rafmacalaba/datause-encoder-data huggingface dataset:
Designing an appropriate fiscal policy response to the crisis is constrained by uncertainty. It is uncertain how long the external shock will last. There is also uncertainty about the transmission of the shock to the domestic economy because we lack important data about the economy – for example, the national income accounts are very rudimentary and do not include GDP by expenditure – and we do not have a clear understanding of important relationships in the macro economy; for example the extent to which prices clear the market, and hence by how much output will fall in response to a fall in aggregate demand, and how dependent is production on imports. There is also uncertainty about the impact of fiscal policy on real variables; the size and sign of fiscal multipliers.

The observations in this paper were carried out in the context of advisory services provided to the Tajik authorities as they faced the consequences of global economic crisis over the last few months. The process of engagement with the Tajik authorities, providing them much needed practical and technical guidance at every stage of the way in addressing the crisis, was very much appreciated by all stakeholders. Countries facing crisis need to share experiences with other countries and this was also a hallmark of the support provided to the Tajiks. The strong local leadership, inter ministerial coordination and multi sectoral approach to crisis management is something not always highlighted but the Tajik approach had its challenges of ensuring sustained effort in this area. The close partnership with bilateral and multi lateral donors, with IMF and World Bank leadership, by the government is very important to ensure consistent advice during crisis period. Such consistency in approach should not be under emphasized especially at a time of crisis when conflicting information can lead to inaction and policy inconsistency.

Although the paper focuses on Tajikistan, it may have relevance for other low income non fuel exporting CIS economies, notably the Kyrgyz Republic and Moldova, and probably to a lesser extent, Armenia.<sup>2</sup> These four economies have some important

> 2 We do not include the CIS oil and gas exporters (e.g. Azerbaijan, Kazakhstan and Uzbekistan) in this paper because, although they are also suffering a large external shock, their macroeconomic and fiscal positions differ substantially from that of the CIS non oil and gas exporters. In particular, because of the

3
( _E_ 1  _E_ 0 )  ( _E_ 2  _E_ 1 ). As energy is necessary for living, a household tries to maintain at least some basic minimum level of energy consumption, which is the energy poverty line.<sup>8</sup> If this basic minimum energy consumption happens to be the energy consumption of the income poor, we are likely to find that energy poor are also the income poor or vice versa. That is, by looking at the energy consumption of the income poor, we find the extent of energy consumption needed which is a bare minimum. But this is not likely the case. For example, a household with higher level of income may have energy consumption which is even lower than the minimum energy needed to be energy non-poor. The reverse is likely to be true if a household who is income poor consumes a higher level of energy consumption to make it energy non-poor.

In an attempt to determine that basic minimum energy requirement, our approach investigates how a household’s demand for energy varies with the change in other major welfare indicators such as income. One way to observe that change is to examine the energy demand function. We will see shortly that a household’s energy demand is influenced by several factors – both at household level (level of education, land and non-land assets, hygiene) and at community level (energy price, village infrastructure, prevailing wage structure, and commodity prices). However, for households who are energy poor and are only meeting their basic needs for energy, the relationship between energy use and income is likely to be quite weak. These issues will be explored using a nationally representative household survey of rural energy use in Bangladesh that contains a rich set of data on energy consumption, income and other factors necessary for assessing an energy poverty line.

# **4. The pattern of energy use and its determinants in Bangladesh**

> 8 Basic minimum energy consists of energy needed for minimum quantity of lightning, cooking, and heating.

11
change for Canada may not be a surprise considering the biofuel targets of the country and its duties on biofuel imports. For Thailand, we noticed a lot of diversion of lands not only from forests but also from other crops, such as rice, towards biofuels.

