rafmacalaba/datause-encoder-data huggingface dataset:
# **4. Oil redistribution schemes: A way of increasing citizens’ scrutiny on public spending?**

While there have been many oil redistribution schemes proposed, they have not been combined with a fiscal contract. At best, policy makers and researchers advocate for redistributing directly revenues from oil extraction to citizens (proposals from Sala-i-Martin and Subramanian (2003) for Nigeria and Birdsall and Subramanian. (2004) for Iraq), and letting them spend or save it. This is probably related to the fact that in developing countries, tax evasion is likely to be high (Newbery et al. 1987, Bird et al. 2008) and direct taxation is relatively small (OECD 2008). Therefore, higher direct taxation must be put in place _after_ a higher redistribution share. But the main strength of oil economies lies in the fact that they benefit from sufficient revenues that they can share a part to citizens.

In the real world, the Alaska Permanent Fund is one of the few examples of oil redistribution schemes (Anderson 2002). Despite strong government effectiveness and good governance, the current redistribution scheme is increasingly coming under criticism. It appears as if there is a growing apathy from the population on public spending scrutiny and gradually, investment in public goods is neglected. Some voices in Alaska are calling for the introduction of new taxes on individuals in order to create a fiscal contract. Some observers of the situation in Alaska seem to believe that without a taxation relationship, the efficiency of public spending deteriorates overtime.

In order to explicitly take account of this relationship, we present some characteristics of another option, which could be called: “citizen funds+”: a share of oil revenues would be redistributed annually to any eligible citizen of the 14
**Figure 2. Correlation between local residuals and distance between localities**

<!-- Start of picture text -->
1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>0-25 Km 20-25 Km 25-40 Km 40-65 Km 65 + Km<br>Linear model Quadratic model<br><!-- End of picture text -->

- _Note:_ The legend indicates the specification chosen for the neighborhood effect. Mean values are computed at the VDC level.

# **8. Microsimulation results**

The microsimulations performed are based on an iterative process, using the preferred estimation of the hedonic price function at the household level and measures of actual destruction at the local level. The process involves first evaluating the physical loss of assets each household might have experienced due to the disaster. Then, the monetary damage from this loss is evaluated as if asset prices had not changed, which results in new market values for all dwellings in each locality. A mean of these new market values allows assessing the change in neighborhood effects due to the disaster. Plugging this change in the hedonic price function leads to a second-round impact of the disaster, hence to new market values for dwellings and new neighborhood effects. The computation is repeated until changes become marginal.

More specifically, the first step in this process is to adjust the market value of each dwelling in a way that reflects the physical destruction it might have experienced due to the disaster. In terms of equation (6), the damage term 0h 1h 0 is assessed by allocating the estimated local destruction rates 0 1 ⁄ 0 across the households covered by the 2011 NLSS III. Four ii �KK −KK �pp alternative allocation rules are used to this effect: ii ii ii �KK −KK �KK

- _(i) Proportional._ The market value of all dwellings in a locality is reduced by a fraction equal to the proportion of houses destroyed in the locality.

- _(ii) Random._ Households in the locality are drawn randomly from the sample and the market value of their dwellings is set equal to zero. Drawings continue until the cumulative damage matches the proportion of houses destroyed in the locality.

- _(iii) Cheapest_ . Dwellings with the lowest market value are supposed to be destroyed first. Their market value is set equal to zero, continuing until the cumulative damage equals the proportion of houses destroyed in the locality.

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28 FERNANDES-KLENOW-MELESHCHUK-PIEROLA-RODR ́ıGUEZ-CLARE

# **Simple Melitz model with lognormal distribution**

Online Appendix Table I9 reports the QQ-estimate of _σ_ ̄ _φ_ . We report three sets of estimates: for the full sample, the largest 50% of firms and the largest 25% of firms for each origin-destination pair in each year. These estimates are on the high side relative to the estimate obtained by HMT, so we will use the minimum among them, ̄ _σφ_ = 4 _._ 02, which corresponds to the subsample with the largest 25% of firms.<sup>28</sup> We highlight three findings. First, a lognormal distribution allows the intensive margin elasticity to be positive even under the assumption of a continuum of firms. Second, for our estimate of the shape parameter ( _σ_ ̄ _φ_ = 4 _._ 02), the implied IME is 0.28, which is close to that from the data.<sup>29</sup> Third, most of the action comes from the right tail of the exporter size distribution, as seen in Figure 10.

We use equations (29) and (30) to compute the model-implied fixed and variable trade costs. The correlations between those costs and distance are reported in Table 11 and plotted in Figure 11. In contrast to our results under Pareto, now under lognormal both the model-implied variable and fixed trade costs are increasing with distance.

