rafmacalaba/datause-encoder-data huggingface dataset:
A problem for estimation in this context is the variable length of comparison periods imposed by variable park establishment dates and fixed bounding dates in the Hansen deforestation data. To accommodate this, we perform successive experiments for parks with different start dates. We are not likely to produce robust results in such quasi-experiments with very short observation periods either before or after establishment dates. Accordingly, we adopt two years as the minimum standard and perform successive estimations for parks with start dates from 2002 to 2009. We estimate the panel by fixed effects, which enables us to focus on the time series result of interest. Since we do not need multivariate controls in this case, our statistical approach is simple and straightforward. For each park, we define a dummy variable whose value is 0 for the period from 2000 to its year of establishment, and 1 for later periods. For example, the test variable for a park established in 2004 is 0 for 2001-2004 and 1 for 2005-2012. Our regressions use the dummy variable to test whether the outside/inside ratio is significantly different after the park has been established. We also use annual dummy variables for 2002-2012 to control for general fluctuations that might confound the measured effect of park establishment (the first year is excluded to avoid perfect collinearity).

Table 4 presents results for rolling samples in Africa, LAC and Asia/Pacific. For each region, each row presents results for parks established in the base year or later. The table includes four columns for each variable: the number of observations used for estimation; the estimated log of the outside/inside ratio prior to park establishment; the estimated impact of park establishment; and the outside/inside ratio prior to park establishment (this is the antilog of column 2, included to show typical magnitudes of outside/inside ratios across regions and over time).

Our estimates for outside/inside ratios are quite strong and consistent, both across regions and for different temporal samples within regions. The results in the second and fourth columns for each 16
Table 4: Contribution of Changes in Distortions in Explaining Growth (%)

||N<br>(1)|Fraction for which Implied Growth is<br>Postive Share of Actual Growth (%)<br>(2)|
|---|---|---|
|1980s – 1990s|24|63|
|1980s – 2000s|25|64|
|1980s – 2010s|22|64|
|1990s – 2000s|64|48|
|1990s – 2010s|55|64|
|2000s – 2010s|71|61|

Table 5: Contribution of Changes in Distortions to Growth: Changing Labor Shares (%)

|||Pe|rcenta|ge of Ac|tual Gro|wth Ac|counted(|Actual G|rowth|≥10%)|
|---|---|---|---|---|---|---|---|---|---|---|
|||Be|nchma|rk|Propo<br>in<br>(Decen|rtionate<br>Labor S<br>nial Decl|Change<br>hares<br>ine = 8%)|Dispro<br>in|portion<br>Labor|ate Change<br>Shares|
|||Pe|rcenti|le||Percenti|le||Percen|tile|
|Period|N|10|50|90|10|50|90|10|50|90|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|1980s – 1990s|16|-34.4|1.2|7.4|-40.8|-7.1|3.8|-29.7|0.8|9.2|
|1980s – 2000s|22|-20.5|0.4|13.3|-30.2|-5.3|2.6|-8.3|0.5|30.1|
|1980s – 2010s|20|-14.0|1.4|9.2|-47.3|-6.5|9.0|-5.7|3.9|17.2|
|1990s – 2000s|57|-22.5|0.1|12.0|-28.7|-3.9|7.1|-14.8|-0.2|22.2|
|1990s – 2010s|50|-19.4|1.2|11.5|-24.2|-4.2|10.9|-14.0|1.1|14.9|
|2000s – 2010s|33|-22.3|1.3|22.2|-28.2|-2.4|16.7|-16.5|2.4|31.6|

Statistics populated from those country-periods in which the actual growth was greater than or equal to 10%.

43
communities are likely to help mitigate the impacts of the shocks. In light of these findings, these policy instruments should be given due consideration in the design and implementation of adaptation strategies.