**Table 1. Land deforested due to biofuel expansion in 2020**

|Country/Region|Million|hectares|% change fro|m the baseline|
|---|---|---|---|---|
||AT|ET|AT|ET|
|**World total**|**4.8**|**26.3**|**0.1**|**0.5**|
|**High-income**|**2.7**|**11.4**|**0.2**|**1.0**|
|Aus-NZ|0.0|0.2|0.1|0.4|
|Japan|0.0|0.3|0.1|1.0|
|Canada|1.3|6.6|0.2|1.2|
|United States|0.2|1.1|0.1|0.5|
|France|0.3|0.8|1.3|3.8|
|Germany|0.1|0.4|0.6|1.9|
|Italy|0.1|0.2|0.6|1.7|
|Spain|0.0|0.1|0.6|1.7|
|UK|0.1|0.3|1.2|3.3|
|Rest of EU & EFTA|0.5|1.5|0.3|0.9|
|**Middle & Low-income**|**2.2**|**14.9**|**0.1**|**0.4**|
|China|0.2|1.2|0.1|0.5|
|Indonesia|0.2|0.8|0.1|0.6|
|Malaysia|0.0|0.1|0.1|0.3|
|Thailand|0.4|1.9|1.1|5.2|
|Rest of EAP|0.1|0.8|0.0|0.3|
|India|0.7|1.8|0.7|2.0|
|Rest of SA|0.0|0.0|0.0|0.2|
|Argentina|0.0|0.1|0.1|0.6|
|Brazil|1.5|7.4|0.3|1.6|
|Rest of LAC|0.1|1.4|0.0|0.3|
|Russia|-1.2|-2.1|-0.1|-0.1|
|Rest of ECA|0.1|0.4|0.1|0.6|
|MENA|0.0|0.0|0.1|0.5|
|South Africa|0.0|0.2|0.1|6.8|
|Rest of SSA|0.1|0.7|0.0|0.1|

However, in some countries like India and Thailand, natural forests are taken to be successfully protected and it is unlikely that demand for biofuels would change the forest protection policy. This implies that if the production of biofuels causes any conversion of land, the new land should come from pasture, not from forest. To capture this, we have developed a scenario where all new lands required for production of biofuels under the AT and ET scenario 20
on GFS is limited, we instead use gross debt from World Economic Outlook (WEO). All government variables are as a ratio to GDP.

This paper focuses on the impact of tax on two measures of economic output. First, we calculate 10and 15-year ahead growth as the log change in real GDP per capita from the World Bank’s World Development Indicators (WDI). To measure inclusive growth, we use the recently created “prosperity gap” by Kraay _et al._ (2023) and again look at the 10- and 15-year ahead level of the prosperity gap. The prosperity gap is defined as the average factor by which incomes need to be multiplied to bring everyone in a country to an income of $25 per day.

# **5. Results**

# **5.1 Identifying the threshold**

about 12.5 percent (figure 3). This threshold is determined by excluding resource-rich economies,<sup>3</sup> which often exhibit lower tax revenues<sup>4</sup> but higher and volatile growth outcomes. For the optimal threshold, we conduct a grid search, commencing from 7 percent (the 5th percentile of the tax-toGDP ratio) to 30 percent, with intervals of 0.1. The results are in line with those presented in GJW. Interestingly, the threshold has not moved up despite the significant growth in spending needs since the previous empirical work was conducted.

**Figure 3: The growth maximizing tax to GDP ratio hovers around 12.5 percent**

_(10-year ahead) (15-year ahead)_ <!-- Start of picture text --> 90% Confidence intervals Coefficient 90% Confidence intervals Coefficient<br>60 60<br>40 40<br>20<br>20<br>0<br>0<br>-20<br>-20<br>-40<br>-40 -60<br>-60 -80<br>7 9 11 13 15 17 19 21 23 25 27 29 7 9 11 13 15 17 19 21 23 25 27 29<br>Tax to GDP Tax to GDP<br><!-- End of picture text --> Source: Authors’ calculations.

Note: The depicted figure provides an estimation of the coefficient on the tax-to-GDP threshold dummy in Equation 1. The sequence of the figures represents the impact on cumulative growth rates over different time horizons: the left figure pertains to a 10-year period, and the right figure to a 15-year period. Dashed red line indicates the optimal threshold value.

> 3 The resource rich economies are identified as those with natural resource rent to GDP ratio exceeding 19.4 percent.

> 4 Resource-rich countries often have lower tax collection due to significant revenues from natural resources, which are used to attract investment and foster economic growth.

8
3 trade that influences the trade effect of price-distorting policies. And third, the welfare effect of a policy such as an import tariff is related to the square of that tariff rate, unlike the trade effect which is related just to the rate itself.