Overall, the model does much better in fitting the data when we assume that firm productivity is distributed lognormal than when we assume that it is distributed Pareto. However, the IME for each percentile is not a perfect match to the data, and there is still a negative correlation between the model implied variable and fixed trade costs, although it is much closer to zero than with Pareto (-0.3 rather than -0.8). In any case, this is just a ”proof of concept” that lognormallydistributed productivity can by itself improve the performance of the model relative to the data. In the next subsection we present the results obtained with the estimated full Melitz-lognormal model.

# **Full Melitz-lognormal model**

To estimate the parameters of the full Melitz-lognormal model we use firm-level data from the EDD and China for the year 2007.<sup>30</sup> This entails 39 different origins, but we had to drop 2 origins from our analysis due to convergence issues discussed in the Online Appendix F. As mentioned above, we consider 15 destinations: USA, Germany, France, Japan, and the 11 largest destinations by export value for each origin. Finally, we use the estimates of Bas et al. (2015) and set _σ_ = 5.

Before presenting the results of the estimation and discussing their implications for the IME,

> 28  ̄ See the section in the Appendix titled QQ-Estimation of _σφ_ for a discussion of these estimates and their relation to the estimate in HMT.

> 29Using Head et al. (2014) estimate of  ̄ _σφ_ = 2 _._ 4 we get IME of around 0.12.

> 30For computational reasons, for China we considered only a random sample consisting of 5% of exporters.
# **4. Results**

## **4.1 Consumption distribution with FAFH**

To motivate and understand the impact that FAFH has on the selected welfare indexes, it is useful to first analyze the change in the entire consumption distribution. Changes in the lower tail will be reflected in the poverty indexes while changes in the shape of the entire distribution will be reflected in our inequality index.

Figure 2 - Panel A presents the empirical distributions of the log of per capita expenditure whether FAFH is excluded or not. As mentioned before, the distribution shifts to the right once consumption of FAFH is accounted for. To see in more detail how the consumption of FAFH is distributed, Panel B presents graphs: the absolute amount spent on FAFH and the share of expenditures on FAFH over total expenditures, both by percentile of the expenditure distribution when no FAFH is included. The message that emerges from those graphs is clear. On one hand, the absolute amount spent on FAFH increases along the expenditure distribution. As expected, individuals in households with higher total per capita expenditures also spend more on food away from home. However, the expenditure on FAFH as a share of total expenditure follows the opposite pattern. In relative terms, expenditure on FAFH is more important at the lower end of the distribution.

## **4.2 Changes in welfare indicators**

## 4.2.1 Poverty

We now evaluate the impact that inclusion of FAFH has on poverty estimates. Our baseline scenario is a world where consumption and poverty lines are constructed assuming that no information on FAFH exists. These extreme and moderate poverty rate estimates constitute the benchmark against which the impact of FAFH is going to be assessed. We note them as “at home” (Table 1).

Next, we proceed to include in the estimate of food consumption and poverty lines, one at a time, three components of FAFH that are collected in the ENAHO, based on ‘who’ within the household consumes the food. We look at: food consumed away by adults (15 and older), food consumed away by children (14 and younger), and food consumed at-home by the household (takeout). As pointed out in the previous section, food consumed away from home by adult household 17
- V. Experienced P4P creates effort incentives which contribute to higher (or lower) teacher performance, as measured by the learning outcomes of their students;

- VI. These selection and incentive effects are apparent in the composite performance metric.

The model predicts that the set of ( _τ, θ_ ) types preferring a teaching job advertised under P4P to a job in the outside sector is different from the set of types preferring a teaching job advertised under FW to a job in the outside sector. This gives Hypothesis I. Since the model abstracts from labor demand effects (by assuming employers hire at random from the set of ( _τ, θ_ ) types that apply), this prediction simply maps through to placed recruits; i.e. to Hypothesis II via _θ_ , Hypothesis III via _τ_ , and Hypotheses IV to VI via the effect of _θ_ and _τ_ on performance.<sup>14</sup> The model also predicts that any given ( _τ, θ_ ) type who applies to, and is placed in, a teaching job will exert more effort under experienced P4P than experienced FW. This gives Hypotheses V and VI via the effect of _e_ on performance.

**Analysis of blinded data** Combining several previously-known insights, we used blinded data to maximize statistical power for our main hypothesis tests.

The first insights, pertaining to simulation, are due to Humphreys et al. (2013) and Olken (2015). Researchers can use actual outcome data with the treatment variable scrambled or removed to estimate specifications in ‘mock’ data. This permits navigation of an otherwise intractable ‘analysis tree’. They can also improve statistical power by simulating treatment effects and choosing the specification that minimizes the standard error. Without true treatment assignments, the influence of any decision over eventual treatment effect estimates is unknown; thus, these benefits are garnered without risk of _p_ -hacking.<sup>15</sup>

> 14When mapping the theory to our empirical context, we distinguish between these hypotheses for two reasons: we have better data for placed recruits because we were able to administer detailed survey instruments to this well-defined sub-sample; and for placed recruits we can identify the advertised treatment effect from student learning outcomes, avoiding the use of proxies for ( _τ, θ_ ). A further consideration is that the impact of advertised treatment might differ between placed recruits and applicants due to labour-demand effects. We discuss this important issue in Section 5.