# **5. Concluding Remarks**

Very little empirical evidence exists on the welfare losses that households experience as a consequence of weather shocks. In principle, households at low levels of income are most vulnerable to the impacts of weather extremes given their geographical locations, limited assets and access to resources and services, low human capital and high dependence upon natural resources for income and consumption. While there is wide recognition of the impending threat of climate change upon the poor, limited attention is given to quantifying such effects of climate change and identifying household _adaptation_ strategies and targeted measures that could mitigate the poverty impacts. This paper seeks to make a contribution by analyzing the potential welfare impacts of rainfall shocks in rural Indonesia with a focus on households engaged in family farm businesses, in particular rice farming. It also attempts to identify community characteristics capable of dampening the adverse impact of climate change and extremes. The focus on rice farming is due to the fact that rice is a staple food in Indonesia.

The basic approach adopted here is to exploit cross-sectional variation in the data and link a welfare indicator (i.e. real consumption per capita) or some component thereof (i.e. food versus non-food expenditure) to a weather shock defined on the basis of available rainfall data focusing mainly on rural households. In particular, we consider two types of shocks: delayed onset of monsoon and rain shortfall in the 90 day period following monsoon onset. We find that delay in the monsoon onset does not have a significant impact on the welfare of rural households. However, rice farm households located in areas experiencing low rainfall following the monsoon onset are negatively affected by the low rainfall shock. Nonfood expenditure per capita is the most affected component. This suggests that rice farm households protect their food expenditure in the face of weather shocks. Further study is needed to better understand these choices and their implications for adaptation strategies.

We use propensity score matching to identify potential policy instruments that might moderate the welfare impact of climate change and extremes. Our results indicate that credit availability, the existence of safety nets and community-based programs offer the strongest 17
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*It refers to the cost of sending $300 dollars through Western Union, MoneyGram and Citibank in February 2005. Sources: Western Union’s web site (www.westernunion.com), MoneyGram’s website (www.moneygram.com) and Citibank’s website (web.dataus.citibank.com)

