rafmacalaba/datause-encoder-data huggingface dataset:
economic incentives created by each of the five policy designs. We find that, for a given carbon price, abatement-based rebating and the two intensity-based variants lead to additional reductions in emission intensity, as compared with lump-sum or output-based rebating. If domestic politics or competitiveness concerns constrain carbon prices to below the social cost of carbon, these policy variants may be helpful in increasing the environmental effectiveness of a carbon price with potentially beneficial welfare implications. In contrast to abatement-based rebating, which further discourages output by raising the opportunity cost of emissions, the two forms of intensity-based rebating offer additional support for output, similar to output-based rebating. Thus, intensity-based rebating, like output-based rebating, also helps address concerns over structural frictions associated with output losses of emission-intensive industries. We use our model to rank each of the five policy variants according to how well they protect against reductions in firm output and how much incentive they provide for reductions in emissions. Our ranking suggests that by both protecting output and increasing emission reductions achieved at a given carbon price, intensity-based rebating may be useful in addressing key concerns that prevent wider adoption of effective carbon pricing.<sup>8</sup> In practice, rebating mechanisms are typically reserved for the sectors that are least able to pass on their carbon costs: emission-intensive industries that are also highly trade-exposed. Thus, in addition to comparing the policies in a partial-equilibrium analytical setting with one sector only, we also implement each policy approach in a multisector computable general equilibrium (CGE) model. The CGE model not only relaxes dimensionality restrictions but also allows us to account for important market interactions via economy-wide substitution and income effects. Furthermore, the CGE setting opens up for a broader welfare analysis where we can trade off economy-wide efficiency impacts of alternative rebating schemes with sector-specific implications. Using the numerical model parameterized to US data, we find important quantitative differences between the policy approaches. Notably, with the same carbon price, abatement- and intensity-based rebating policies lead to two to three times

> 8A caveat, which we note in a later section of the paper, is that when firms are heterogeneous, IBER does entail additional distortions, due to the mechanism of leveraging emissions payments to incentivize additional reductions. Firms with lower baseline emissions payments get smaller rebates, and firms with higher-than-average baseline emission intensity have a harder time meeting the rebate criteria. As a result, marginal abatement costs will not be equalized across firms.

5
Contraction in irrigated land activities in the West leads to a release of labor from these activities. The amount of labor release is 1.25 percent in tobacco production, followed by 1.69 percent in the production of cotton. Although there is a drop in labor demand in irrigated land activities in the West, alternative activities compete for the labor released from irrigated activities as they experience an increase in labor demand. In fact, we see a slight increase in the wages paid to labor by 1.1 percent as a result of the decrease in irrigation water in the West region. The price of rainfed land rent also increases by 3.7 percent in the West Region. Since water is used only by irrigated activities, a decline in the, water supply accompanied by a drastic increase in water prices, forces the area allocated to irrigated activities to contract. This decreases the price of irrigated land. Furthermore, the price of rainfed land increases because rainfed agriculture enjoys the increase in prices without bearing any cost for the ever increasing water prices. The inter-sectoral transfer of water raises drastically the shadow value of water for irrigation by 70 percent. This adjustment can also be interpreted as a domestic terms of trade effect between irrigated and rainfed land resources (Table 4.11).

**Table 4.11. Change in factor prices**

|||**Base**|**% C**|**hange**|
|---|---|---|---|---|
||**Region**|**Level**|||
|||**(TRY)**|**2.a**|**2.b**|
|**Labor**||1.00|1.10|1.18|
||**West**|0.27|3.69|6.65|
||**Central**|0.11|3.30|5.86|
|**Rainfed Land**|**East**|0.11|3.08|5.45|
||**Southeast**|0.23|2.84|4.97|
||**LSCB**|0.14|4.03|7.32|
||**West**|0.64|-9.59|-19.06|
||**Central**|0.26|2.79|4.89|
|**Irrigated Land**|**East**|0.27|2.93|5.14|
||**Southeast**|0.53|2.13|3.78|
||**LSCB**|0.70|2.60|4.61|
||**West**|0.13|69.64|160.81|
||**Central**|0.11|4.37|7.63|
|**Water**|**East**|0.05|4.48|7.87|
||**Southeast**|0.09|2.65|4.88|
||**LSCB**|0.13|3.32|5.96|
||**Urban**|0.81|-47.62|-60.98|