> 15This would not be true if, for example, an outcome in question was known to have different support as a function of treatment, allowing the ‘blinded’ researcher to infer treatment from the outcome variable. For our blinded pre-analysis, we only consider outcomes (TTC score, and student test scores) that are nearly continuously distributed and which we believe are likely to have the same support in all study arms. To make this analysis possible, we drew inspiration from Fafchamps and Labonne (2017), who suggest dividing labor within a research team. In our case, IPA oversaw

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dependent variables in the empirical analysis.

# **5.2 Nonparametric Analysis**

Sex difference in the adequacy ratio of different nutrients at each age are nonparametrically (using locally weighted regression method, lowess with bandwidth 0.8) shown in Figures 1 and 2. These figures illustrate a number of points. First, sex disparity in adequacy ratio is least observed for calorie and among the micronutrients, for niacin. As discussed in the appendix, calorie requirement figures for children and adolescents are based on US National Child Health Survey (NCHS) standard and not based on actual energy expenditure because of lack of data on time allocation for these age groups. Only for these groups, the adequacy ratio seems to be less than 1. This might imply that perhaps these groups are having calories based on their actual energy expenditure (which is unobserved in the data), but still they are having calories less than NCHS standards to meet their full long-term growth potentials. On the other hand, for the adults for which the requirements are based on actual energy expenditure, the adequacy ratio for both males and females are at or above 1. Calorie intakes of all ages are also most stable (compared to other nutrients) across different rounds (as requirement figures are fixed, any movement in adequacy ratio across rounds implies fluctuation in intakes). All these are consistent with the view in nutrition literature that given the wide variety of sources to meet one’s calorie need, in normal circumstances (i.e., without famine) an individual can always meet his/her calorie need (World Health Organization, 1985).

Second, for most of the other nutrients, sex disparity is prominent, persistent, and often widens for adolescents and adults compared with children. For vitamin A and D in some rounds, at a relatively high age (above 70 or so), females’ adequacy ratio cross over males’. However, this cross-over should be interpreted with caution due to the limited number of observations at those ages (with number of males higher than females). Third, with the exception of protein and vitamin C, nutrient deficiency is prominent across ages for all other nutrients with adequacy ratio lower than 1. The situation is most alarming for iron, where both males and females across ages are in deficiency, with females’ deficiency worse than males’. While protein adequacy ratio is above 1 across all ages (with males’ ratio higher than females’), the good quality protein (i.e., protein from animal, dairy, and fish, jointly termed as animal protein) as a share of required protein is very low. The situation is even worse for haem iron. Finally, (vertical) shift of these age-adequacy profiles for most of the nutrients (and changes in shapes for some nutrients) across rounds imply variation of individual intakes of these nutrients across rounds.

To illustrate the role of bargaining in intrahousehold nutrient allocation, lowess graphs of 20
to select beneficiaries. We know from MAFC administrative data that all agro-dealers claimed to the partner bank NMB that _all_ of the vouchers distributed in 2010/11 had been redeemed, and so there is a discrepancy between the number of inputs that beneficiaries purchased with vouchers, and the number of vouchers claimed by agro-dealers for payment (see Section A.2 of the Online Appendix for details of the process that agro-dealers followed to redeem the vouchers collected from beneficiaries). This suggests that agro-dealers either forced farmers to sign and turn over all three vouchers while only selling two of the three inputs on average, or agro-dealers allowed the face value of one voucher to be used as top-up for the purchase of the two inputs. For example, for the purchase of urea, the farmer would hand out the urea and DAP voucher and use all or part of the face value of the DAP voucher towards payment of the urea top-up.

We also checked whether the quantity of inputs purchased with vouchers corresponds to the package designed by MAFC and find that it is the case. That is, for each input purchased, farmers bought the amount stipulated by the program (enough for 1 acre of land), but they only purchased two of the three inputs, on average, even though agro-dealers claimed that farmers had redeemed all three vouchers.

