rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 3: Estimates of the effect of the movie on perceptions and BREGS participation by gender**

|Dependent variable|F<br>Film in villa|ull R2 Sample<br>ge Constant|<br>N|Film in village|Women<br> <br>Constant|N|Film in villag|Men<br>e<br>Constant|N|
|---|---|---|---|---|---|---|---|---|---|
|1 Can influence wages|0.033|0.637***|4408|0.016|0.617***|2259|0.053|0.659***|2149|
|2 Benefits of participating in Gram Sabha|0.104***|0.152***|4356|0.096***|0.112***|2267|0.115***|0.197***|2089|
|3 Women participate in Gram Sabha|0.088***|0.367***|4507|0.087***|0.340***|2395|0.090**|0.398***|2112|
|4 Get work under BREGS when demands|0.085***|0.084***|4201|0.072***|0.069***|2125|0.100***|0.099***|2076|
|5 BREGS projects have increased employment|0.084***|0.176***|4173|0.067**|0.147***|2096|0.105***|0.206***|2077|
|6 BREGS decreased migration of labor|0.093***|0.140***|4071|0.087***|0.108***|2028|0.102***|0.172***|2043|
|7 BREGS work will be available next year|-0.032|0.563***|1906|-0.041|0.520***|831|-0.03|0.599***|1075|
|8 Women have the right to chose BREGS projects|0.047*|0.632***|3188|0.03|0.614***|1515|0.064*|0.649***|1673|
|9 Assets useful to women created under BREGS|-0.088***|0.832***|4013|-0.090***|0.844***|2039|-0.088***|0.821***|1974|
|10 Women work in BREGS projects|-0.072***|0.721***|4211|-0.045|0.717***|2162|-0.104***|0.726***|2049|
|11 Women treated well at BREGS worksite|0.003|0.981***|2602|-0.004|0.983***|1274|0.01|0.978***|1328|
|12 Women get work if they bring children to worksite|0.008|0.779***|2322|0.01|0.762***|1104|0.009|0.795***|1218|
|13 Distance women would be willing to go to work on<br>BREGS worksite|9.18|61.741***|3063|36.132|61.053***|1584|-24.708|62.457***|1479|
|14 Women paid equal wages as men|0.021|0.807***|2454|0.004|0.824***|1198|0.038|0.790***|1256|
|15 Women of HH would like to work on BREGS|-0.013|0.454***|4929|-0.011|0.467***|2740|-0.015|0.437***|2189|
|16 Women of HH would be allowed to work on BREGS|-0.017|0.458***|4913|-0.019|0.473***|2724|-0.014|0.440***|2189|
|17 Knowledge of BREGS increased in last year in your<br>family|<br>0.163***|0.427***|4834|0.163***|0.348***|2637|0.166***|0.522***|2197|
|<br>18 BREGS work opportunities improved in last year for<br>your family|<br>0.105***|0.119***|4798|0.110***|0.110***|2606|0.098***|0.129***|2192|
|<br>19 Infrastructure improved in village|0.068***|0.611***|4888|0.087***|0.602***|2671|0.044|0.621***|2217|
|20 Work increased in village|0.063**|0.424***|4560|0.048|0.407***|2410|0.081**|0.444***|2150|
|21 Wage increased in village|0.018|0.755***|4627|0.017|0.740***|2472|0.018|0.771***|2155|
|22 Migration decreased in village|0.112***|0.184***|4506|0.094***|0.148***|2355|0.134***|0.225***|2151|

22
# Paris: UNESCO.

UNESCO Regional Office for Education in Asia and the Pacific (1984) SciefnceEducan inAsia nd Eadfi .Bangkok: UNESCO U.S. Dept. of Education (1987) IJUS, Study of Education in Japan. Washington: GPO. Walters, Pamela Barnhouse (1981) "Educational Change and National Economic Development." Hvard Educaional Review 51: 94-106.

Weick, Karl (1976) "Educational Organizations as Loosely Coupled Systems." Adminisave Science,Ouarterly 21: 1-19.

Wheeler, David (1980) Human Resource Development and Economic Growtlh in LQDC's. World Bank Staff Working Paper No. 407. Washington, D.C.: World Bank.

Wong, Suk-Ying (forthcoming) "The Evolution and Organization of the Social Science Curriculum: A Cross-National Study: 1900-1986." Sociology of Education. World Bank (1983) World Tables. Third Edition. Washington: World Bank.