_Source: Results from TACOGEM-W._ 47
# _4.1. The FLS Data_

We first illustrate the effects of our prior choices using the growth data of FLS. The latter data set contains _k_ = 41 potential regressors to model the average per capita GDP growth over 1960-1992 for a sample of _n_ = 72 countries.<sup>5</sup> As expected, results with _m_ = 41 _/_ 2 = 20 _._ 5, fixed _θ_ and _g_ = 1 _/k_<sup>2</sup> (the FLS settings) are virtually identical to those obtained in FLS on the basis of a chain of the same length. Figure 5 provides a complete picture of the posterior model size distributions (overplotted with the priors) for all eight combinations of prior settings. Summaries in the form of the first two moments are provided in ~~Table 1.~~ <!-- Start of picture text --> Fixed q, m = 7 Fixed q, m = 20.5<br>0.3<br>0.25 g= k 12 g= 1 n 0.2 g= k 12 g= 1 n<br>0.2 0.15<br>0.15<br>0.1<br>0.1<br>0.05<br>0.05<br>Prior<br>Prior<br>0 0<br>0 10 20 30 40 0 10 20 30 40<br>Numberof Regressors Numberof Regressors<br>Random q, m = 7 Random q, m = 20.5<br>0.25 0.25<br>g= 1<br>k 2<br>0.2 g= 1 g= 1 0.2<br>k 2 n<br>g= 1<br>0.15 0.15 n<br>0.1 0.1<br>0.05 0.05 Prior<br>Prior<br>0 0<br>0 10 20 30 40 0 10 20 30 40<br>Numberof Regressors Numberof Regressors<br>ytis ytis<br>n n<br>e e<br>D D<br>ytilib ytilib<br>a a<br>b b<br>Pro Pro<br>ytis ytis<br>n n<br>e e<br>D D<br>ytilib ytilib<br>a a<br>b b<br>Pro Pro<br><!-- End of picture text -->

**Fig. 5.** Model Size: Prior and Posterior Distributions for FLS data

From these results, we immediately notice the striking influence that prior assumptions have on model size in the posterior. Even <mark>if we fx the prior m</mark> ean model size, simply changing between random and fixed _θ_ or choosing a different _g_ can substantially alter the posterior distribution of model size, _W_ . Clearly, influence of the choice of _m_ is massive for fixed _θ_ , whereas its effect is much less severe for the case of random _θ_ . This accords with the fact (discussed in Subsection 3.1)

> 5 The dataset and the code used in FLS are available on the _JAE_ website at http://qed.econ.queensu.ca/jae/.

11
To evaluate the reliability of the estimates, the ecozone-level CDHS prevalence of stunting and underweight were compared with the ecozone-level estimates in this study. Table 3 summarizes these results. The differences between the CDHS estimates and this study are within two standard errors of the CDHS estimates, suggesting that the differences can be attributed to the random errors. It should also be noted from Table 3 that the standard errors estimated with the CDHS alone are higher than those for this study except for the underweight model in Tonlesap. The standard errors from Coastal ecozone in the survey are quite high, and our methodology improved the ecozone level estimates quite substantially. We also arrive at the same conclusion when comparisons are made at the level of 17 strata.

We also checked the magnitude of standard errors associated with commune-level estimates of malnutrition prevalence. The simple means of the standard errors are 9.0 percent for stunting and 10.1 percent for underweight, while the simple means of the coefficient of variation are 3.8 percent for stunting and 4.2 percent for underweight. They are reasonably small as they are about the same magnitude as the standard errors in the survey at the ecozone level. We should note, however, that there are communes for which the standard errors are quite high, as the maximum standard error was 22.7 percent for stunting and 18.5 percent for underweight. Overall, the accuracy of commune-level estimates for stunting and underweight is about the same. It should be noted that, as the number of communes included in a nutrition program increases, the idiosyncratic component of the error tends to decrease. Hence, if a proposed nutrition intervention delivers assistance to a relatively large number of communes, high levels of standard errors for each commune are not necessarily worrisome.

Using a GIS, we converted the commune-level estimates into maps. Figures 1 and 2 are the maps of stunting and underweight at the commune level. These maps show the point estimates of the prevalence of malnutrition as of the census year 1998 at the commune level, and the orange and red areas represent bad situations.