Column 4 of Table 4 reports satisfaction with the selection process of beneficiaries. The dependent variable takes value 1 if the respondent reported being very satisfied with the selection of voucher beneficiaries in 2010/11. We find that, unsurprisingly, beneficiaries are 16 percentage points more likely to report being satisfied with the selection process compared to non-beneficiaries. Interestingly, nonbeneficiaries in lottery villages are 11 percentage points more likely to report being satisfied with the selection process than non-beneficiaries in non-lottery villages (an increase of 18 percent from a base of 62 percent). In fact, in lottery villages there is no difference in satisfaction between beneficiaries and non-beneficiaries (p-value is 0.396). Arguably, respondents in lottery villages may think that a lottery is a fairer method of choosing beneficiaries compared to selection by the VVC.

# _4.2.2 Displacement of sales_

While column 4 of Table 4 shows higher satisfaction among farmers when a lottery is used to select beneficiaries, the lottery could randomly select as beneficiaries less productive farmers, and as a result, one would expect more transfers of inputs from (less productive) beneficiaries to (more productive) nonbeneficiaries in a secondary market for inputs.

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||**Table 11**<br>|**Proximity to m**<br>|**ining and quali**<br>|**ty of public heal**<br>|**th services**<br>|||
|---|---|---|---|---|---|---|---|
||**(1)**<br>**Expensive**|**(2)**<br>**Pills**|**(3)**<br>**Care**|**(4)**<br>**Absenteeism **|**(5)**<br>**Long line**|**(6)**<br>**Clean **|**(7)**<br>**Corruption **|
|Cum. gold within 20km|-0.003|-0.007*|-0.001|-0.009|0.001|0.001|0.245**|
||(0.002)|(0.004)|(0.001)|(0.006)|(0.001)|(0.001)|(0.107)|
|Education|-0.109***|-0.007|0.072|0.126***|0.055|0.076|-0.214**|
||(0.040)|(0.044)|(0.048)|(0.048)|(0.044)|(0.047)|(0.102)|
|Age|-0.006**|-0.005|-0.007*|-0.007*|-0.007*|-0.003|-0.015**|
||(0.003)|(0.003)|(0.004)|(0.004)|(0.003)|(0.004)|(0.007)|
|Male|0.075|0.073|0.186*|0.249***|0.140*|0.221**|0.207|
||(0.079)|(0.087)|(0.099)|(0.093)|(0.078)|(0.087)|(0.233)|
|Poverty|-0.161***|-0.107***|-0.157***|-0.083*|-0.094**|-0.188***|-0.084|
||(0.039)|(0.038)|(0.045)|(0.043)|(0.038)|(0.040)|(0.085)|
|Rural|-0.262*|-0.165|-0.699***|-0.131|-0.272*|-0.480***|-0.671*|
||(0.135)|(0.166)|(0.166)|(0.166)|(0.152)|(0.172)|(0.360)|
|Observations|2,199|2,199|2,201|2,199|2,197|2,191|559|

Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.

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strategy we multiplied the number of days missed by each household member by total household expenditure level divided by the number of workers. For this strategy we assume that each working household member brings to the household the same amount of money due to their work. As this is quite unrealistic, we turn to a second strategy consisting in multiplying the number of missed work days of a household member by the average daily wage of his/her occupation based on occupation information in Table 21. It should be noted that results derived from the first or second strategy are very similar. We choose to focus on those obtained through the second strategy only because of more realistic assumptions.

_Table 21: Estimated annual wage for different occupations (in Cedis)_

|Occupations|Estimated wage(annual)|
|---|---|
|Managers|19276|
|Professionals|11632|
|T&A professionals|10648|
|Clerical support workers|8939|
|Service/sales workers|9799|
|Skilled agricultural workers|7618|
|Craft and trade|9876|
|Manufacturing|7222|
|Elementary Occupation|7274|
|Retiree|7715|

The largest losses occur through housing repair costs and assets, and medical costs and missed work days only play a minor role. Total loss can be decomposed in four different sources (asset loss, repair costs, income loss due to missed days of work, and medical costs) which provides additional information on how households were impacted by the flood. Asset losses and housing repairs make up a large share of total losses (49% each). For the households affected in our sample, labor and medical related costs were marginal in comparison to asset losses and housing repairs.

Among the 15% of affected households that missed days of work due to the flood, service workers stand out as the most affected occupation group. Service workers missed on average 10 days of work, which is significant. Other categories report longer interruption durations, but with sample sizes that are too small to draw any conclusion. Missed days of work account for a small portion of total losses.

_Table 22 - Missed days of work caused by being affected by flood by occupation_

|Occupation|Average number of missed days|Standard deviation|Sample Size|
|---|---|---|---|
|Professionals|4|(1.780)|4|
|T&A Professionals|3|(0.307)|6|
|Clerical support workers|1|(0)|1|
|Sales/service workers|10|(2.453)|46|
|Skilled Agricultural workers|13|(4)|2|
|Craft & Trade|14|(9.313)|4|
|Manufacturing|14|(0)|1|
|ElementaryOccupation|5|(1.936)|4|

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