22
|Empowered citizens enhancingaccountabilityof Edu reform andquLucia Casap|
|---|
|Empowered citizens enhancingaccountabilityof Edu reform andquLucia Casap|
|Empowered citizens enhancingaccountabilityof Edu reform andquLucia Casap|
|Empowered citizens enhancingaccountabilityof Edu reform andquLucia Casap|
|Agricultural Development Support Project<br>Mio Takada|
|Agricultural Development Support Project<br>Mio Takada|
|Agricultural Development Support Project<br>Mio Takada|
|Agricultural Development Support Project<br>Mio Takada|
|Agricultural Development Support Project<br>Mio Takada|
|Agricultural Development Support Project<br>Mio Takada|
|Agricultural Development Support Project<br>Mio Takada|
|Agricultural Development Support Project<br>Mio Takada|
|Agricultural Development Support Project<br>Mio Takada|
|Agricultural Development Support Project<br>Mio Takada|
|Agricultural Development Support Project<br>Mio Takada|
|Agricultural Development Support Project<br>Mio Takada|
|Population and Health Support Project<br>Cedric Ndizeye|
|Population and Health Support Project<br>Cedric Ndizeye|
|Population and Health Support Project<br>Cedric Ndizeye|
|Population and Health Support Project<br>Cedric Ndizeye|
|Population and Health Support Project<br>Cedric Ndizeye|
|Population and Health Support Project<br>Cedric Ndizeye|
|Population and Health Support Project<br>Cedric Ndizeye|
|Population and Health Support Project<br>Cedric Ndizeye|
|Population and Health Support Project<br>Cedric Ndizeye|
|Population and Health Support Project<br>Cedric Ndizeye|
|Philippines Renewable EnergyDevelopment<br>FengLiu|
|Philippines Renewable EnergyDevelopment<br>FengLiu|
|Philippines Renewable EnergyDevelopment<br>FengLiu|
|Philippines Renewable EnergyDevelopment<br>FengLiu|
|Philippines Renewable EnergyDevelopment<br>FengLiu|
|Philippines Renewable EnergyDevelopment<br>FengLiu|
|Support for Development of MIS and Single Registryof BeneficiariesCornelia M. Tesliuc|
|Support for Development of MIS and Single Registryof BeneficiariesCornelia M. Tesliuc|
|Support for Development of MIS and Single Registryof BeneficiariesCornelia M. Tesliuc|
|Jamaica Foundations for Competitiveness and Growth<br>Thomas A. Vis|
|Jamaica Foundations for Competitiveness and Growth<br>Thomas A. Vis|
|Jamaica Foundations for Competitiveness and Growth<br>Thomas A. Vis|
|Jamaica Foundations for Competitiveness and Growth<br>Thomas A. Vis|
|Jamaica Foundations for Competitiveness and Growth<br>Thomas A. Vis|
|Jamaica Foundations for Competitiveness and Growth<br>Thomas A. Vis|
|Jamaica Foundations for Competitiveness and Growth<br>Thomas A. Vis|
|Jamaica Foundations for Competitiveness and Growth<br>Thomas A. Vis|
|Jamaica Foundations for Competitiveness and Growth<br>Thomas A. Vis|
|Jamaica Foundations for Competitiveness and Growth<br>Thomas A. Vis|
|Jamaica Foundations for Competitiveness and Growth<br>Thomas A. Vis|
|Jamaica Foundations for Competitiveness and Growth<br>Thomas A. Vis|
where corresponds to consumption in period 1 for consumer _i_ . ̅̅̅̅̅̅ corresponds to the running variable, the normalized annual accumulated consumption in period 0. That is: (6) ̅̅̅̅̅̅ The treatment variable is a binary indicator of whether individual _i_ in period 0 was assigned the higher unit price. It is determined as: Parameter in (5) captures the average effect of barely surpassing the threshold, and hence having received a substantially larger utility bill, once we flexibly control for the running variable, ̅̅̅̅̅̅ . The intuition behind this approach is that all observable and unobservable variables should evolve smoothly around this threshold, and hence any jump in consumption in period 1 can be attributed to the discontinuous increase in the amount billed. In other words, in a valid RD design, a key assumption is that observations are randomly distributed between the treatment and control group in a local neighborhood of the threshold (Lee and Lemieux [2010]). In our setting, this assumption seems plausible for three reasons. First, as documented above, consumers have highly imperfect knowledge about the price determination mechanism. Second, they tend to be unaware of the location of the relevant threshold. Third, even if they had perfect information about the pricing scheme, it would be difficult and time-consuming – if at all possible – to precisely manipulate gas utilization so as to avoid passing this cutoff. Indeed, this would require obtaining access to gas readings, forecasting factors affecting future demand, and knowing exactly when the billing period ends (i.e. the exact timing of the meter reading).