As shown in Figure 3(a), stunting and underweight exhibit by and large similar patterns, 16
> <sup>29</sup>

**Table 4:  How much more likely are respondents from clustered ethnic groups to express a partisan preference?**

**<u>(Logit estimation; coefficients are odds ratios; robust z-statistics in parentheses)</u>**

|Dependent variable:<br>Does respondent<br>Cluster<br>rule:|Lax: many<br>parties/|Lax, 25%<br>threshold|One<br>group,|One<br>party,|Monogamous:<br>one party, one|
|---|---|---|---|---|---|
|express a partisan<br>|groups||many<br>|many|group|
|preference?(0-1)|||parties|groups||
|Bivariate regression: Respondent|1.62|1.30|1.18|1.10|1.15|
|ethnicgroupclustered?(0,1)|(7.89)|(6.78)|(3.54)|(2.62)|(2.90)|
|Respondent ethnic group clustered?|1.564|1.248|1.181|1.096|1.130|
|(0,1)|(6.61)|(5.25)|(3.00)|(2.28)|(2.22)|
|Respondent offered a gift before|1.365|1.371|1.354|1.357|1.351|
|last election? (0,1)|(6.74)|(6.83)|(6.56)|(6.62)|(6.52)|
|Do politicians usually make gifts?|0.844|0.843|0.846|0.847|0.846|
|(0,1)|(-3.90)|(-3.92)|(-3.84)|(-3.82)|(-3.83)|
|Do politicians usually keep their|1.403|1.403|1.400|1.402|1.401|
|promises? (0,1)|(6.70)|(6.72)|(6.67)|(6.70)|(6.68)|
|Favors democracy (0-1)|1.363|1.357|1.363|1.361|1.363|
||(8.21)|(8.10)|(8.21)|(8.18)|(8.21)|
|Approves incumbent performance,|1.236|1.241|1.239|1.239|1.239|
|jobs (0-1)|(4.68)|(4.77)|(4.75)|(4.75)|(4.75)|
|Approves incumbent performance,|1.147|1.157|1.155|1.154|1.153|
|economy (0-1)|(3.37)|(3.59)|(3.55)|(3.52)|(3.51)|
|Fraction of seven services they|0.739|0.730|0.737|0.734|0.736|
|receive(0-1)|(-4.40)|(-4.58)|(-4.45)|(-4.50)|(-4.47)|
|Fraction of five assets they|1.666|1.667|1.672|1.662|1.670|
|possess(0-1)|(5.63)|(5.64)|(5.67)|(5.61)|(5.66)|
|Highest level of education? (0, none|1.009|1.006|1.007|1.007|1.007|
|- 9, post-graduate)|(0.78)|(0.53)|(0.59)|(0.64)|(0.62)|
|Observations|16017|16017|16017|16017|16017|
|Pseudo R-squared|0.10|0.0986|0.0977|0.0975|0.0975|

**Note** :  Country fixed effects and other controls not reported.  Students, those working in the home, those who never had a job and those with no religion are significantly less likely to express a partisan preference.  Younger respondents, men, Catholics, evangelical Protestants, Sunni Muslims and other Muslims (not Shiite) are significantly more likely.  These controls are insignificant:  population share of ethnic group; whether respondent expresses an ethnic grievance; whether respondent believes individual is more responsible for own welfare than government; respondent’s level of education; whether respondent belongs to a mainline Protestant church or is Christian with no specific sectarian affiliation; frequency of attendance at church services; and whether respondent is a subsistence or peasant farmer or vendor/hawker.

Respondents who believe politicians usually keep their promises are significantly more likely to exhibit a partisan preference. However, only 17 percent of respondents
data. Overall, the NEDUM-2D model is very robust as it relies on very simple basic economic behaviors that are likely to remain true both through space and time. As a consequence the calibration process is mostly done through refining the data that are used to inform the model. However, a very limited number of parameters can then be marginally modified so as to minimize the discrepancies between observations and model results.