16
**<u>Table 1. The main outcome variables</u>**

||||**Treatment**|||||**Control**|||
|---|---|---|---|---|---|---|---|---|---|---|
|Variables|Year<br>2004|Year<br>2008|Year<br>2012|Year<br>2016|Year<br>2020|Year<br>2004|Year<br>2008|Year<br>2012|Year<br>2016|Year<br>2020|
|_Individual employment_|||||||||||
|Currently working (%)|88.98|88.96|91.19|90.29|91.54|83.47|82.50|86.73|86.89|87.37|
||(0.96)|(0.90)|(0.63)|(0.63)|(0.63)|(0.69)|(0.86)|(0.82)|(0.68)|(0.61)|
|Self-employed farm work (%)|81.89|80.43|65.55|62.79|48.49|68.62|66.29|54.14|53.67|42.78|
||(1.57)|(1.55)|(2.21)|(2.27)|(2.68)|(1.58)|(1.85)|(1.67)|(1.52)|(1.88)|
|Self-employed non-farm work|1.62|2.49|2.27|2.78|9.73|5.33|5.72|5.00|4.02|9.97|
|(%)|(0.31)|(0.57)|(0.36)|(0.45)|(1.81)|(0.70)|(0.77)|(0.62)|(0.50)|(1.22)|
|Having wage job (%)|5.47|6.04|23.37|24.73|33.32|9.52|10.49|27.59|29.20|34.61|
||(0.79)|(0.75)|(2.14)|(1.95)|(2.31)|(0.82)|(0.89)|(1.40)|(1.37)|(1.52)|
|Monthly wage (thousand VND)|2.20|2.42|2.45|3.33|4.18|2.16|2.94|2.66|3.40|2.96|
||(0.12)|(0.16)|(0.14)|(0.21)|(0.24)|(0.08)|(0.11)|(0.10)|(0.14)|(0.19)|
|_Per capita income from different so_|_urces (milli_|_on VND)_|||||||||
|Per capita income|6.86|8.94|10.95|13.33|21.91|10.18|13.78|16.36|20.37|25.47|
||(0.28)|(0.40)|(0.48)|(0.76)|(1.28)|(0.40)|(0.45)|(0.69)|(0.80)|(1.02)|
|Per capita income from wages|1.23|1.96|3.56|4.65|10.46|1.89|2.62|5.19|6.70|9.78|
||(0.15)|(0.20)|(0.36)|(0.42)|(0.86)|(0.19)|(0.20)|(0.37)|(0.36)|(0.51)|
|Per capita income from nonfarm|0.39|0.56|0.73|1.26|2.42|1.19|1.62|1.74|1.71|2.97|
|production|(0.06)|(0.09)|(0.15)|(0.26)|(0.61)|(0.18)|(0.22)|(0.23)|(0.21)|(0.33)|
|Per capita income from farm|4.69|5.42|5.73|6.09|6.83|5.60|7.05|7.89|9.51|10.06|
|production|(0.19)|(0.25)|(0.22)|(0.39)|(0.57)|(0.24)|(0.28)|(0.44)|(0.58)|(0.71)|
|Per capita remittances from|0.22|0.44|0.33|0.66|1.46|0.68|1.17|0.79|1.48|1.89|
|<br>private sources|(0.02)|(0.08)|(0.05)|(0.15)|(0.30)|(0.10)|(0.14)|(0.11)|(0.17)|(0.19)|
|Per capita public cash transfers|0.08|0.26|0.28|0.33|0.30|0.13|0.29|0.43|0.43|0.38|
||(0.02)|(0.06)|(0.05)|(0.07)|(0.06)|(0.02)|(0.06)|(0.06)|(0.06)|(0.05)|
|Per capita income from other|0.25|0.30|0.31|0.34|0.44|0.69|1.03|0.33|0.53|0.40|
|sources|(0.05)|(0.05)|(0.05)|(0.04)|(0.06)|(0.09)|(0.12)|(0.05)|(0.08)|(0.05)|
|The income poverty rate (%)|76.42|61.25|57.64|43.63|16.85|57.99|34.95|33.00|22.28|10.65|
||(2.45)|(3.16)|(2.57)|(3.13)|(2.09)|(1.97)|(2.16)|(1.83)|(1.80)|(1.27)|

Note: The sample is limited to households residing in districts with a poverty rate exceeding 40% in 2006. Within this sample, households living in the 30A districts are referred to as the treatment group, while households residing in districts without the 30A Programs but having the 2006 poverty rate above 40% are considered the control group.

The income variables are adjusted to the 2020 price using overall CPI. Standard errors of the means in parentheses.

40
The purpose of this paper is to analyze the potential welfare impacts of rainfall shocks in rural Indonesia, and to draw relevant policy lessons. With an estimated population of 237.5 million, Indonesia is the largest archipelago and the fourth most populous nation in the world. Located in Southeastern Asia between the Indian and the Pacific Oceans, the country has a tropical climate with two distinct seasons, monsoon wet and dry, and is endowed with high levels of biodiversity. The country has been experiencing change in both mean _temperature_ and _precipitation_ . Since 1900, it is estimated that the annual mean temperature has increased about 0.3<sup>o</sup> C. 1998 was the warmest year in the century as the temperature rose 1<sup>o</sup> C above the 19611990 average (PEACE 2007). The increase in average temperature is projected to lie between 0.36 and 0.47<sup>o</sup> C by the year 2020. It is reported that overall annual precipitation has decreased by 2 to 3 percent, but there are significant regional differences (WWF 2007). Southern regions such as Java, Lampung, South Sumatra, South Sulawesi, and Nusa Tenggara have seen _a decline in annual rainfall_ . Northern regions on the other hand have experienced an increase in precipitation. These include most of Kalimantan and North Sulawesi. These changes in precipitation are strongly influenced by El Niño Southern Oscillation (ENSO). Indonesia tends to experience droughts during the warm phase of ENSO (i.e. El Niño) and excessive rain in the cool phase (i.e. La Niña). With the possible exception of southern Indonesia annual rainfall is expected to increase across the rest of the country (Naylor et al. 2002).