We focus on behavioral reactions during the period of mid-May to mid-July 2009, as these two winter months account for the bulk of annual gas consumption (about 30% of the total). It would have been possible to examine consumption patterns of these households in later billing cycles. However, beginning in June 2009, public reactions motivated by the increase in gas prices generated a disruption in the normal billing process of the company. This generated differences in the timing of bill issuance for individuals on both sides of the cutoff, making it difficult to disentangle between timing effects and those stemming from the price shock.

Though we would like to compare observations just above and below the threshold, in practice a larger window around the cutoff has to be used to obtain precise estimates. In choosing the width of this window, researchers typically face a trade-off between bias and precision: a wider window provides greater precision but at the expense of expected higher bias. In our setting, because the utility company had records on almost two million consumers, we were able to select a narrow window around the threshold and still have a sizable sample size of 7200 consumers.

8
<u>Table 1: Cyclical Properties of Employment States Mexico and Brazil</u>

|||Mexico<br>|||Brazil<br>||
|---|---|---|---|---|---|---|
|Series|St. Dev|Correlation|Elasticity|St. Dev|Correlation|Elasticity|
|O|0.010|-0.157|-0.059|0.012|-0.123|-0.070|
|U|0.130|-0.878|-4.235|0.109|-0.816|-4.421|
|E|0.008|0.753|0.234|0.011|0.535|0.288|
|S|0.015|-0.415|-0.235|0.016|-0.219|-0.172|
|I|0.019|-0.243|-0.170|0.026|0.030|0.038|
|F|0.019|0.780|0.560|0.016|0.616|0.498|
|U/U+E|0.129|-0.889|-4.263|0.110|-0.817|-4.475|
|F/E|0.013|0.674|0.325|0.010|0.431|0.210|

Notes: The table shows the standard deviation, the correlation with output and the elasticity with respect to output of the logged and HP filter de-trended of different employment statuses for Mexico and Brazil. The series have been smoothed using a 4 quarter moving average to remove high frequency fluctuations. O=Out of the Labour Force, U=Unemployment, E=Employment, S=Informal Self-Employed, I=Informal Salaried, and F=Formal Sector, all as proportions of working age population. Data for Mexico (left panels) is drawn from the quarterly National Urban Labor Survey (ENEU) from 1987:Q1 to 2004:Q4. Data for Brazil (right panels) is drawn from the Monthly Employment Survey (PME), quarterly averaged from 1983:Q1 to 2001:Q2.

40
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_

# **TABLE OF CONTENTS**

|**1. Long Term Economic and Demographic Trends ..................................................... 1**|
|---|
|**2. The Macroeconomic and Social Impact of the Crisis 2002-03................................. 4**|
|The Social Impact ....................................................................................... 6|
|**3. The Role of Cocoa in Cote d’Ivoire’s Economy........................................................ 9**|
|**4. Capital, Labor, And Total Factor Productivity..................................................... 12**|
|Physical Capital Stock ................................................................................................ 12|
|Human Capital ............................................................................................................ 14|
|Total Factor Productivity (TFP).................................................................................. 15|
|_Deterministic Accounting Estimate of TFP_................................................................ 15|
|_Stochastic Regression Model of Factor Productivity_................................................. 16|
|**5. Concluding Remarks ................................................................................................ 17**|
|**ANNEX ONE: Methodological Notes ........................................................................... 20**|
|Estimating the Average Schooling of Workers .......................................................... 20|
|Estimating the Capital Stock: Sensitivity to Initial Capital and Depreciation............ 23|
|Calculating TFP and Productivity Regressions .......................................................... 25|
|_Calculating Total Factor Productivity_........................................................................ 25|
|Comparison With Deterministic TFP Estimates by the World Bank ......................... 26|
|Stochastic Regression Model of Factor Productivity.................................................. 28|
|Table A-3: Output growth as a Function of Capital Accumulation............................ 29|
|Productivity Regression Data ..................................................................................... 33|
|How Terms of Trade Impact TFP............................................................................... 34|
|**ANNEX TWO: Some Comparisons of Cote d’Ivoire’s Growth Record ................... 37**|
|Cross Country Comparisons ....................................................................................... 37|
|Other Developing Countries ....................................................................................... 37|

i
re-interview sample households and their splits after the 2007 survey. Out-migrants were tracked through either direct or phone interviews.