**Map 1 : The simulated and real urban area of the GBA+ region in 2012. The green shade corresponds to the simulation whilst the black is geographic data. The dark green shade shows areas where the model accurately predicts urbanization. Light green shows areas where the model inaccurately predicts urbanization. Black stands for areas where the model fails to reproduce urbanization. Finally where the map is yellow, there is no urbanization and the model agrees with the data.**

It can be seen from Map 1 that the simulated and the actual urban area in 2012 coincide quite well within the GBA+ region (which appears in light yellow). The model captures the general size of urbanized area but also its shape and specific urbanization directions along the transportation network. There are however some discrepancies even within the GBA+ boundaries. In particular it can be seen that the map misses some areas towards the North and the North West of the GBA+ region. This can mainly be explained either by the existence of local secondary employment centers that attract settlements or the presence of local amenities.

The shape and size is one among other features of an urban area. Below we compare data with simulations for the distribution of land values and population densities.

17
**Table 1: Potential supply of land for rainfed cultivation (in million hectares)**

|**Region**|**Total area**|**Area close to market**<br>**(<6 h travel time)**|**Area far from market**<br>**(>6 h travel time)**|
|---|---|---|---|
|Sub-Saharan Africa|201.5|94.9|106.6|
|Latin America and the Caribbean|123.3|93.9|29.4|
|Eastern Europe and Central Asia|52.4|43.7|8.7|
|East and South Asia|14.3|3.3|11.0|
|Middle East and North Africa|3.0|2.6|0.4|
|Rest of World|51.0|24.6|26.4|
|**Total**|**445.6**|**263.1**|**182.5**|

_Source:_ Deininger _et al._ 2010. _Note:_ The figures are for currently uncultivated land that is suitable for cultivation of at least one of five key crops (wheat, sugarcane, oil palm, maize and soybean), excluding forests and protected areas, and with a population density of less than 25 people per km<sup>2</sup> (.25 people per hectare) 18
15 constitutes about half the investment expenditures. For simplicity, it is assumed that farmers self-finance (or borrow from informal sources) 65 percent of the required production credit need. Production credit required from institutional sources may now be calculated as half of the required investment credit or 1.5 percent of the agricultural GDP. For projected 1995-96 agricultural GDP of Rs. 449.1 billion, this is calculated to be<sup>Rs. 6.74</sup> billion.

Total needed from institutional sources for 1995-96 is a simple addition of the estimated investment and production credit need of Rs. 20.21 billion.

How robust are these estimates of required institutional credit? One way to check this is to compare the estimates with the actual lending done by the institutional sources in the recent past (see also Box 2.1). Table 2.2 and 2.3 and the accompanying figure show the annual agricultural growth rate an the associated disbursed institutions agricultural credit as a share of agricultural GDP between 1989-90 and 1994-95. Ignoring 1992-93 when agricultural GDP feel sharply due to monsoon flooding, the average annual growth rate of agriculture was 5.05 percent and average annual disbursed credit as a percentage of agricultural GDP was 5.54. The recent actual experience shows that 1 percent agricultural GDP growth is associated with institutional credit to agricultural GDP rate of 1.12 percent. This report estimates (Table 2.1) that 4 percent growth rate would require institutional credit/agricultural GDP rate of 3 percent (under the assumption that 5 percentage points of investment is financed through non-formal sources). The two estimates are close.

Table 2.2 Institutional Credit Disbursement (Rs. million)

||ADBP|Toccavi|Cooperatives|Commercial<br>Banks|TOTAL|
|---|---|---|---|---|---|
|1980/81|1,0066.6|8.6|1,128.3|1,816.1|4,019.6|
|1989/90|9,389.9|35.6|815.1|3,629.5|13,890.1|
|1990/91|8,323.9|56.3|3,017.5|3,517.6|14,914.8|
|1991/92|6,996.4|56.3|3,247.0|4,179.6|14,479.3|
|1992/93|8,643.4|50.8|2,978.0|4,526.5|16,198.1|
|1993/94|8,989.3|---|2,621.5|4,063.3|15,647.1<br>|
|1994/95|8,293.9|-|2,076.3|2,849.18|13,849.4|

Source: State Bank of Pakistan
|**Annex 5. Transfers in constant 20**|**07 prices, tr**<br>|**illion**<br>|**Rupia**<br>|**h**<br>|||||
|---|---|---|---|---|---|---|---|---|
||**2000 2001**|**2002 **|**2003 **|**2004**|**2005 **|**2006**|**2007**|**2008***|
|Total Transfer to Regions|129 136|156|181|180|183|241|253|261|
|Revenue Sharing|34|40|47|51|60|69|63|69|
|General Allocation funds (DAU)|102|110|116|114|108|155|165|160|
|Special allocation funds (DAK)|1|1|4|6|6|12|16|19|
|Special Autonomyand Adj. Funds|0|6|14|10|9|4|9|12|

*Revised budget (APBN-P).