These issues would not be a problem if there was a global model of each commodity market (or a global economy wide computable general equilibrium (CGE) model) calibrated for a particular year of interest, since the NRA and CTE estimates for that product could be inserted in such a model to generate partial (or general) equilibrium estimates of the global trade and welfare effects of those distortionary policies in that year. However, global models do not exist for many commodities, and global CGE models typically have to aggregate many of the smaller commodities into groups to keep the model tractable. Moreover, such models are calibrated to a particular year and so are incapable of providing a long time series of estimates of the global trade and welfare effects of distortionary policies affecting particular commodity markets.

The purpose of the present article is to draw on the recently developed literature on trade restrictiveness indexes to provide a more-satisfactory set of indicators of global trade and welfare effects of distortionary commodity policies. The article begins by drawing on the recent literature on the family of trade restrictiveness indexes (TRIs) to develop two new members of that family for individual global commodity markets. The previous literature focuses mostly on policy distortions to imports, but we focus also on policies that distort exports since the latter are still prevalent in a number of agricultural markets. The first of the new indexes refers to the volume of trade: it is the ad valorem
# Table 1

# Random Effects Estimates: Effects of Rainfall Measures on Profits

(1983 Rupees) from Crop Production and Total Farm Profits

# in Ten ICRISAT Villages

|Crop Pr|ofits|Total|Profits|
|---|---|---|---|
|Weather Variable<br>(1)|(2)|(1)|(2)|
|Monsoon onset date<br>-13.9<br>(2.68)a|-15.7<br> <br>(2.78)|-9.35<br> <br>(1.68)|-12.4<br> <br>(2.05)|
|End of monsoon<br>-|-1.29<br>(0.37)|-<br>|-.357<br>(0.10)|
|Fraction of days in season<br>-|1993||1722|
|with rain|(2.25)||(1.81)|
|Rain per day in season<br>-|-85.1<br>(1.44)|-<br>|-108<br>(1.70)|
|Consecutive<br>days of intraseasonal<br>-|-2.67|-|-4.95|
|drought- first episode|:0.35)||(0.61)|
|Consecutive days of intraseasonal<br>-|-2.59|-|-8.41|
|drought- second episode|(0.33)||(0.99)|
|Constant<br>7054|7619|7440|8219|
|(7.13)|(5.72)|<br>(6.89)|<br>(5.70)|
|F<br>82.0|25.2|87.7|26.9|
|x<br>(Breusch-Pagan)<br>7886|7809|8438|8394|
|<br>n<br>2168|2168|2168|2168|

a. Absolute values of asymptotic t-ratios in parentheses.

13
# **Table 2**

# **How Do Cultural Revolution Shocks Influence Ability to Attend High School and College? Marginal Effects from Probit Models**