# Figure 1 to be inserted

The 2007 survey was designed to overlap with villages in the 1994/95 PATANAS survey conducted by ICASEPS to build panel data on households. The 1994/95 PATANAS survey mainly focused on agricultural production activities in 48 villages chosen from different agro-climatic zones in seven provinces, though the sample also included households in fishery villages and laborer households. In 2007, we visited those villages to expand the scope of research as a general household survey under the IMDG survey.

In the 2007 round, we also added 51 new villages in the same seven provinces. These new villages were selected using the following criteria. First we chose the same districts where PATANAS villages are located. We listed villages that had received relatively large amounts of government infrastructure projects during the period 1995 to 2005, funded by either the Japan Bank for International Cooperation or the World Bank. Finally, the new villages were randomly sampled from the list.

In the revisited villages in 2007, we re-sampled 20 households per village from the 1994/95 sample (using a proportional sampling based on landholding size) and followed the split households. In the new villages, we sampled 24 households from two main hamlets in each village. Since one of the 48 villages in the 1994/95 PATANAS was not accessible for safety reasons in the 2007 survey (in West Nusa Tenggara province), we have the total of 98 villages that are available for various research objectives. (The 2010 survey followed sample households and their split households in all 98 villages.) Some household members split from the 2007 original households to start their new households or to join other households. In the household-level panel analysis, for households that split between the two rounds of data collection in 2007 and 2010, we aggregated incomes of original household and split households by using the 2007 household unit as the base in the following way. We include new 7
# Analyzing the Effects of U.S. Agricultural Policy on Mexican Agricultural Markets Using the MEXAGMKTS Model

# by Gerald T. O'Mara

# Table of Contents

|I.|Introduction|1|
|---|---|---|
|II.|The Strategic<br>Role of Agriculture<br>in Mexican<br>Econ<br>.zic Policy|2|
|III.|MEXAGMKTS<br>Model Structure|3|
|IV.|The FAIRMODEL|a|
|V.|The USAGMKTS Model|9|
|VI.|Experiments<br>to Estimate<br>the Sensitivity<br>of Prices,<br>Production,<br>and Traded<br>Quantities<br>in Mexican<br>Agricultural<br>Markets<br>to U.S. Agricultural<br>Policy|10|
|Summ|ary<br>of Analysis<br>of Mexican Responses<br>to U.S.<br>Agricultural<br>Policy|22|
|Refere|nces|23|
account for 75 percent of all trading--substantially more than the fraction of shares they own.

If one counts the volume of trade in derivative securities--index futures, options, and over-the-counter synthetic equities markets--then the increase in effective turnover of stocks would be more dramatic than the NYSE turnover figures indicate. Lowenstein (1988) found that the high turnovers impose an exorbitant burden (tax) on the funds that are being churned, and reduced the underlying stream of income by about one-sixth. In fact, the annual stock tumover "tax" roughly equalled the value of the new stocks issued each year for cash, which of course is the primary reason for having a stock market.

The high turnover of institutional portfolios is taken as suggestive that they are more speculative than are individual investors. However, as noted by Shiller (1992), the evidence is far from conclusive about the propensity of institutional investors to engage in speculative behavior. There is a common perception that institutional investors are subject to an incentive system that rewards short-term returns than are individuals, many of whom have long investmnent horizons. However, there does not appear to be any conclusive evidence that institutional investors ignore the long-term profitability of the companies they invest in any more than individual investors when they dominated the trade in the stock market earlier.

Part of the problem is that investment managers are evaluated on too short term a basisand are not often not given adequate time to manage their portfolios well. As noted by Shiller (1992), some corporate clients of portfolio managers reportedly use the 12/24 rule, which is to fire any portfolio manger whose performance is 12 percent under the S&P 500 index for any 24month period.