Note: deflated with implicit GDP deflator. Projected 2008 GDP deflator growth of 12 percent. _Source: World Bank calculations based on Ministry of Finance publications._ 29
For the meta-analysis, we collect the b coefficients from empirical studies of interest rate passthrough. In doing so, we focus on studies that estimate the interest rate pass-through using equation 1 or its extended specification as in equations 2 and 4.

The vector _X_ appearing in equations 2-5 typically includes variables capturing the overall macroeconomic environment and bank-level characteristics. For instance, Holton and d’Acri (2015) include into vector _X_ macroeconomic variables such as the government bond yield, unemployment rate, and inflation rate. Van Leuvensteijn et al. (2013) and Leroy and Luccote (2015) control for bank competition using the aggregate Boone and the Lerner indices, respectively, both as a stand-alone control and in an interaction with the money market rate (see equation 4). Studies also control for market expectation of future interest rates, including the spread between the government bond yield and the money market rate (Cifarelli and Paladino, 2016; Gregor and Melecký, 2018), the forecast of the money market rate (Banerjee et al., 2013), and the Volatility Index (VIX), which reflects the expectation of the market’s volatility over the next 30 days (Grigoli and Mota, 2017).

As for bank-level characteristics, researchers mostly control for credit risk using the ratio of nonperforming loans (Gambacorta et al., 2015; Grigolli and Mota, 2017; Gregor and Melecký, 2018) or determinants of credit risk such as the business climate indicators (Mihaylov, 2016) or the price of credit default swaps (Gambacorta et al., 2015). Other typical bank characteristics include bank assets, bank capital, the leverage ratio, the loans to deposits ratio, and the liquidity ratio (Horváth and Podpiera, 2012; Holton and d’Acri, 2015; Havránek et al., 2016).

The empirical literature, however, does not typically control for broader country context, which is understandable for bank-level studies for a single country. For instance, overall financial depth, capital market development, and penetration of financial services among firms and households may influence the intensity of the interest rate pass-through (Visco, 2007; Khan, 2011; Saborowski and Weber, 2013; Mehrotra and Yetman, 2015; IMF, 2016). Moreover, the intensity of the interest rate pass-through may vary depending on the monetary policy framework and institutions, such as central bank independence, in a given country (Frenkel, 1999; Kharroubi and Zampolli, 2016). The approach of meta-regression 6
to a non-Yoruba state. In the phone survey, participants are asked whether they served in the state they were originally posted to or whether they redeployed. I crosscheck their responses with administrative data available on the state they were posted to in that cohort and the state in which they report to have served and I observe that 89 percent of those I observe to have redeployed admit to doing so. In the empirical analysis, I use the treatment status from the original state a student was assigned to as an instrument for the state in which they ultimately served.

Second, there are also reports of students using personal connections or other means to influence their original assignment. The survey asks students whether or not they influenced their posting to their desired state. Ten percent say they did so.<sup>17</sup> While there is no available administrative data to verify their responses, given the high level of accurate responses on the redeployment question, I take their responses as given. To account for these potential endogenous moves, I focus the analysis on those who do not report influencing their posting. I include results on the full sample in the appendix and find that the impacts are qualitatively similar, suggesting that national integration outcomes were not the primary driver of deviations from random assignment.<sup>18</sup> The crucial feature of the randomized allocation is that the characteristics of corps members should not determine their state postings. Analysis of the university administrative data reveals patterns that are consistent with the assignment rules. All the analyses exclude women who are married at the time of posting (3.3 percent of the cohort) as their posting is explicitly non-random. The rule of not posting students to their home state is followed in 99.7% of cases.

To examine whether individual characteristics are correlated with treatment status,

> 17Only 13 percent of those who say they influenced their posting served in a non-Yoruba state.

> 18Fifty percent of those who say they influenced their postings served in Lagos state, the economic capital of Nigeria, suggesting that access to economic opportunities may have been a key motivation.

15
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