|Model<br>DepVar|1<br>college ?|2<br>college ?|3<br>college ?|4<br>college ?|5<br>high?|6<br>high ?|7<br>high ?|
|---|---|---|---|---|---|---|---|
|College-Completion-by-25 Shock x Father's Education|-0.0354<br>|---|---|0.0164<br>|---|---|---|
||(0.0322)|---|---|(0.0423)|---|---|---|
|College-Completion-by-25 Shock x Father High|1.4400|---|---|1.2600|---|---|---|
|Administrator|(0.6000)|---|---|(0.8100)|---|---|---|
|High School Shock x Father's Education|---|-0.0262|---|-0.0237|-0.0237|---|-0.0157|
||---|(0.0090)|---|(0.0097)|(0.0122)|---|(0.0129)|
|High School Shock x Father High Administrator|---|0.2720|---|0.2150|-0.0030|---|-0.1730|
||---|(0.2020)|---|(0.1910)|(0.2975)|---|(0.3190)|
|Cohort Sent Down Youth Share x Father's Education|---|---|-0.0129|-0.0113|---|-0.0224|-0.0178|
||---|---|(0.0068)|(0.0086)|---|(0.0095)|(0.0103)|
|Cohort Sent Down Youth Share x Father High|---|---|0.1840|0.0150|---|0.2790|0.3210|
|Administrator|---|---|(0.1200)|(0.1574)|---|(0.2140)|(0.2240)|
|Age|0.020|0.019|0.024|0.022|-0.061|-0.062|-0.062|
||(0.029)|(0.029)|(0.030)|(0.029)|(0.045)|(0.046)|(0.046)|
|Age-Squared|0.000|0.000|0.000|0.000|0.001|0.001|0.001|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.001)|(0.001)|(0.001)|
|Female|-0.335|-0.317|-0.353|-0.344|0.414|0.406|0.405|
||(0.338)|(0.338)|(0.338)|(0.339)|(0.402)|(0.405)|(0.405)|
|Height|0.000|0.000|0.000|0.000|0.003|0.003|0.003|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|(0.002)|(0.002)|
|Height*Female|0.002|0.002|0.002|0.002|-0.003|-0.003|-0.003|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.003)|(0.003)|(0.003)|
|Father's Years of Schooling|0.009|0.009|0.010|0.010|0.015|0.019|0.019|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.003)|(0.003)|(0.003)|
|Mother's Years of Schooling|0.005|0.005|0.005|0.005|0.004|0.004|0.004|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.003)|(0.003)|(0.003)|
|Joint Significance of "Instruments"||||||||
|<br>Chi-Square Statistic|5.92|8.96|4.70|16.70|3.93|6.10|8.40|
|Chi-Probability|0.052|0.011|0.095|0.011|0.141|0.047|0.078|
|Obs|3611|3611|3599|3599|3611|3599|3599|

Notes: (1) College-Completion-by-25 Shock: The deviation of average college attainment by age 25 (conditional on high school completion) for birth cohort relative to pre and post-Cultural Revolution trend.

(2) High School Shock: The deviation of average high school attainment (conditional on middle school completion) for birth cohort relative to pre and post-Cultural Revolution trend.

(3) All models include number of siblings, dummy variable for missing information on siblings and mother and father's education, and vectors of school quality and location variables and parent occupation dummy variables.

(4) All models include statistically significant (city) x (three-year birth cohort) interactions.

(5) All models show robust standard errors cluster corrected at the three-year city cohort level.
Table 2: Average treatment effect within each subgroup defined by ranking

|(1)<br>(2)<br>**“Strong” group**<br>**“Weak” group**|(3)<br>**Diference**|
|---|---|
|_Panel A: Average subgroup ATE_||
|Sample averages<br>0.268<br>0.021|0.246|
|(0.059)<br>(0.061)|(0.084)|
|AIPW<br>0.255<br>0.029<br>(0.056)<br>(0.057)|0.226<br>(0.080)|
|_Panel B: Average subgroup ATE by counselling style_||
|SDM<br>-<br>-0.009<br>(0.091)|-|
|MiM<br>0.269<br>0.043|0.226|
|(0.073)<br>(0.117)|(0.138)|
|IDM<br>0.246<br>0.086|0.160|
|(0.081)<br>(0.090)|(0.121)|

Notes: “Strong” and “weak” refer to subgroups whose conditional average treatment effect (CATE) of switching from _High_ to _Discounted_ (i.e., _Low_ or _Mid_ ) prices is above (strong) or below (weak) the median CATE estimate in the data; Panel A shows estimates for the entire sample and Panel B shows estimates per counselling style. The ATE estimates in Panel B are sample averages.

Panel B of Table 2 further subdivides the sample depending on counseling assignment. Consistent with the analysis in the previous subsection, we find that clients receiving _SDM_ are all classified by the causal forest as weakly affected. Meanwhile, the “strong” group contains clients from both the _MiM_ and _IDM_ groups, and each of these subgroups show a positive and significant effect of discounts.

Table 3 characterizes clients in each subgroup. Clients who are strongly affected by discounted LARC prices are substantially younger than weakly-affected clients. Consequently, they are more likely to be students, to want to delay pregnancy by at least three years, and to want more children in the future. They also have higher levels of education and are more likely to present at the FP department to seek counseling. Figure B1 in the appendix presents LARC adoption rates under _High_ and _Discounted_ prices for each counseling regime by individual client characteristics. The patterns are consistent with Table 3.