On the other hand, Jones et al. (1990) argue that the dramatic increase in trading volume 3
Table 5 shows the optimized renewable subsidy rates in the different cases. As mentioned, under CAT no subsidy is warranted, whether or not embodied emissions are priced and whether the subsidy is independent or electricity tax-funded. Nor can an independent renewable subsidy lower costs under a USPS. However, a USPS can benefit from a modest RPS, due to the implicit electricity tax component introduced by the RPS. Both the UEPS and DEPS benefit directly from the independent renewable subsidies, as proper levels of renewable subsidy can address the output subsidy disparities and level the playing field between renewable and fossil-based plants in both systems; implementation with RPS can confer additional benefits.

Optimized subsidy rates vary based on the type of TPS and other overlapping policies. With IEP in place, the allowance price decreases, reducing implicit output subsidies for fossilbased plants and necessitating smaller renewable subsidies to compensate for the distortions. When the renewable support policy is an RPS rather than an independent subsidy, the optimized subsidy level is significantly higher, as higher renewable subsidies under RPS not only provide support for renewables but also raise the implicit electricity tax, enhancing cost-effectiveness. The IEP then allows an even greater reduction in the renewable subsidy, since the improved carbon cost pass-through to consumers means less implicit tax is needed from the RPS.

**Table 5. Optimized Renewable Subsidy Rates under Different ETSs (%)**

|||**Type **<br>|**of ETS**<br>||
|---|---|---|---|---|
|**Overlapping Policies**|**CAT**|**USPS**|**UEPS**|**DEPS**|
|RPS+IEP (R*2)|0.0|2.0|12.0|14.0|
|RPS (R*N)|0.0|2.5|13.5|16.0|
|IRS+IEP (S*2)|0.0|0.0|7.5|9.0|
|IRS (S*N)|0.0|0.0|8.0|10.0|

Note: See Table 3 for the definitions of the overlapping policies. The asterisk (*) represents the cases with optimal renewable subsidy rates. Optimal renewable subsidy rates shown here refer to the renewable subsidy level that leads to the lowest cost per ton over the entire simulation period.

Figure 4 compares the outcomes of cost-minimizing overlapping policies across ETS types and phases.<sup>25</sup> The least-cost policy enabling China to achieve its emission targets is CAT with no overlapping policies.<sup>26</sup> Evidently, if the ETS is a CAT, additional policies are not needed

> 25 A full set of results of all policy cases are provided in Table C2 in Appendix C.

> 26 Although the numerical model includes several features (more sectors, international trade, and general equilibrium effects) not incorporated in the theroretial model, these numerical findings reinforce the theoretical model’s predictions.

39
because the effects of abating global emissions of greenhouse gases on aggregate economic damages necessarily accrue more to higher-income countries. Unlike adaptation, emissions mitigation does not specifically target the poor. The major gains in poverty averted by following the optimal abatement strategy would indeed occur on a longer time horizon, by 2100 and beyond.

**Table 2. Climate change impacts on world poverty (at the $2 a day level)**

||Number|of poor (mi|llions)|Headco|unt poverty r|ate (%)|
|---|---|---|---|---|---|---|
|Scenarios|2005|2055|Change|2005|2055|Change|
|Baseline|2,069.4|1,259.1|(810.3)|32.3|14.1|(18.2)|
|BAU|2,069.4|1,269.2|(800.2)|32.3|14.2|(18.1)|
|Difference from baseline|..|10.1|10.1|..|0.12|0.12|
|Optimal abatement|2,069.4|1,268.5|(800.9)|32.3|14.2|(18.1)|
|Difference from BAU|..|(0.7)|(0.7)|..|(0.01)|(0.01)|

Source: authors own estimates.

Even though aggregate impacts on poverty seem to be modest by mid-century it does not imply that the impacts will be equally distributed among the population. To analyze how climate change will affect specific sectors of the population one needs to use household-level data and explicitly model the channels through which future warming will affect economic activity.

# **3. INTRODUCING HETEROGENEITY: MICROECONOMIC APPROACH**

The discussion so far has relied on the evidence emerging from the relationship between climate (temperature and precipitation) and growth or the level of GDP in a cross section or a panel of countries or municipalities within selected countries. While informative, these studies do not shed any light on the channels through which climate change can impact household welfare. For example, climate change may have a negative effect on agricultural productivity and also affect poor people’s livelihood through its effects on health, access to water and natural resources, and infrastructure. Considering the complexities involved in modeling some of these channels, the literature has largely focused on the impacts on agricultural output. This section 10