idiosyncratic slack variables should shift toward zero.

> 26 Similar results hold for a fixed-effects OLS model, despite strong evidence against in favor of the composite error specification. See supplemental table S2.

15
6 We use an informal expected-utility framework that captures the risk of medical care expenses associated with informal employment prior to UC to explore the possible effects of the UC reform: we conclude that UC could cause unmarried individuals to prefer the informal sector over the formal sector, and may cause a married couple to favour having both of them working in the informal sector in preference to having the husband work in the formal sector and the wife not working. We explore empirically the impacts of the UC scheme on the probabilities of working, working in the informal sector, and working in the informal sector. Our identification strategy comes from the staggered rollout of the UC scheme over a period of 12 months, with six provinces adopting the program in April 2001, 15 provinces doing so in June 2001, 55 provinces and 13 districts in Bangkok following in October 2001, and the final set of Bangkok’s districts adopting UC between November 2001 and April 2002. We estimate the labor market effects of UC by linking an individual’s labor market status at a particular date to their length of exposure to the UC “regime”, which will depend on the survey date and the person’s province of residence. Our statistical power comes from the fact that we have an average of 62,000 respondents at each survey date, and 68 survey dates most of which are just one month apart. The accuracy of our estimates is enhanced by the fact that 17 of the 68 surveys we use predate the launch of UC: this allows us our regressions to capture the pre-reform trends in our labor market outcomes. We control for the potentially confounding effects of the Thai Village Fund that was also rolled out from 2001 onwards.

We find that UC appears to have encouraged employment especially among married women, to have reduced formal-sector employment among married men but not among other groups, and to have increased informal-sector employment especially among married