13
**<u>Table C.10 Descriptive statistics of estimation sample, Mauritania</u>**

|||**2004-2008**|||**2008-2014**|||**2014-201**|**9**|
|---|---|---|---|---|---|---|---|---|---|
||**2004**|**2008**|**diff**|**2008**|**2014**|**diff**|**2014**|**2019**|**diff**|
|L f i i|11.66|12.09|0.4***|12.10|12.54|0.4***|12.56|12.68|0.1***|
|og o per capta consumpton|(0.01)|(0.01)|(0.0)|(0.01)|(0.01)|(0.0)|(0.01)|(0.01)|(0.0)|
|Hd`|42.41|43.90|1.5***|41.90|46.40|4.5***|42.42|45.49|3.1***|
|eas age|(0.10)|(0.08)|(0.1)|(0.08)|(0.10)|(0.1)|(0.10)|(0.11)|(0.1)|
|Hd i fl|0.16|0.29|0.1***|0.30|0.30|0.0|0.31|0.37|0.1***|
|ea s emae|(0.00)|(0.00)|(0.0)|(0.00)|(0.01)|(0.0)|(0.01)|(0.01)|(0.0)|
|Hd' hih di ll i i|0.10|0.11|0.0|0.12|0.12|-0.0|0.14|0.29|0.1***|
|eas gest eucaton eve s prmary|(0.00)|(0.00)|(0.0)|(0.00)|(0.00)|(0.0)|(0.00)|(0.01)|(0.0)|
|Hd' hiht dti ll i d|0.13|0.13|-0.0|0.13|0.13|0.0|0.15|0.19|0.0***|
|eas ges eucaon eve s seconary|(0.00)|(0.00)|(0.0)|(0.00)|(0.00)|(0.0)|(0.00)|(0.00)|(0.0)|
|Hd' hih di ll i i|0.05|0.05|0.0|0.05|0.05|0.0**|0.05|0.06|0.0|
|eas gest eucaton eve s tertary|(0.00)|(0.00)|(0.0)|(0.00)|(0.00)|(0.0)|(0.00)|(0.00)|(0.0)|
|Ub|0.51|0.48|-0.0***|0.47|0.59|0.1***|0.59|0.50|-0.1***|
|ran area|(0.01)|(0.01)|(0.0)|(0.01)|(0.01)|(0.0)|(0.01)|(0.01)|(0.0)|

Note: ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels. Standard errors are in parentheses. The data are unweighted. Household heads' ages are restricted to between 25 and 55 for the first survey round and adjusted accordingly for the second survey round. The reference groups are household with no primary education and living in rural areas.

88
**Table 3.** **<u>Full Set of Attributes and Levels</u>**

|**Attribute**|**Levels**|
|---|---|
|Written Contract|None<br>6 months initially<br>1 year initially<br>Long term|
|Termination Notice|None<br>15 days<br>30 days<br>60 days|
|Working hours|30-45 hours per week<br>45-60 hours per week<br>60-75 hours per week|
|Amount of paid leave<br>(excluding government holidays and festival<br>leave)|None<br>5 days<br>10 days<br>15 days|
|Provident Fund|Yes<br>No|
|Monthly income|Same as now<br>10% increase over current income<br>20% increase over current income<br>30% increase over current income<br>40% increase over current income<br>50% increase over current income|

_Source_ : Authors’ choice experiment used in survey.

_Note_ : Full set of attributes and levels from the choice experiment presented to respondents during the survey.

41
Using _θ_<sup>ˆ</sup><sup>_I_</sup> , we can predict _μ_ by _g_ ( _xch, θ_<sup>ˆ</sup><sup>_I_</sup> ) for all observations in _S_ . Therefore, the prediction estimator for _μ_ under Assumption 7 is given by: The following is a direct extension of Theorem 6 to the double-sample estimator:

**Theorem 10** _Suppose that Assumptions 1, 2, 7, 8, and 9 hold. Then, μ_ ˆ<sup>_DS_</sup> _satisfies the following properties as J →∞:_

Note that Theorem 6 is a special case of Theorem 10 when _r_ = 1 (i.e., _k_ = _K_ ).