These observed and expected changes in climate are bound to have adverse impacts on the ecosystems, the associated resources and the lives of people who rely on these resources and on agricultural activities. _The 1997-1998 droughts associated with El Niño led to massive crop failures, water shortages and forest fires in parts of Indonesia, and likely exacerbated the impacts of the financial crisis at that time_ . El Niño events tend to delay rainfall, leading to a decrease in rice planting in the main rice-growing regions in Indonesia such as Java and Bali. Adapting projections by the IPCC to local conditions, Naylor et al (2007) predict that by 2050 change in the mean climate will increase the probability of a 30-day delay in monsoon from 9-18 percent currently to 30-40 percent. This delay combined with increased temperature could reduce the yield of rice and soybean by as much as 10 percent. Our analysis considers the welfare implications of both a late monsoon onset and low level of rainfall. As we note later, a certain amount of rainfall is needed in the 90 day post-onset for rice to grow properly.

3
the overall trade restrictiveness is marginal in our sample.

# 7 Concluding Remarks

A set of consistently estimated import demand elasticities at the tariff line level is critical to understand the consequences of trade liberalization. This paper is the fi rst attempt to provide a theoretically consistent methodology to estimate import demand elasticities for a wide variety of countries at the six digit level of the HS (4,625 tariff lines). Moreover, the methodology can be easily implemented with existing trade data.

We fi nd that the sample average import demand elasticities is -1.67, while the sample median is -1.08. There is wide variation in import demand elasticities across countries and tariff lines, and the estimates exhibit some interesting patterns. First, homogeneous goods have more elastic import demand than differentiated goods. Second, the average estimated elasticities decrease as we increase the level of aggregation at which they are estimated. Third, large countries tend to have more elastic import demands. Fourth, more developed countries tend to have less elastic import demand.

In addition, we calculate trade restrictiveness indices (TRIs) and welfare losses associated with the existing tariff structures for 88 countries. We use a methodology that is both theoretically sound, and more importantly consistent with our estimated elasticities. Results show that both simple and weighted average tariffs tend to underestimate the distortion imposed by the tariff regime by 30 percent on average. Because of the large variance in the United States tariff schedule, underestimation of trade restrictiveness is among the largest. The simple and import-weighted tariffs in the United States are around 4 percent, whereas the TRI is close to 15 percent. With welfare losses at around $7 billion per year, the United States is the country in our sample whose tariff structure imposes the largest welfare costs on its nationals. It is followed by China, Mexico, India and Germany. Finally, given the high TRI of the US, the $7 billion per year deadweight loss estimate is also larger then previous fi ndings of the literature which mainly use average tariffs.

21
managed by the same task manager, excluding the project in question ( _TMQUAL_ ). To capture country effects, we consider, as before, the average over the life of the project of the country-level CPIA score.

We first regress project outcomes on task manager quality and a full set of country dummies (column (3)). Task manager quality is again highly significant, indicating that even within countries, projects associated with higher quality task managers are more likely to be rated as satisfactory. In Column (4) we ask the symmetric question, regressing project outcomes on the CPIA score and a full set of task manager dummies. The CPIA is also highly significant, indicating that the projects of a given task manager are more likely to be rated as satisfactory in countries with good CPIA scores.

Finally, we include both CPIA scores and task manager quality, and find that both are very significant predictors of project success. The relative magnitude of these effects is interesting. The standard deviation of _TMQUAL_ in the sample is 0.77, and so a one-standard-deviation improvement in task manager quality leads to an improvement in the project outcome rating of 0.26 (on a six-point scale, or about 0.2 standard deviations of the outcome measure). For _CPIAAV_ the standard deviation is 0.59, and so a one-standard-deviation improvement in country-level policy performance implies an improvement of 0.25 in the project outcome measure. This suggests that the magnitude of the standardized impact of task manager quality and policy quality in the recipient country on project outcomes is roughly the same.