In the binary context, we have the following results:

**Theorem 11** _Suppose that Assumptions 1, 2, 3, 7, and 8 hold. Further, θ_<sup>ˆ</sup><sup>_I_</sup> = ( _β_<sup>ˆ</sup><sup>_T_</sup> _,_ ˆ _ση_<sup>2</sup><sup>_,_ˆ</sup><sup>_σ_</sup> _e_<sup>2)</sup><sup>_Tis a_</sup> _maximum-likelihood estimator. Then, μ_ ˆ<sup>_DS_</sup> _satisfies the following as J →∞:_

_where_ Σ _φx and_ Σ _φB are defined as follows:_ _Further,_ Ω<sup>_−_</sup> _c_<sup>1</sup> _can be written as:_ _where Ik and_ **1** _k are the K × K-identity matrix and K-vector of ones, respectively._ Let us further develop the expression for the asymptotic variance by making some modest simplifying assumptions that will ease the exposition.

12
frequency of transit than on driving speeds.

# **8. Conclusions**

We propose a novel approach to measuring vehicular mobility within cities, and decomposing it into uncongested mobility and a congestion factor. We apply it using novel large scale data on counterfactual trips in 154 Indian cities collected from Google Maps. After showing that various sampling and estimation strategies yield similar estimates of mobility, we document a number of important facts about mobility in Indian cities. Among the most important, we first highlight large mobility differences across cities. Second, slow mobility is primarily due to cities being slow all the time rather than congested at peak hours. We do nonetheless find an important role for congestion in the largest cities, especially close to their centers. Third, several city attributes are consistently correlated with mobility and its components. We find that population and land area are key correlates of city mobility. A larger population leads to slower uncongested mobility as well as more congestion. We also find that both recent population growth and a measure of cars per capita are positively associated with uncongested mobility but also with congestion. More primary roads and a more regular grid-pattern are associated with moderately faster mobility. Higher income cities have higher uncongested mobility, but also higher congestion, leading to a hill-shaped relationship between income and overall mobility. Overall, these indicators of urban economic are associated with better worse development mobility despite congestion, contrary to a conventional wisdom that urban growth and development condemns developing cities to complete gridlock. While in principle, variation in uncongested mobility could be due to many city attributes beyond those we consider here in our regressions, such as the state of the vehicle stock or driving culture, we interpret it as being primarily due to the quality of the road network. Most old cars can be driven 45 kilometers per hour (the 99th percentile of our trip speed distribution), and Google Maps’ algorithm is likely to pick out a high moment of the block speed distribution in order to distinguish motorized from non-motorized vehicles.

We that this set of evidence on urban and in a hope first cross-city mobility congestion developing country can help guide policy and future research. We now review three of our findings that have research and policy implications. First, we document that congestion in India is not a nationwide problem, but rather is highly concentrated near the center of the largest Indian cities. Given their importance to the Indian economy, these areas with the 36
# **4 Findings and discussion**

We find that a number of countries are highly financially vulnerable. As shown in Figure 8, the majority of the disaster prone countries in our sample experience a disaster financing problem, a resource gap, below a 50 year event.

<!-- Start of picture text --> Count rie s Experienc ing Init ial Re sourc e Gap Bet w ee n<br>S pec ified Ret urn Periods<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>50 Y e ars or 51 t o 100 101 t o 250 No Gap W it hin<br>Le ss Y ea rs Y ea rs Periods<br>Ret urn Periods<br>Count rie<br><!-- End of picture text --> Fig. 8: Number of countries experiencing a resource gap for some return periods Figure 9 shows how risk as measured in expected losses translates into an estimate of financial vulnerability based on assessments of financial and economic resilience. No clear relationship is discernible between the losses as percent of GDP and the resource gap (in terms of a probabilistic return period) and the resource gap for a 100 year event, which shows that the translation is a more complex one and GDP as a mere indicator of vulnerability may not suffice.

<!-- Start of picture text --> 500 250%<br>400 200%<br>300 150%<br>200 100%<br>100 50%<br>- 0%<br>0% 2% 4% 6% 8% 10% 0% 2% 4% 6%<br>Expected losses as % GDP Expected losses as % GDP<br>Finance gap year<br>100 year finance gap<br><!-- End of picture text --> Fig. 9: Risk vs. financial vulnerability 23