# **6.  Interpretation, Implications, and Conclusions**

In this paper, we analyze the correlates of project outcomes for a very large sample of World Bank projects since the early 1980s. In our analysis, we have distinguished between country-level correlates of country-average project performance, and project-level correlates of project-level variation in project outcomes _within_ countries. This distinction is important as the within-country share of the variation in project outcomes is large, at around 80 percent. Consistent with existing literature, we find that country-level variables, most notably the CPIA measure of policy and institutional quality, are robust partial correlates of project performance. This basic finding underscores the importance of continued country-level selectivity in aid allocation. In the case of projects funded by IDA, these results can be seen as a validation of IDA’s Performance Based Allocation system which emphasizes "macro" country-level measures of policy and institutional quality in determining the cross-country allocation of aid.

22
**Table 2. Demand Scenarios for the ZMCM in 2010**

||**Number**<br>**of**<br>**consumers**|**% change**<br>**from base**<br>**scenario**|**Volume**<br>**(MMCMY)**|<br>**% change**<br>**from base**<br>**scenario**|
|---|---|---|---|---|
|1. Base scenario|1,965,526||6,980||
|2. Fast population growth<sup>a</sup>|2,635,320|34.1|7,131|2.2|
|3. Fast economic growth<sup>b</sup>|1,965,526|0.0|7,415|6.2|
|4. Economic stagnation<sup>c</sup>|1,965,526|0.0|6,437|–7.8|
|5. 20 percent decrease in national gas prices|1,992,651|1.4|7,890|13.0|
|6. 20 percent decrease in liquid petroleum gas prices|1,917,140|–2.5|6,932|–0.7|
|7. 20 percent decrease in electric tariffs|1,894,425|–3.6|6,379|–8.6|
|8. 20 percent increase in natural gas prices|1,939,496|–1.3|6.230|–10.7|
|9. 20 percent increase in liquid petroleum gas prices|2,008,602|2.2|7,025|0.6|
|10. 20 percent increase in electric tariffs|2,051,169|4.4|7,617|9.1|
|11. Constant fuel oil prices|1,965,526|0.0|5,691|–18.5|
|12. Moderate increase in fuel oil prices|1,965,526|0.0|6.357|–8.9|
|13. Convergence of Mexican and U.S. capitalprices|1,965,526|0.0|6,134|–12.1|

a. 25 percent more than in the base scenario. b. 1 percent more than in the base scenario.

c. Zero growth rate.

This comparative analysis indicated that the demand model provided a reasonable explanation of natural gas demand in Mexico. Furthermore, the base scenario could be used with confidence because the inelastic behavior of demand suggests that results from the demand equation are not so sensitive to measurement precision in the explanatory variables.

In parallel, demand was estimated using criteria for the technical and market conditions of the ZMCM. This scenario, termed the optimistic scenario predicts higher average daily consumption, more consumers, and lower average costs for the ZMCM (tables A7 and A8). The discrepancy comes from the base scenario’s assumption that a certain proportion of residential consumers can opt to use liquid petroleum gas instead of natural gas. Likewise, the average consumption of 1.85 cubic meters a day in the base scenario is obtained from the ratio of total volume to consumers. The optimistic scenario projects the number of residential consumers first according to demographic and engineering data, and proposes an expected consumption of 2.0 cubic meters a day. This is multiplied by the number of consumers to calculate total volume.

The calculation of demand by consumer class was used to estimate natural gas distribution costs for several hypothetical symmetric and non-symmetric partitions of the ZMCM. In the symmetric case the ZMCM was divided into five distribution franchise zones with the same number of clients, the same volume of delivered gas, and the same amount of urban territory. This was deemed unrealistic because it assumed that load and all other variables are uniformly distributed. The next step was to account for heterogeneity in population density and load characteristics by using non-symmetric partitions with a roughly balanced distribution of different types of consumers in each zone (figure 3).

12